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Teixeira H, Dias C, Silva AV, Ventura J. Advances on MXene-Based Memristors for Neuromorphic Computing: A Review on Synthesis, Mechanisms, and Future Directions. ACS NANO 2024; 18:21685-21713. [PMID: 39110686 PMCID: PMC11342387 DOI: 10.1021/acsnano.4c03264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 07/22/2024] [Accepted: 07/25/2024] [Indexed: 08/21/2024]
Abstract
Neuromorphic computing seeks to replicate the capabilities of parallel processing, progressive learning, and inference while retaining low power consumption by drawing inspiration from the human brain. By further overcoming the constraints imposed by the traditional von Neumann architecture, this innovative approach has the potential to revolutionize modern computing systems. Memristors have emerged as a solution to implement neuromorphic computing in hardware, with research based on developing functional materials for resistive switching performance enhancement. Recently, two-dimensional MXenes, a family of transition metal carbides, nitrides, and carbonitrides, have begun to be integrated into these devices to achieve synaptic emulation. MXene-based memristors have already demonstrated diverse neuromorphic characteristics while enhancing the stability and reducing power consumption. The possibility of changing the physicochemical properties through modifications of the surface terminations, bandgap, interlayer spacing, and oxidation for each existing MXene makes them very promising. Here, recent advancements in MXene synthesis, device fabrication, and characterization of MXene-based neuromorphic artificial synapses are discussed. Then, we focus on understanding the resistive switching mechanisms and how they connect with theoretical and experimental data, along with the innovations made during the fabrication process. Additionally, we provide an in-depth review of the neuromorphic performance, making a connection with the resistive switching mechanism, along with a compendium of each relevant performance factor for nonvolatile and volatile applications. Finally, we state the remaining challenges in MXene-based devices for artificial synapses and the next steps that could be taken for future development.
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Affiliation(s)
- Henrique Teixeira
- IFIMUP, Departamento de Física
e Astronomia, Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre s/n, 4169-007, Porto, Portugal
| | - Catarina Dias
- IFIMUP, Departamento de Física
e Astronomia, Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre s/n, 4169-007, Porto, Portugal
| | - Andreia Vieira Silva
- IFIMUP, Departamento de Física
e Astronomia, Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre s/n, 4169-007, Porto, Portugal
| | - João Ventura
- IFIMUP, Departamento de Física
e Astronomia, Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre s/n, 4169-007, Porto, Portugal
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2
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Li J, Serafin EK, Koorndyk N, Baccei ML. Astrocyte D1/D5 Dopamine Receptors Govern Non-Hebbian Long-Term Potentiation at Sensory Synapses onto Lamina I Spinoparabrachial Neurons. J Neurosci 2024; 44:e0170242024. [PMID: 38955487 PMCID: PMC11308343 DOI: 10.1523/jneurosci.0170-24.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 06/20/2024] [Accepted: 06/27/2024] [Indexed: 07/04/2024] Open
Abstract
Recent work demonstrated that activation of spinal D1 and D5 dopamine receptors (D1/D5Rs) facilitates non-Hebbian long-term potentiation (LTP) at primary afferent synapses onto spinal projection neurons. However, the cellular localization of the D1/D5Rs driving non-Hebbian LTP in spinal nociceptive circuits remains unknown, and it is also unclear whether D1/D5R signaling must occur concurrently with sensory input in order to promote non-Hebbian LTP at these synapses. Here we investigate these issues using cell-type-selective knockdown of D1Rs or D5Rs from lamina I spinoparabrachial neurons, dorsal root ganglion (DRG) neurons, or astrocytes in adult mice of either sex using Cre recombinase-based genetic strategies. The LTP evoked by low-frequency stimulation of primary afferents in the presence of the selective D1/D5R agonist SKF82958 persisted following the knockdown of D1R or D5R in spinoparabrachial neurons, suggesting that postsynaptic D1/D5R signaling was dispensable for non-Hebbian plasticity at sensory synapses onto these key output neurons of the superficial dorsal horn (SDH). Similarly, the knockdown of D1Rs or D5Rs in DRG neurons failed to influence SKF82958-enabled LTP in lamina I projection neurons. In contrast, SKF82958-induced LTP was suppressed by the knockdown of D1R or D5R in spinal astrocytes. Furthermore, the data indicate that the activation of D1R/D5Rs in spinal astrocytes can either retroactively or proactively drive non-Hebbian LTP in spinoparabrachial neurons. Collectively, these results suggest that dopaminergic signaling in astrocytes can strongly promote activity-dependent LTP in the SDH, which is predicted to significantly enhance the amplification of ascending nociceptive transmission from the spinal cord to the brain.
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Affiliation(s)
- Jie Li
- Department of Anesthesiology, Pain Research Center, University of Cincinnati College of Medicine, Cincinnati, Ohio 45267
| | - Elizabeth K Serafin
- Department of Anesthesiology, Pain Research Center, University of Cincinnati College of Medicine, Cincinnati, Ohio 45267
| | - Nathan Koorndyk
- Neuroscience Graduate Program, University of Cincinnati College of Medicine, Cincinnati, Ohio 45267
| | - Mark L Baccei
- Department of Anesthesiology, Pain Research Center, University of Cincinnati College of Medicine, Cincinnati, Ohio 45267
- Neuroscience Graduate Program, University of Cincinnati College of Medicine, Cincinnati, Ohio 45267
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3
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Tye KM, Miller EK, Taschbach FH, Benna MK, Rigotti M, Fusi S. Mixed selectivity: Cellular computations for complexity. Neuron 2024; 112:2289-2303. [PMID: 38729151 PMCID: PMC11257803 DOI: 10.1016/j.neuron.2024.04.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 03/08/2024] [Accepted: 04/12/2024] [Indexed: 05/12/2024]
Abstract
The property of mixed selectivity has been discussed at a computational level and offers a strategy to maximize computational power by adding versatility to the functional role of each neuron. Here, we offer a biologically grounded implementational-level mechanistic explanation for mixed selectivity in neural circuits. We define pure, linear, and nonlinear mixed selectivity and discuss how these response properties can be obtained in simple neural circuits. Neurons that respond to multiple, statistically independent variables display mixed selectivity. If their activity can be expressed as a weighted sum, then they exhibit linear mixed selectivity; otherwise, they exhibit nonlinear mixed selectivity. Neural representations based on diverse nonlinear mixed selectivity are high dimensional; hence, they confer enormous flexibility to a simple downstream readout neural circuit. However, a simple neural circuit cannot possibly encode all possible mixtures of variables simultaneously, as this would require a combinatorially large number of mixed selectivity neurons. Gating mechanisms like oscillations and neuromodulation can solve this problem by dynamically selecting which variables are mixed and transmitted to the readout.
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Affiliation(s)
- Kay M Tye
- Salk Institute for Biological Studies, La Jolla, CA, USA; Howard Hughes Medical Institute, La Jolla, CA; Department of Neurobiology, School of Biological Sciences, University of California, San Diego, La Jolla, CA 92093, USA; Kavli Institute for Brain and Mind, San Diego, CA, USA.
| | - Earl K Miller
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | - Felix H Taschbach
- Salk Institute for Biological Studies, La Jolla, CA, USA; Biological Science Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA; Department of Neurobiology, School of Biological Sciences, University of California, San Diego, La Jolla, CA 92093, USA.
| | - Marcus K Benna
- Department of Neurobiology, School of Biological Sciences, University of California, San Diego, La Jolla, CA 92093, USA.
| | | | - Stefano Fusi
- Center for Theoretical Neuroscience, Columbia University, New York, NY, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA; Department of Neuroscience, Columbia University, New York, NY, USA; Kavli Institute for Brain Science, Columbia University, New York, NY, USA.
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4
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Fernández JG, Keemink S, van Gerven M. Gradient-free training of recurrent neural networks using random perturbations. Front Neurosci 2024; 18:1439155. [PMID: 39050673 PMCID: PMC11267880 DOI: 10.3389/fnins.2024.1439155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Accepted: 06/25/2024] [Indexed: 07/27/2024] Open
Abstract
Recurrent neural networks (RNNs) hold immense potential for computations due to their Turing completeness and sequential processing capabilities, yet existing methods for their training encounter efficiency challenges. Backpropagation through time (BPTT), the prevailing method, extends the backpropagation (BP) algorithm by unrolling the RNN over time. However, this approach suffers from significant drawbacks, including the need to interleave forward and backward phases and store exact gradient information. Furthermore, BPTT has been shown to struggle to propagate gradient information for long sequences, leading to vanishing gradients. An alternative strategy to using gradient-based methods like BPTT involves stochastically approximating gradients through perturbation-based methods. This learning approach is exceptionally simple, necessitating only forward passes in the network and a global reinforcement signal as feedback. Despite its simplicity, the random nature of its updates typically leads to inefficient optimization, limiting its effectiveness in training neural networks. In this study, we present a new approach to perturbation-based learning in RNNs whose performance is competitive with BPTT, while maintaining the inherent advantages over gradient-based learning. To this end, we extend the recently introduced activity-based node perturbation (ANP) method to operate in the time domain, leading to more efficient learning and generalization. We subsequently conduct a range of experiments to validate our approach. Our results show similar performance, convergence time and scalability when compared to BPTT, strongly outperforming standard node perturbation and weight perturbation methods. These findings suggest that perturbation-based learning methods offer a versatile alternative to gradient-based methods for training RNNs which can be ideally suited for neuromorphic computing applications.
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Affiliation(s)
- Jesús García Fernández
- Department of Machine Learning and Neural Computing, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
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Scharfen HE, Memmert D. The model of the brain as a complex system: Interactions of physical, neural and mental states with neurocognitive functions. Conscious Cogn 2024; 122:103700. [PMID: 38749233 DOI: 10.1016/j.concog.2024.103700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 05/07/2024] [Accepted: 05/07/2024] [Indexed: 06/16/2024]
Abstract
The isolated approaching of physical, neural and mental states and the binary classification into stable traits and fluctuating states previously lead to a limited understanding concerning underlying processes and possibilities to explain, measure and regulate neural and mental performance along with the interaction of mental states and neurocognitive traits. In this article these states are integrated by i) differentiating the model of the brain as a complex, self-organizing system, ii) showing possibilities to measure this model, iii) offering a classification of mental states and iv) presenting a holistic operationalization of state regulations and trait trainings to enhance neural and mental high-performance on a macro- and micro scale. This model integrates current findings from the theory of constructed emotions, the theory of thousand brains and complex systems theory and yields several testable hypotheses to provide an integrated reference frame for future research and applied target points to regulate and enhance performance.
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6
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Amjad U, Choi J, Gibson DJ, Murray R, Graybiel AM, Schwerdt HN. Synchronous Measurements of Extracellular Action Potentials and Neurochemical Activity with Carbon Fiber Electrodes in Nonhuman Primates. eNeuro 2024; 11:ENEURO.0001-24.2024. [PMID: 38918051 PMCID: PMC11232371 DOI: 10.1523/eneuro.0001-24.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 06/03/2024] [Accepted: 06/05/2024] [Indexed: 06/27/2024] Open
Abstract
Measuring the dynamic relationship between neuromodulators, such as dopamine, and neuronal action potentials is imperative to understand how these fundamental modes of neural signaling interact to mediate behavior. We developed methods to measure concurrently dopamine and extracellular action potentials (i.e., spikes) in monkeys. Standard fast-scan cyclic voltammetric (FSCV) electrochemical (EChem) and electrophysiological (EPhys) recording systems are combined and used to collect spike and dopamine signals, respectively, from an array of carbon fiber (CF) sensors implanted in the monkey striatum. FSCV requires the application of small voltages at the implanted sensors to measure redox currents generated from target molecules, such as dopamine. These applied voltages create artifacts at neighboring EPhys measurement sensors which may lead to misclassification of these signals as physiological spikes. Therefore, simple automated temporal interpolation algorithms were designed to remove these artifacts and enable accurate spike extraction. We validated these methods using simulated artifacts and demonstrated an average spike recovery rate of 84.5%. We identified and discriminated cell type-specific units in the monkey striatum that were shown to correlate to specific behavioral task parameters related to reward size and eye movement direction. Synchronously recorded spike and dopamine signals displayed contrasting relations to the task variables, suggesting a complex relationship between these two modes of neural signaling. Future application of our methods will help advance our understanding of the interactions between neuromodulator signaling and neuronal activity, to elucidate more detailed mechanisms of neural circuitry and plasticity mediating behaviors in health and in disease.
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Affiliation(s)
- Usamma Amjad
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15213
| | - Jiwon Choi
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15213
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, Maryland 20815
| | - Daniel J Gibson
- Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
| | - Raymond Murray
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15213
| | - Ann M Graybiel
- Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
| | - Helen N Schwerdt
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15213
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, Maryland 20815
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Eckmann S, Young EJ, Gjorgjieva J. Synapse-type-specific competitive Hebbian learning forms functional recurrent networks. Proc Natl Acad Sci U S A 2024; 121:e2305326121. [PMID: 38870059 PMCID: PMC11194505 DOI: 10.1073/pnas.2305326121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 04/25/2024] [Indexed: 06/15/2024] Open
Abstract
Cortical networks exhibit complex stimulus-response patterns that are based on specific recurrent interactions between neurons. For example, the balance between excitatory and inhibitory currents has been identified as a central component of cortical computations. However, it remains unclear how the required synaptic connectivity can emerge in developing circuits where synapses between excitatory and inhibitory neurons are simultaneously plastic. Using theory and modeling, we propose that a wide range of cortical response properties can arise from a single plasticity paradigm that acts simultaneously at all excitatory and inhibitory connections-Hebbian learning that is stabilized by the synapse-type-specific competition for a limited supply of synaptic resources. In plastic recurrent circuits, this competition enables the formation and decorrelation of inhibition-balanced receptive fields. Networks develop an assembly structure with stronger synaptic connections between similarly tuned excitatory and inhibitory neurons and exhibit response normalization and orientation-specific center-surround suppression, reflecting the stimulus statistics during training. These results demonstrate how neurons can self-organize into functional networks and suggest an essential role for synapse-type-specific competitive learning in the development of cortical circuits.
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Affiliation(s)
- Samuel Eckmann
- Computation in Neural Circuits Group, Max Planck Institute for Brain Research, Frankfurt am Main60438, Germany
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, CambridgeCB2 1PZ, United Kingdom
| | - Edward James Young
- Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, CambridgeCB2 1PZ, United Kingdom
| | - Julijana Gjorgjieva
- Computation in Neural Circuits Group, Max Planck Institute for Brain Research, Frankfurt am Main60438, Germany
- School of Life Sciences, Technical University Munich, Freising85354, Germany
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8
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Parnas M, Manoim JE, Lin AC. Sensory encoding and memory in the mushroom body: signals, noise, and variability. Learn Mem 2024; 31:a053825. [PMID: 38862174 PMCID: PMC11199953 DOI: 10.1101/lm.053825.123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Accepted: 11/21/2023] [Indexed: 06/13/2024]
Abstract
To survive in changing environments, animals need to learn to associate specific sensory stimuli with positive or negative valence. How do they form stimulus-specific memories to distinguish between positively/negatively associated stimuli and other irrelevant stimuli? Solving this task is one of the functions of the mushroom body, the associative memory center in insect brains. Here we summarize recent work on sensory encoding and memory in the Drosophila mushroom body, highlighting general principles such as pattern separation, sparse coding, noise and variability, coincidence detection, and spatially localized neuromodulation, and placing the mushroom body in comparative perspective with mammalian memory systems.
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Affiliation(s)
- Moshe Parnas
- Department of Physiology and Pharmacology, Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 69978, Israel
| | - Julia E Manoim
- Department of Physiology and Pharmacology, Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - Andrew C Lin
- School of Biosciences, University of Sheffield, Sheffield S10 2TN, United Kingdom
- Neuroscience Institute, University of Sheffield, Sheffield S10 2TN, United Kingdom
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9
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Billot A, Kiran S. Disentangling neuroplasticity mechanisms in post-stroke language recovery. BRAIN AND LANGUAGE 2024; 251:105381. [PMID: 38401381 PMCID: PMC10981555 DOI: 10.1016/j.bandl.2024.105381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 11/28/2023] [Accepted: 01/12/2024] [Indexed: 02/26/2024]
Abstract
A major objective in post-stroke aphasia research is to gain a deeper understanding of neuroplastic mechanisms that drive language recovery, with the ultimate goal of enhancing treatment outcomes. Subsequent to recent advances in neuroimaging techniques, we now have the ability to examine more closely how neural activity patterns change after a stroke. However, the way these neural activity changes relate to language impairments and language recovery is still debated. The aim of this review is to provide a theoretical framework to better investigate and interpret neuroplasticity mechanisms underlying language recovery in post-stroke aphasia. We detail two sets of neuroplasticity mechanisms observed at the synaptic level that may explain functional neuroimaging findings in post-stroke aphasia recovery at the network level: feedback-based homeostatic plasticity and associative Hebbian plasticity. In conjunction with these plasticity mechanisms, higher-order cognitive control processes dynamically modulate neural activity in other regions to meet communication demands, despite reduced neural resources. This work provides a network-level neurobiological framework for understanding neural changes observed in post-stroke aphasia and can be used to define guidelines for personalized treatment development.
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Affiliation(s)
- Anne Billot
- Center for Brain Recovery, Boston University, Boston, USA; Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, USA; Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Swathi Kiran
- Center for Brain Recovery, Boston University, Boston, USA.
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10
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Li Y, Wu J, Hua X, Zheng M, Xu J. The promotion-like effect of the M1-STN hyperdirect pathway induced by ccPAS enhanced balance performances: From the perspective of brain connectivity. CNS Neurosci Ther 2024; 30:e14710. [PMID: 38615363 PMCID: PMC11016345 DOI: 10.1111/cns.14710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 03/16/2024] [Accepted: 03/26/2024] [Indexed: 04/16/2024] Open
Abstract
AIMS The present study aimed to explore the effect of cortico-cortical paired-associative stimulation (ccPAS) in modulating hyperdirect pathway and its influence on balance performance. METHODS Forty healthy participants were randomly allocated to the active ccPAS group (n = 20) or the sham ccPAS group (n = 20). The primary motor cortex and subthalamic nucleus were stimulated sequentially with ccPAS. Unlike the active ccPAS group, one wing of coil was tilted to form a 90° angle with scalp of stimulation locations for the sham ccPAS group. Magnetic resonance imaging, functional reach test (FRT), timed up and go (TUG) test, and limit of stability (LOS) test were performed, and correlation between them was also analyzed. RESULTS Three participants in the sham ccPAS group were excluded because of poor quality of NIfTI images. The active group had strengthened hyperdirect pathway, increased functional connectivity (FC) between orbital part of frontal cortex and bilateral precuneus, and decreased FC among basal ganglia (all p < 0.05). Regional network properties of triangular and orbital parts of IFG, middle cingulate cortex, and hippocampus increased. The active group performed better in FRT and LOS (all p < 0.05). FRT positively correlated with FC of the hyperdirect pathway (r = 0.439, p = 0.007) and FCs between orbital part of frontal cortex and bilateral precuneus (all p < 0.05). CONCLUSION The ccPAS enhanced balance performance by promotion-like plasticity mechanisms through the hyperdirect pathway.
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Affiliation(s)
- Yu‐Lin Li
- Engineering Research Center of Traditional Chinese Medicine Intelligent RehabilitationMinistry of EducationShanghaiChina
- Department of Rehabilitation Medicine, Huashan HospitalFudan UniversityShanghaiChina
| | - Jia‐Jia Wu
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western MedicineShanghai University of Traditional Chinese MedicineShanghaiChina
| | - Xu‐Yun Hua
- Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western MedicineShanghai University of Traditional Chinese MedicineShanghaiChina
| | - Mou‐Xiong Zheng
- Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western MedicineShanghai University of Traditional Chinese MedicineShanghaiChina
| | - Jian‐Guang Xu
- Engineering Research Center of Traditional Chinese Medicine Intelligent RehabilitationMinistry of EducationShanghaiChina
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western MedicineShanghai University of Traditional Chinese MedicineShanghaiChina
- School of Rehabilitation ScienceShanghai University of Traditional Chinese MedicineShanghaiChina
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11
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Gao Y, Zhou Y, Ji X, Graham AJ, Dundas CM, Miniel Mahfoud IE, Tibbett BM, Tan B, Partipilo G, Dodabalapur A, Rivnay J, Keitz BK. A hybrid transistor with transcriptionally controlled computation and plasticity. Nat Commun 2024; 15:1598. [PMID: 38383505 PMCID: PMC10881478 DOI: 10.1038/s41467-024-45759-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 02/02/2024] [Indexed: 02/23/2024] Open
Abstract
Organic electrochemical transistors (OECTs) are ideal devices for translating biological signals into electrical readouts and have applications in bioelectronics, biosensing, and neuromorphic computing. Despite their potential, developing programmable and modular methods for living systems to interface with OECTs has proven challenging. Here we describe hybrid OECTs containing the model electroactive bacterium Shewanella oneidensis that enable the transduction of biological computations to electrical responses. Specifically, we fabricated planar p-type OECTs and demonstrated that channel de-doping is driven by extracellular electron transfer (EET) from S. oneidensis. Leveraging this mechanistic understanding and our ability to control EET flux via transcriptional regulation, we used plasmid-based Boolean logic gates to translate biological computation into current changes within the OECT. Finally, we demonstrated EET-driven changes to OECT synaptic plasticity. This work enables fundamental EET studies and OECT-based biosensing and biocomputing systems with genetically controllable and modular design elements.
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Affiliation(s)
- Yang Gao
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, TX, 78712, USA
| | - Yuchen Zhou
- Department of Electrical and Computer Engineering, University of Texas at Austin, Austin, TX, 78712, USA
- Microelectronics Research Center, University of Texas at Austin, Austin, TX, 78758, USA
| | - Xudong Ji
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, 60208, USA
- Simpson Querrey Institute, Northwestern University, Chicago, IL, 60611, USA
| | - Austin J Graham
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, TX, 78712, USA
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Christopher M Dundas
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, TX, 78712, USA
- Department of Biology, Stanford University, Stanford, CA, 94305, USA
| | - Ismar E Miniel Mahfoud
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, TX, 78712, USA
| | - Bailey M Tibbett
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, TX, 78712, USA
| | - Benjamin Tan
- Microelectronics Research Center, University of Texas at Austin, Austin, TX, 78758, USA
- Department of Chemistry, University of Texas at Austin, Austin, TX, 78712, USA
| | - Gina Partipilo
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, TX, 78712, USA
| | - Ananth Dodabalapur
- Department of Electrical and Computer Engineering, University of Texas at Austin, Austin, TX, 78712, USA
- Microelectronics Research Center, University of Texas at Austin, Austin, TX, 78758, USA
| | - Jonathan Rivnay
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, 60208, USA
- Simpson Querrey Institute, Northwestern University, Chicago, IL, 60611, USA
| | - Benjamin K Keitz
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, TX, 78712, USA.
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12
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Hadler MD, Tzilivaki A, Schmitz D, Alle H, Geiger JRP. Gamma oscillation plasticity is mediated via parvalbumin interneurons. SCIENCE ADVANCES 2024; 10:eadj7427. [PMID: 38295164 PMCID: PMC10830109 DOI: 10.1126/sciadv.adj7427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 01/02/2024] [Indexed: 02/02/2024]
Abstract
Understanding the plasticity of neuronal networks is an emerging field of (patho-) physiological research, yet the underlying cellular mechanisms remain poorly understood. Gamma oscillations (30 to 80 hertz), a biomarker of cognitive performance, require and potentiate glutamatergic transmission onto parvalbumin-positive interneurons (PVIs), suggesting an interface for cell-to-network plasticity. In ex vivo local field potential recordings, we demonstrate long-term potentiation of hippocampal gamma power. Gamma potentiation obeys established rules of PVI plasticity, requiring calcium-permeable AMPA receptors (CP-AMPARs) and metabotropic glutamate receptors (mGluRs). A microcircuit computational model of CA3 gamma oscillations predicts CP-AMPAR plasticity onto PVIs critically outperforms pyramidal cell plasticity in increasing gamma power and completely accounts for gamma potentiation. We reaffirm this ex vivo in three PVI-targeting animal models, demonstrating that gamma potentiation requires PVI-specific signaling via a Gq/PKC pathway comprising mGluR5 and a Gi-sensitive, PKA-dependent pathway. Gamma activity-dependent, metabotropically mediated CP-AMPAR plasticity on PVIs may serve as a guiding principle in understanding network plasticity in health and disease.
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Affiliation(s)
- Michael D. Hadler
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Charitéplatz 1, 10117 Berlin, Germany
- Institute of Neurophysiology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Alexandra Tzilivaki
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Charitéplatz 1, 10117 Berlin, Germany
- Einstein Center for Neurosciences Berlin, Charitéplatz 1, 10117 Berlin, Germany
- Neurocure Cluster of Excellence, Charitéplatz 1, 10117 Berlin, Germany
| | - Dietmar Schmitz
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Charitéplatz 1, 10117 Berlin, Germany
- Einstein Center for Neurosciences Berlin, Charitéplatz 1, 10117 Berlin, Germany
- Neurocure Cluster of Excellence, Charitéplatz 1, 10117 Berlin, Germany
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Robert Rössle-Straße 10, 13125 Berlin, Germany
| | - Henrik Alle
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Charitéplatz 1, 10117 Berlin, Germany
- Institute of Neurophysiology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Jörg R. P. Geiger
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Charitéplatz 1, 10117 Berlin, Germany
- Institute of Neurophysiology, Charité-Universitätsmedizin Berlin, Berlin, Germany
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13
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Rivera-Villaseñor A, Higinio-Rodríguez F, López-Hidalgo M. Astrocytes in Pain Perception: A Systems Neuroscience Approach. ADVANCES IN NEUROBIOLOGY 2024; 39:193-212. [PMID: 39190076 DOI: 10.1007/978-3-031-64839-7_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/28/2024]
Abstract
Astrocytes play an active role in the function of the brain integrating neuronal activity and regulating back neuronal dynamic. They have recently emerged as active contributors of brain's emergent properties such as perceptions. Here, we analyzed the role of astrocytes in pain perception from the lens of systems neuroscience, and we do this by analyzing how astrocytes encode nociceptive information within brain processing areas and how they are key regulators of the internal state that determines pain perception. Specifically, we discuss the dynamic interactions between astrocytes and neuromodulators, such as noradrenaline, highlighting their role in shaping the level of activation of the neuronal ensemble, thereby influencing the experience of pain. Also, we will discuss the possible implications of an "Astro-NeuroMatrix" in the integration of pain across sensory, affective, and cognitive dimensions of pain perception.
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Affiliation(s)
- Angélica Rivera-Villaseñor
- Escuela Nacional de Estudios Superiores, Universidad Nacional Autónoma de México, Queretaro, Qro., Mexico
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Queretaro, Qro., Mexico
| | - Frida Higinio-Rodríguez
- Escuela Nacional de Estudios Superiores, Universidad Nacional Autónoma de México, Queretaro, Qro., Mexico
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Queretaro, Qro., Mexico
| | - Mónica López-Hidalgo
- Escuela Nacional de Estudios Superiores, Universidad Nacional Autónoma de México, Queretaro, Qro., Mexico.
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14
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Buxton RB, Wong EC. Metabolic energetics underlying attractors in neural models. J Neurophysiol 2024; 131:88-105. [PMID: 38056422 DOI: 10.1152/jn.00120.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 11/13/2023] [Accepted: 12/04/2023] [Indexed: 12/08/2023] Open
Abstract
Neural population modeling, including the role of neural attractors, is a promising tool for understanding many aspects of brain function. We propose a modeling framework to connect the abstract variables used in modeling to recent cellular-level estimates of the bioenergetic costs of different aspects of neural activity, measured in ATP consumed per second per neuron. Based on recent work, an empirical reference for brain ATP use for the awake resting brain was estimated as ∼2 × 109 ATP/s-neuron across several mammalian species. The energetics framework was applied to the Wilson-Cowan (WC) model of two interacting populations of neurons, one excitatory (E) and one inhibitory (I). Attractors were considered to exhibit steady-state behavior and limit cycle behavior, both of which end when the excitatory stimulus ends, and sustained activity that persists after the stimulus ends. The energy cost of limit cycles, with oscillations much faster than the average neuronal firing rate of the population, is tracked more closely with the firing rate than the limit cycle frequency. Self-sustained firing driven by recurrent excitation, though, involves higher firing rates and a higher energy cost. As an example of a simple network in which each node is a WC model, a combination of three nodes can serve as a flexible circuit element that turns on with an oscillating output when input passes a threshold and then persists after the input ends (an "on-switch"), with moderate overall ATP use. The proposed framework can serve as a guide for anchoring neural population models to plausible bioenergetics requirements.NEW & NOTEWORTHY This work bridges two approaches for understanding brain function: cellular-level studies of the metabolic energy costs of different aspects of neural activity and neural population modeling, including the role of neural attractors. The proposed modeling framework connects energetic costs, in ATP consumed per second per neuron, to the more abstract variables used in neural population modeling. In particular, this work anchors potential neural attractors to physiologically plausible bioenergetics requirements.
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Affiliation(s)
- Richard B Buxton
- Department of Radiology, University of California, San Diego, California, United States
| | - Eric C Wong
- Department of Radiology, University of California, San Diego, California, United States
- Department of Psychiatry, University of California, San Diego, California, United States
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15
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Amjad U, Choi J, Gibson DJ, Murray R, Graybiel AM, Schwerdt HN. Synchronous Measurements of Extracellular Action Potentials and Neurochemical Activity with Carbon Fiber Electrodes in Nonhuman Primates. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.23.573130. [PMID: 38187624 PMCID: PMC10769335 DOI: 10.1101/2023.12.23.573130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Measuring the dynamic relationship between neuromodulators, such as dopamine, and neuronal action potentials is imperative to understand how these fundamental modes of neural signaling interact to mediate behavior. Here, we developed methods to measure concurrently dopamine and extracellular action potentials (i.e., spikes) and applied these in a monkey performing a behavioral task. Standard fast-scan cyclic voltammetric (FSCV) electrochemical (EChem) and electrophysiological (EPhys) recording systems are combined and used to collect spike and dopamine signals, respectively, from an array of carbon fiber (CF) sensors implanted in the monkey striatum. FSCV requires the application of small voltages at the implanted sensors to measure redox currents generated from target molecules, such as dopamine. These applied voltages create artifacts at neighboring EPhys-measurement sensors, producing signals that may falsely be classified as physiological spikes. Therefore, simple automated temporal interpolation algorithms were designed to remove these artifacts and enable accurate spike extraction. We validated these methods using simulated artifacts and demonstrated an average spike recovery rate of 84.5%. This spike extraction was performed on data collected from concurrent EChem and EPhys recordings made in a task-performing monkey to discriminate cell-type specific striatal units. These identified units were shown to correlate to specific behavioral task parameters related to reward size and eye-movement direction. Synchronous measures of spike and dopamine signals displayed contrasting relations to the behavioral task parameters, as taken from our small set of representative data, suggesting a complex relationship between these two modes of neural signaling. Future application of our methods will help advance our understanding of the interactions between neuromodulator signaling and neuronal activity, to elucidate more detailed mechanisms of neural circuitry and plasticity mediating behaviors in health and in disease.
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Affiliation(s)
- Usamma Amjad
- Department of Bioengineering, University of Pittsburgh, USA
| | - Jiwon Choi
- Department of Bioengineering, University of Pittsburgh, USA
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - Daniel J Gibson
- McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, USA
| | - Raymond Murray
- Department of Bioengineering, University of Pittsburgh, USA
| | - Ann M Graybiel
- McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, USA
| | - Helen N Schwerdt
- Department of Bioengineering, University of Pittsburgh, USA
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
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16
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Piette C, Gervasi N, Venance L. Synaptic plasticity through a naturalistic lens. Front Synaptic Neurosci 2023; 15:1250753. [PMID: 38145207 PMCID: PMC10744866 DOI: 10.3389/fnsyn.2023.1250753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 11/20/2023] [Indexed: 12/26/2023] Open
Abstract
From the myriad of studies on neuronal plasticity, investigating its underlying molecular mechanisms up to its behavioral relevance, a very complex landscape has emerged. Recent efforts have been achieved toward more naturalistic investigations as an attempt to better capture the synaptic plasticity underpinning of learning and memory, which has been fostered by the development of in vivo electrophysiological and imaging tools. In this review, we examine these naturalistic investigations, by devoting a first part to synaptic plasticity rules issued from naturalistic in vivo-like activity patterns. We next give an overview of the novel tools, which enable an increased spatio-temporal specificity for detecting and manipulating plasticity expressed at individual spines up to neuronal circuit level during behavior. Finally, we put particular emphasis on works considering brain-body communication loops and macroscale contributors to synaptic plasticity, such as body internal states and brain energy metabolism.
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Affiliation(s)
- Charlotte Piette
- Center for Interdisciplinary Research in Biology (CIRB), College de France, CNRS, INSERM, Université PSL, Paris, France
| | | | - Laurent Venance
- Center for Interdisciplinary Research in Biology (CIRB), College de France, CNRS, INSERM, Université PSL, Paris, France
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17
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Yamakou ME, Desroches M, Rodrigues S. Synchronization in STDP-driven memristive neural networks with time-varying topology. J Biol Phys 2023; 49:483-507. [PMID: 37656327 PMCID: PMC10651826 DOI: 10.1007/s10867-023-09642-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Accepted: 08/07/2023] [Indexed: 09/02/2023] Open
Abstract
Synchronization is a widespread phenomenon in the brain. Despite numerous studies, the specific parameter configurations of the synaptic network structure and learning rules needed to achieve robust and enduring synchronization in neurons driven by spike-timing-dependent plasticity (STDP) and temporal networks subject to homeostatic structural plasticity (HSP) rules remain unclear. Here, we bridge this gap by determining the configurations required to achieve high and stable degrees of complete synchronization (CS) and phase synchronization (PS) in time-varying small-world and random neural networks driven by STDP and HSP. In particular, we found that decreasing P (which enhances the strengthening effect of STDP on the average synaptic weight) and increasing F (which speeds up the swapping rate of synapses between neurons) always lead to higher and more stable degrees of CS and PS in small-world and random networks, provided that the network parameters such as the synaptic time delay [Formula: see text], the average degree [Formula: see text], and the rewiring probability [Formula: see text] have some appropriate values. When [Formula: see text], [Formula: see text], and [Formula: see text] are not fixed at these appropriate values, the degree and stability of CS and PS may increase or decrease when F increases, depending on the network topology. It is also found that the time delay [Formula: see text] can induce intermittent CS and PS whose occurrence is independent F. Our results could have applications in designing neuromorphic circuits for optimal information processing and transmission via synchronization phenomena.
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Affiliation(s)
- Marius E Yamakou
- Department of Data Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Cauerstr. 11, 91058, Erlangen, Germany.
- Max-Planck-Institut für Mathematik in den Naturwissenschaften, Inselstr. 22, 04103, Leipzig, Germany.
| | - Mathieu Desroches
- MathNeuro Project-Team, Inria Center at Université Côte d'Azur, 2004 route des Lucioles - BP 93, 06902, Cedex, Sophia Antipolis, France
| | - Serafim Rodrigues
- Mathematical, Computational and Experimental Neuroscience, Basque Center for Applied Mathematics, Alameda de Mazzaredo 14, 48009, Bilbao, Spain
- Ikerbasque, Basque Foundation for Science, Plaza Euskadi 5, 48009, Bilbao, Spain
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18
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Yang Y, Booth V, Zochowski M. Acetylcholine facilitates localized synaptic potentiation and location specific feature binding. Front Neural Circuits 2023; 17:1239096. [PMID: 38033788 PMCID: PMC10684311 DOI: 10.3389/fncir.2023.1239096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 10/11/2023] [Indexed: 12/02/2023] Open
Abstract
Forebrain acetylcholine (ACh) signaling has been shown to drive attention and learning. Recent experimental evidence of spatially and temporally constrained cholinergic signaling has sparked interest to investigate how it facilitates stimulus-induced learning. We use biophysical excitatory-inhibitory (E-I) multi-module neural network models to show that external stimuli and ACh signaling can mediate spatially constrained synaptic potentiation patterns. The effects of ACh on neural excitability are simulated by varying the conductance of a muscarinic receptor-regulated hyperpolarizing slow K+ current (m-current). Each network module consists of an E-I network with local excitatory connectivity and global inhibitory connectivity. The modules are interconnected with plastic excitatory synaptic connections, that change via a spike-timing-dependent plasticity (STDP) rule. Our results indicate that spatially constrained ACh release influences the information flow represented by network dynamics resulting in selective reorganization of inter-module interactions. Moreover the information flow depends on the level of synchrony in the network. For highly synchronous networks, the more excitable module leads firing in the less excitable one resulting in strengthening of the outgoing connections from the former and weakening of its incoming synapses. For networks with more noisy firing patterns, activity in high ACh regions is prone to induce feedback firing of synchronous volleys and thus strengthening of the incoming synapses to the more excitable region and weakening of outgoing synapses. Overall, these results suggest that spatially and directionally specific plasticity patterns, as are presumed necessary for feature binding, can be mediated by spatially constrained ACh release.
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Affiliation(s)
- Yihao Yang
- Department of Physics, University of Michigan, Ann Arbor, MI, United States
| | - Victoria Booth
- Departments of Mathematics and Anesthesiology, University of Michigan, Ann Arbor, MI, United States
| | - Michal Zochowski
- Department of Physics and Biophysics Program, University of Michigan, Ann Arbor, MI, United States
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19
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Hernandez-Pavon JC, San Agustín A, Wang MC, Veniero D, Pons JL. Can we manipulate brain connectivity? A systematic review of cortico-cortical paired associative stimulation effects. Clin Neurophysiol 2023; 154:169-193. [PMID: 37634335 DOI: 10.1016/j.clinph.2023.06.016] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 05/09/2023] [Accepted: 06/16/2023] [Indexed: 08/29/2023]
Abstract
OBJECTIVE Cortico-cortical paired associative stimulation (ccPAS) is a form of dual-site transcranial magnetic stimulation (TMS) entailing a series of single-TMS pulses paired at specific interstimulus intervals (ISI) delivered to distant cortical areas. The goal of this article is to systematically review its efficacy in inducing plasticity in humans focusing on stimulation parameters and hypotheses of underlying neurophysiology. METHODS A systematic review of the literature from 2009-2023 was undertaken to identify all articles utilizing ccPAS to study brain plasticity and connectivity. Six electronic databases were searched and included. RESULTS 32 studies were identified. The studies targeted connections within the same hemisphere or between hemispheres. 28 ccPAS studies were in healthy participants, 1 study in schizophrenia, and 1 in Alzheimer's disease (AD) patients. 2 additional studies used cortico-cortical repetitive paired associative stimulation (cc-rPAS) in generalized anxiety disorder (GAD) patients. Outcome measures include electromyography (EMG), behavioral measures, electroencephalography (EEG), and functional magnetic resonance imaging (fMRI). ccPAS seems to be able to modulate brain connectivity depending on the ISI. CONCLUSIONS ccPAS can be used to modulate corticospinal excitability, brain activity, and behavior. Although the stimulation parameters used across studies reviewed in this paper are varied, ccPAS is a promising approach for basic research and potential clinical applications. SIGNIFICANCE Recent advances in neuroscience have caused a shift of interest from the study of single areas to a more complex approach focusing on networks of areas that orchestrate brain activity. Consequently, the TMS community is also witnessing a change, with a growing interest in targeting multiple brain areas rather than a single locus, as evidenced by an increasing number of papers using ccPAS. In light of this new enthusiasm for brain connectivity, this review summarizes existing literature and stimulation parameters that have proven effective in changing electrophysiological, behavioral, or neuroimaging-derived measures.
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Affiliation(s)
- Julio C Hernandez-Pavon
- Legs + Walking Lab, Shirley Ryan AbilityLab (Formerly, The Rehabilitation Institute of Chicago), Chicago, IL, USA; Center for Brain Stimulation, Shirley Ryan AbilityLab, Chicago, IL, USA; Department of Physical Medicine and Rehabilitation, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA; Department of Psychological Sciences, Kansas State University, Manhattan, KS, USA.
| | - Arantzazu San Agustín
- Legs + Walking Lab, Shirley Ryan AbilityLab (Formerly, The Rehabilitation Institute of Chicago), Chicago, IL, USA; Center for Brain Stimulation, Shirley Ryan AbilityLab, Chicago, IL, USA; Department of Physical Medicine and Rehabilitation, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA; Neural Rehabilitation Group, Cajal Institute, CSIC, Madrid, Spain; PhD Program in Neuroscience, Autonoma de Madrid University-Cajal Institute, Madrid 28029, Spain
| | - Max C Wang
- Department of Physical Therapy and Human Movement Science, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | | | - Jose L Pons
- Legs + Walking Lab, Shirley Ryan AbilityLab (Formerly, The Rehabilitation Institute of Chicago), Chicago, IL, USA; Center for Brain Stimulation, Shirley Ryan AbilityLab, Chicago, IL, USA; Department of Physical Medicine and Rehabilitation, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA; Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Chicago, IL, USA
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20
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Liu Y, Wang X, Zeng Z, Zhang W, Qu H. An event-driven Spike-DBN model for fault diagnosis using reward-STDP. ISA TRANSACTIONS 2023; 140:55-70. [PMID: 37385860 DOI: 10.1016/j.isatra.2023.06.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 06/07/2023] [Accepted: 06/07/2023] [Indexed: 07/01/2023]
Abstract
Deep neural networks (DNNs) have shown high accuracy in fault diagnosis, but they struggle to effectively capture changes over time in multivariate time-series data and suffer from resource consumption issues. Spike deep belief networks (spike-DBNs) address these limitations by capturing the change in time-varying signals and reducing resource consumption, but they sacrifice accuracy. To overcome these limitations, we propose integrating an event-driven approach into spike-DBNs through the Latency-Rate coding method and the reward-STDP learning rule. The encoding method enhances the event representation capability, while the learning rule focuses on the global behavior of spiking neurons triggered by events. Our proposed method not only maintains low resource consumption but also improves the fault diagnosis ability of spike-DBNs. We conducted a series of experiments to verify our model's performance, and the results demonstrate that our proposed method improves the accuracy of fault classification of manipulators and reduces learning time by nearly 76% compared to spike-CNN under the same conditions.
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Affiliation(s)
- Ying Liu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
| | - Xiuqing Wang
- College of Computer and Cyber Security, Hebei Normal University, Shijiazhuang 050024, China.
| | - Zihang Zeng
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
| | - Wei Zhang
- School of Electronic and Information Engineering, Hebei University of Technology, Tianjin 300401, China.
| | - Hong Qu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
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21
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Zhang T, Cheng X, Jia S, Li CT, Poo MM, Xu B. A brain-inspired algorithm that mitigates catastrophic forgetting of artificial and spiking neural networks with low computational cost. SCIENCE ADVANCES 2023; 9:eadi2947. [PMID: 37624895 PMCID: PMC10456855 DOI: 10.1126/sciadv.adi2947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Accepted: 07/27/2023] [Indexed: 08/27/2023]
Abstract
Neuromodulators in the brain act globally at many forms of synaptic plasticity, represented as metaplasticity, which is rarely considered by existing spiking (SNNs) and nonspiking artificial neural networks (ANNs). Here, we report an efficient brain-inspired computing algorithm for SNNs and ANNs, referred to here as neuromodulation-assisted credit assignment (NACA), which uses expectation signals to induce defined levels of neuromodulators to selective synapses, whereby the long-term synaptic potentiation and depression are modified in a nonlinear manner depending on the neuromodulator level. The NACA algorithm achieved high recognition accuracy with substantially reduced computational cost in learning spatial and temporal classification tasks. Notably, NACA was also verified as efficient for learning five different class continuous learning tasks with varying degrees of complexity, exhibiting a markedly mitigated catastrophic forgetting at low computational cost. Mapping synaptic weight changes showed that these benefits could be explained by the sparse and targeted synaptic modifications attributed to expectation-based global neuromodulation.
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Affiliation(s)
- Tielin Zhang
- Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
- Shanghai Center for Brain Science and Brain-inspired Technology, Lingang Laboratory, Shanghai 200031, China
| | - Xiang Cheng
- Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shuncheng Jia
- Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Chengyu T Li
- Shanghai Center for Brain Science and Brain-inspired Technology, Lingang Laboratory, Shanghai 200031, China
- Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China
| | - Mu-ming Poo
- Shanghai Center for Brain Science and Brain-inspired Technology, Lingang Laboratory, Shanghai 200031, China
- Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China
| | - Bo Xu
- Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
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22
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Sun W, Wu Q, Gao L, Zheng Z, Xiang H, Yang K, Yu B, Yao J. Advancements in Transcranial Magnetic Stimulation Research and the Path to Precision. Neuropsychiatr Dis Treat 2023; 19:1841-1851. [PMID: 37641588 PMCID: PMC10460597 DOI: 10.2147/ndt.s414782] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 08/02/2023] [Indexed: 08/31/2023] Open
Abstract
Transcranial magnetic stimulation (TMS) has become increasingly popular in clinical practice in recent years, and there have been significant advances in the principles and stimulation modes of TMS. With the development of multi-mode and precise stimulation technology, it is crucial to have a comprehensive understanding of TMS. The neuroregulatory effects of TMS can vary depending on the specific mode of stimulation, highlighting the importance of exploring these effects through multimodal application. Additionally, the use of precise TMS therapy can help enhance our understanding of the neural mechanisms underlying these effects, providing us with a more comprehensive perspective. This article aims to review the mechanism of action, stimulation mode, multimodal application, and precision of TMS.
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Affiliation(s)
- Wei Sun
- Department of Psychiatry, the Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang City, Sichuan Province, People’s Republic of China
| | - Qiao Wu
- Department of Psychiatry, the Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang City, Sichuan Province, People’s Republic of China
| | - Li Gao
- Department of Neurology, The Third People’s Hospital of Chengdu, Chengdu Institute of Neurological Diseases, Chengdu City, Sichuan Province, People’s Republic of China
| | - Zhong Zheng
- Neurobiological Detection Center, West China Hospital Affiliated to Sichuan University, Chengdu City, Sichuan Province, People’s Republic of China
| | - Hu Xiang
- Department of Psychiatry, the Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang City, Sichuan Province, People’s Republic of China
| | - Kun Yang
- Department of Psychiatry, the Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang City, Sichuan Province, People’s Republic of China
| | - Bo Yu
- Department of Psychiatry, the Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang City, Sichuan Province, People’s Republic of China
| | - Jing Yao
- Department of Psychiatry, the Third Hospital of Mianyang, Sichuan Mental Health Center, Mianyang City, Sichuan Province, People’s Republic of China
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Gao Y, Zhou Y, Ji X, Graham AJ, Dundas CM, Mahfoud IEM, Tibbett BM, Tan B, Partipilo G, Dodabalapur A, Rivnay J, Keitz BK. A Hybrid Transistor with Transcriptionally Controlled Computation and Plasticity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.16.553547. [PMID: 37645977 PMCID: PMC10462107 DOI: 10.1101/2023.08.16.553547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Organic electrochemical transistors (OECTs) are ideal devices for translating biological signals into electrical readouts and have applications in bioelectronics, biosensing, and neuromorphic computing. Despite their potential, developing programmable and modular methods for living systems to interface with OECTs has proven challenging. Here we describe hybrid OECTs containing the model electroactive bacterium Shewanella oneidensis that enable the transduction of biological computations to electrical responses. Specifically, we fabricated planar p-type OECTs and demonstrated that channel de-doping is driven by extracellular electron transfer (EET) from S. oneidensis. Leveraging this mechanistic understanding and our ability to control EET flux via transcriptional regulation, we used plasmid-based Boolean logic gates to translate biological computation into current changes within the OECT. Finally, we demonstrated EET-driven changes to OECT synaptic plasticity. This work enables fundamental EET studies and OECT-based biosensing and biocomputing systems with genetically controllable and modular design elements.
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Affiliation(s)
- Yang Gao
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, TX, 78712, USA
| | - Yuchen Zhou
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, 78712, USA
- Microelectronics Research Center, The University of Texas at Austin, Austin, TX, 78758, USA
| | - Xudong Ji
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, 60208, USA
- Simpson Querrey Institute, Northwestern University, Chicago, IL, 60611, USA
| | - Austin J. Graham
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, TX, 78712, USA
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Christopher M. Dundas
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, TX, 78712, USA
- Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Ismar E. Miniel Mahfoud
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, TX, 78712, USA
| | - Bailey M. Tibbett
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, TX, 78712, USA
| | - Benjamin Tan
- Microelectronics Research Center, The University of Texas at Austin, Austin, TX, 78758, USA
- Department of Chemistry, University of Texas at Austin, Austin, TX, 78712, USA
| | - Gina Partipilo
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, TX, 78712, USA
| | - Ananth Dodabalapur
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, 78712, USA
- Microelectronics Research Center, The University of Texas at Austin, Austin, TX, 78758, USA
| | - Jonathan Rivnay
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, 60208, USA
- Simpson Querrey Institute, Northwestern University, Chicago, IL, 60611, USA
| | - Benjamin K. Keitz
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, TX, 78712, USA
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Munch AS, Amat-Foraster M, Agerskov C, Bastlund JF, Herrik KF, Richter U. Sub-anesthetic doses of ketamine increase single cell entrainment in the rat auditory cortex during auditory steady-state response. J Psychopharmacol 2023; 37:822-835. [PMID: 37165655 DOI: 10.1177/02698811231164231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
BACKGROUND Understanding the effects of the N-methyl-D-aspartate receptor (NMDA-R) antagonist ketamine on brain function is of considerable interest due to the discovery of its fast-acting antidepressant properties. It is well known that gamma oscillations are increased when ketamine is administered to rodents and humans, and increases in the auditory steady-state response (ASSR) have also been observed. AIMS To elucidate the cellular substrate of the increase in network activity and synchrony observed by sub-anesthetic doses of ketamine, the aim was to investigate spike timing and regularity and determine how this is affected by the animal's motor state. METHODS Single unit activity and local field potentials from the auditory cortex of awake, freely moving rats were recorded with microelectrode arrays during an ASSR paradigm. RESULTS Ketamine administration yielded a significant increase in ASSR power and phase locking, both significantly modulated by motor activity. Before drug administration, putative fast-spiking interneurons (FSIs) were significantly more entrained to the stimulus than putative pyramidal neurons (PYRs). The degree of entrainment significantly increased at lower doses of ketamine (3 and 10 mg/kg for FSIs, 10 mg/kg for PYRs). At the highest dose (30 mg/kg), a strong increase in tonic firing of PYRs was observed. CONCLUSIONS These findings suggest an involvement of FSIs in the increased network synchrony and provide a possible cellular explanation for the well-documented effects of ketamine-induced increase in power and synchronicity during ASSR. The results support the importance to evaluate different motor states separately for more translational preclinical research.
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Affiliation(s)
- Anders Sonne Munch
- Brain Circuit and Function, Lundbeck & University of Copenhagen, Kobenhavn, Denmark
| | | | - Claus Agerskov
- Pathology, Circuits and Symptoms, Lundbeck, Valby, Denmark
| | | | | | - Ulrike Richter
- Pathology, Circuits and Symptoms, Lundbeck, Valby, Denmark
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25
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Yuan Y, Zhu Y, Wang J, Li R, Xu X, Fang T, Huo H, Wan L, Li Q, Liu N, Yang S. Incorporating structural plasticity into self-organization recurrent networks for sequence learning. Front Neurosci 2023; 17:1224752. [PMID: 37592946 PMCID: PMC10427342 DOI: 10.3389/fnins.2023.1224752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 07/13/2023] [Indexed: 08/19/2023] Open
Abstract
Introduction Spiking neural networks (SNNs), inspired by biological neural networks, have received a surge of interest due to its temporal encoding. Biological neural networks are driven by multiple plasticities, including spike timing-dependent plasticity (STDP), structural plasticity, and homeostatic plasticity, making network connection patterns and weights to change continuously during the lifecycle. However, it is unclear how these plasticities interact to shape neural networks and affect neural signal processing. Method Here, we propose a reward-modulated self-organization recurrent network with structural plasticity (RSRN-SP) to investigate this issue. Specifically, RSRN-SP uses spikes to encode information, and incorporate multiple plasticities including reward-modulated spike timing-dependent plasticity (R-STDP), homeostatic plasticity, and structural plasticity. On the one hand, combined with homeostatic plasticity, R-STDP is presented to guide the updating of synaptic weights. On the other hand, structural plasticity is utilized to simulate the growth and pruning of synaptic connections. Results and discussion Extensive experiments for sequential learning tasks are conducted to demonstrate the representational ability of the RSRN-SP, including counting task, motion prediction, and motion generation. Furthermore, the simulations also indicate that the characteristics arose from the RSRN-SP are consistent with biological observations.
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Affiliation(s)
- Ye Yuan
- School of Health Science and Engineering, Institute of Machine Intelligence, University of Shanghai for Science and Technology, Shanghai, China
| | - Yongtong Zhu
- School of Health Science and Engineering, Institute of Machine Intelligence, University of Shanghai for Science and Technology, Shanghai, China
| | - Jiaqi Wang
- School of Health Science and Engineering, Institute of Machine Intelligence, University of Shanghai for Science and Technology, Shanghai, China
| | - Ruoshi Li
- School of Health Science and Engineering, Institute of Machine Intelligence, University of Shanghai for Science and Technology, Shanghai, China
| | - Xin Xu
- School of Health Science and Engineering, Institute of Machine Intelligence, University of Shanghai for Science and Technology, Shanghai, China
| | - Tao Fang
- Automation of Department, Shanghai Jiao Tong University, Shanghai, China
| | - Hong Huo
- Automation of Department, Shanghai Jiao Tong University, Shanghai, China
| | - Lihong Wan
- Origin Dynamics Intelligent Robot Co., Ltd., Zhengzhou, China
| | - Qingdu Li
- School of Health Science and Engineering, Institute of Machine Intelligence, University of Shanghai for Science and Technology, Shanghai, China
| | - Na Liu
- School of Health Science and Engineering, Institute of Machine Intelligence, University of Shanghai for Science and Technology, Shanghai, China
| | - Shiyan Yang
- Eco-Environmental Protection Institution, Shanghai Academy of Agricultural Sciences, Shanghai, China
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26
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Jain A, Nakahata Y, Watabe T, Rusina P, South K, Adachi K, Yan L, Simorowski N, Furukawa H, Yasuda R. Dendritic, delayed, and stochastic CaMKII activation underlies behavioral time scale plasticity in CA1 synapses. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.01.549180. [PMID: 37577549 PMCID: PMC10418109 DOI: 10.1101/2023.08.01.549180] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Behavioral time scale plasticity (BTSP), is a form of non-Hebbian plasticity induced by integrating pre- and postsynaptic components separated by behavioral time scale (seconds). BTSP in the hippocampal CA1 neurons underlies place cell formation. However, the molecular mechanisms underlying this behavioral time scale (eligibility trace) and synapse specificity are unknown. CaMKII can be activated in a synapse-specific manner and remain active for a few seconds, making it a compelling candidate for the eligibility trace during BTSP. Here, we show that BTSP can be induced in a single dendritic spine using 2-photon glutamate uncaging paired with postsynaptic current injection temporally separated by behavioral time scale. Using an improved CaMKII sensor, we saw no detectable CaMKII activation during this BTSP induction. Instead, we observed a dendritic, delayed, and stochastic CaMKII activation (DDSC) associated with Ca 2+ influx and plateau 20-40 s after BTSP induction. DDSC requires both pre-and postsynaptic activity, suggesting that CaMKII can integrate these two signals. Also, optogenetically blocking CaMKII 30 s after the BTSP protocol inhibited synaptic potentiation, indicating that DDSC is an essential mechanism of BTSP. IP3-dependent intracellular Ca 2+ release facilitates both DDSC and BTSP. Thus, our study suggests that the non-synapse specific CaMKII activation provides an instructive signal with an extensive time window over tens of seconds during BTSP.
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27
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Guskjolen A, Cembrowski MS. Engram neurons: Encoding, consolidation, retrieval, and forgetting of memory. Mol Psychiatry 2023; 28:3207-3219. [PMID: 37369721 PMCID: PMC10618102 DOI: 10.1038/s41380-023-02137-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 06/02/2023] [Accepted: 06/13/2023] [Indexed: 06/29/2023]
Abstract
Tremendous strides have been made in our understanding of the neurobiological substrates of memory - the so-called memory "engram". Here, we integrate recent progress in the engram field to illustrate how engram neurons transform across the "lifespan" of a memory - from initial memory encoding, to consolidation and retrieval, and ultimately to forgetting. To do so, we first describe how cell-intrinsic properties shape the initial emergence of the engram at memory encoding. Second, we highlight how these encoding neurons preferentially participate in synaptic- and systems-level consolidation of memory. Third, we describe how these changes during encoding and consolidation guide neural reactivation during retrieval, and facilitate memory recall. Fourth, we describe neurobiological mechanisms of forgetting, and how these mechanisms can counteract engram properties established during memory encoding, consolidation, and retrieval. Motivated by recent experimental results across these four sections, we conclude by proposing some conceptual extensions to the traditional view of the engram, including broadening the view of cell-type participation within engrams and across memory stages. In collection, our review synthesizes general principles of the engram across memory stages, and describes future avenues to further understand the dynamic engram.
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Affiliation(s)
- Axel Guskjolen
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, BC, Canada.
| | - Mark S Cembrowski
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, BC, Canada.
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada.
- School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada.
- Department of Mathematics, University of British Columbia, Vancouver, BC, Canada.
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28
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Jeon I, Kim T. Distinctive properties of biological neural networks and recent advances in bottom-up approaches toward a better biologically plausible neural network. Front Comput Neurosci 2023; 17:1092185. [PMID: 37449083 PMCID: PMC10336230 DOI: 10.3389/fncom.2023.1092185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 06/12/2023] [Indexed: 07/18/2023] Open
Abstract
Although it may appear infeasible and impractical, building artificial intelligence (AI) using a bottom-up approach based on the understanding of neuroscience is straightforward. The lack of a generalized governing principle for biological neural networks (BNNs) forces us to address this problem by converting piecemeal information on the diverse features of neurons, synapses, and neural circuits into AI. In this review, we described recent attempts to build a biologically plausible neural network by following neuroscientifically similar strategies of neural network optimization or by implanting the outcome of the optimization, such as the properties of single computational units and the characteristics of the network architecture. In addition, we proposed a formalism of the relationship between the set of objectives that neural networks attempt to achieve, and neural network classes categorized by how closely their architectural features resemble those of BNN. This formalism is expected to define the potential roles of top-down and bottom-up approaches for building a biologically plausible neural network and offer a map helping the navigation of the gap between neuroscience and AI engineering.
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Affiliation(s)
| | - Taegon Kim
- Brain Science Institute, Korea Institute of Science and Technology, Seoul, Republic of Korea
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29
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Andrade-Talavera Y, Fisahn A, Rodríguez-Moreno A. Timing to be precise? An overview of spike timing-dependent plasticity, brain rhythmicity, and glial cells interplay within neuronal circuits. Mol Psychiatry 2023; 28:2177-2188. [PMID: 36991134 PMCID: PMC10611582 DOI: 10.1038/s41380-023-02027-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 02/27/2023] [Accepted: 03/01/2023] [Indexed: 03/31/2023]
Abstract
In the mammalian brain information processing and storage rely on the complex coding and decoding events performed by neuronal networks. These actions are based on the computational ability of neurons and their functional engagement in neuronal assemblies where precise timing of action potential firing is crucial. Neuronal circuits manage a myriad of spatially and temporally overlapping inputs to compute specific outputs that are proposed to underly memory traces formation, sensory perception, and cognitive behaviors. Spike-timing-dependent plasticity (STDP) and electrical brain rhythms are suggested to underlie such functions while the physiological evidence of assembly structures and mechanisms driving both processes continues to be scarce. Here, we review foundational and current evidence on timing precision and cooperative neuronal electrical activity driving STDP and brain rhythms, their interactions, and the emerging role of glial cells in such processes. We also provide an overview of their cognitive correlates and discuss current limitations and controversies, future perspectives on experimental approaches, and their application in humans.
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Affiliation(s)
- Yuniesky Andrade-Talavera
- Laboratory of Cellular Neuroscience and Plasticity, Department of Physiology, Anatomy and Cell Biology, Universidad Pablo de Olavide, ES-41013, Seville, Spain.
| | - André Fisahn
- Department of Biosciences and Nutrition and Department of Women's and Children's Health, Karolinska Institutet, 171 77, Stockholm, Sweden
| | - Antonio Rodríguez-Moreno
- Laboratory of Cellular Neuroscience and Plasticity, Department of Physiology, Anatomy and Cell Biology, Universidad Pablo de Olavide, ES-41013, Seville, Spain.
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30
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Andrade-Talavera Y, Pérez-Rodríguez M, Prius-Mengual J, Rodríguez-Moreno A. Neuronal and astrocyte determinants of critical periods of plasticity. Trends Neurosci 2023:S0166-2236(23)00105-4. [PMID: 37202300 DOI: 10.1016/j.tins.2023.04.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 03/20/2023] [Accepted: 04/24/2023] [Indexed: 05/20/2023]
Abstract
Windows of plasticity allow environmental experiences to produce intense activity-dependent changes during postnatal development. The reordering and refinement of neural connections occurs during these periods, significantly influencing the formation of brain circuits and physiological processes in adults. Recent advances have shed light on factors that determine the onset and duration of sensitive and critical periods of plasticity. Although GABAergic inhibition has classically been implicated in closing windows of plasticity, astrocytes and adenosinergic inhibition have also emerged more recently as key determinants of the duration of these periods of plasticity. Here, we review novel aspects of the involvement of GABAergic inhibition, the possible role of presynaptic NMDARs, and the emerging roles of astrocytes and adenosinergic inhibition in determining the duration of windows of plasticity in different brain regions.
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Affiliation(s)
- Yuniesky Andrade-Talavera
- Laboratory of Cellular Neuroscience and Plasticity, Department of Physiology, Anatomy and Cell Biology, Universidad Pablo de Olavide, ES-41013 Seville, Spain
| | - Mikel Pérez-Rodríguez
- Laboratory of Cellular Neuroscience and Plasticity, Department of Physiology, Anatomy and Cell Biology, Universidad Pablo de Olavide, ES-41013 Seville, Spain
| | - José Prius-Mengual
- Laboratory of Cellular Neuroscience and Plasticity, Department of Physiology, Anatomy and Cell Biology, Universidad Pablo de Olavide, ES-41013 Seville, Spain
| | - Antonio Rodríguez-Moreno
- Laboratory of Cellular Neuroscience and Plasticity, Department of Physiology, Anatomy and Cell Biology, Universidad Pablo de Olavide, ES-41013 Seville, Spain.
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Ziesel D, Nowakowska M, Scheruebel S, Kornmueller K, Schäfer U, Schindl R, Baumgartner C, Üçal M, Rienmüller T. Electrical stimulation methods and protocols for the treatment of traumatic brain injury: a critical review of preclinical research. J Neuroeng Rehabil 2023; 20:51. [PMID: 37098582 PMCID: PMC10131365 DOI: 10.1186/s12984-023-01159-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 03/13/2023] [Indexed: 04/27/2023] Open
Abstract
BACKGROUND Traumatic brain injury (TBI) is a leading cause of disabilities resulting from cognitive and neurological deficits, as well as psychological disorders. Only recently, preclinical research on electrical stimulation methods as a potential treatment of TBI sequelae has gained more traction. However, the underlying mechanisms of the anticipated improvements induced by these methods are still not fully understood. It remains unclear in which stage after TBI they are best applied to optimize the therapeutic outcome, preferably with persisting effects. Studies with animal models address these questions and investigate beneficial long- and short-term changes mediated by these novel modalities. METHODS In this review, we present the state-of-the-art in preclinical research on electrical stimulation methods used to treat TBI sequelae. We analyze publications on the most commonly used electrical stimulation methods, namely transcranial magnetic stimulation (TMS), transcranial direct current stimulation (tDCS), deep brain stimulation (DBS) and vagus nerve stimulation (VNS), that aim to treat disabilities caused by TBI. We discuss applied stimulation parameters, such as the amplitude, frequency, and length of stimulation, as well as stimulation time frames, specifically the onset of stimulation, how often stimulation sessions were repeated and the total length of the treatment. These parameters are then analyzed in the context of injury severity, the disability under investigation and the stimulated location, and the resulting therapeutic effects are compared. We provide a comprehensive and critical review and discuss directions for future research. RESULTS AND CONCLUSION: We find that the parameters used in studies on each of these stimulation methods vary widely, making it difficult to draw direct comparisons between stimulation protocols and therapeutic outcome. Persisting beneficial effects and adverse consequences of electrical simulation are rarely investigated, leaving many questions about their suitability for clinical applications. Nevertheless, we conclude that the stimulation methods discussed here show promising results that could be further supported by additional research in this field.
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Affiliation(s)
- D Ziesel
- Institute of Health Care Engineering with European Testing Center of Medical Devices, Graz University of Technology, Graz, Austria
| | - M Nowakowska
- Research Unit of Experimental Neurotraumatology, Department of Neurosurgery, Medical University of Graz, Graz, Austria
| | - S Scheruebel
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging, Biophysics Division, Medical University of Graz, Graz, Austria
| | - K Kornmueller
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging, Biophysics Division, Medical University of Graz, Graz, Austria
| | - U Schäfer
- Research Unit of Experimental Neurotraumatology, Department of Neurosurgery, Medical University of Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
| | - R Schindl
- Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging, Biophysics Division, Medical University of Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
| | - C Baumgartner
- Institute of Health Care Engineering with European Testing Center of Medical Devices, Graz University of Technology, Graz, Austria
- BioTechMed-Graz, Graz, Austria
| | - M Üçal
- Research Unit of Experimental Neurotraumatology, Department of Neurosurgery, Medical University of Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
| | - T Rienmüller
- Institute of Health Care Engineering with European Testing Center of Medical Devices, Graz University of Technology, Graz, Austria.
- BioTechMed-Graz, Graz, Austria.
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32
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Zeng J, Li X, Zhang R, Lv M, Wang Y, Tan K, Xia X, Wan J, Jing M, Zhang X, Li Y, Yang Y, Wang L, Chu J, Li Y, Li Y. Local 5-HT signaling bi-directionally regulates the coincidence time window for associative learning. Neuron 2023; 111:1118-1135.e5. [PMID: 36706757 PMCID: PMC11152601 DOI: 10.1016/j.neuron.2022.12.034] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 10/03/2022] [Accepted: 12/30/2022] [Indexed: 01/27/2023]
Abstract
The coincidence between conditioned stimulus (CS) and unconditioned stimulus (US) is essential for associative learning; however, the mechanism regulating the duration of this temporal window remains unclear. Here, we found that serotonin (5-HT) bi-directionally regulates the coincidence time window of olfactory learning in Drosophila and affects synaptic plasticity of Kenyon cells (KCs) in the mushroom body (MB). Utilizing GPCR-activation-based (GRAB) neurotransmitter sensors, we found that KC-released acetylcholine (ACh) activates a serotonergic dorsal paired medial (DPM) neuron, which in turn provides inhibitory feedback to KCs. Physiological stimuli induce spatially heterogeneous 5-HT signals, which proportionally gate the intrinsic coincidence time windows of different MB compartments. Artificially reducing or increasing the DPM neuron-released 5-HT shortens or prolongs the coincidence window, respectively. In a sequential trace conditioning paradigm, this serotonergic neuromodulation helps to bridge the CS-US temporal gap. Altogether, we report a model circuitry for perceiving the temporal coincidence and determining the causal relationship between environmental events.
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Affiliation(s)
- Jianzhi Zeng
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China; Institute of Molecular Physiology, Shenzhen Bay Laboratory, Shenzhen 518132, China; PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China; Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China; National Biomedical Imaging Center, Peking University, Beijing 100871, China; Hefei National Laboratory for Physical Sciences at the Microscale, CAS Key Laboratory of Brain Function and Disease, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, Anhui, China.
| | - Xuelin Li
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China; PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China; National Biomedical Imaging Center, Peking University, Beijing 100871, China
| | - Renzimo Zhang
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China; PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China; Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China; National Biomedical Imaging Center, Peking University, Beijing 100871, China; Yuanpei College, Peking University, Beijing 100871, China
| | - Mingyue Lv
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China; PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China; National Biomedical Imaging Center, Peking University, Beijing 100871, China
| | - Yipan Wang
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China; PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China; National Biomedical Imaging Center, Peking University, Beijing 100871, China
| | - Ke Tan
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China; PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China
| | - Xiju Xia
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China; PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China; National Biomedical Imaging Center, Peking University, Beijing 100871, China; PKU-THU-NIBS Joint Graduate Program, Beijing 100871, China
| | - Jinxia Wan
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China; PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China; National Biomedical Imaging Center, Peking University, Beijing 100871, China
| | - Miao Jing
- Chinese Institute for Brain Research, Beijing 102206, China
| | - Xiuning Zhang
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China; PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China; National Biomedical Imaging Center, Peking University, Beijing 100871, China
| | - Yu Li
- School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Yang Yang
- Institute of Biophysics, State Key Laboratory of Brain and Cognitive Science, Center for Excellence in Biomacromolecules, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Liang Wang
- Guangdong Provincial Key Laboratory of Biomedical Optical Imaging Technology & Center for Biomedical Optics and Molecular Imaging & CAS Key Laboratory of Health Informatics, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Jun Chu
- Guangdong Provincial Key Laboratory of Biomedical Optical Imaging Technology & Center for Biomedical Optics and Molecular Imaging & CAS Key Laboratory of Health Informatics, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Yan Li
- Institute of Biophysics, State Key Laboratory of Brain and Cognitive Science, Center for Excellence in Biomacromolecules, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yulong Li
- State Key Laboratory of Membrane Biology, School of Life Sciences, Peking University, Beijing 100871, China; Institute of Molecular Physiology, Shenzhen Bay Laboratory, Shenzhen 518132, China; PKU-IDG/McGovern Institute for Brain Research, Beijing 100871, China; Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China; National Biomedical Imaging Center, Peking University, Beijing 100871, China; Yuanpei College, Peking University, Beijing 100871, China; PKU-THU-NIBS Joint Graduate Program, Beijing 100871, China; Chinese Institute for Brain Research, Beijing 102206, China.
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Sanchez-Garcia M, Chauhan T, Cottereau BR, Beyeler M. Efficient multi-scale representation of visual objects using a biologically plausible spike-latency code and winner-take-all inhibition. BIOLOGICAL CYBERNETICS 2023; 117:95-111. [PMID: 37004546 DOI: 10.1007/s00422-023-00956-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 02/10/2023] [Indexed: 05/05/2023]
Abstract
Deep neural networks have surpassed human performance in key visual challenges such as object recognition, but require a large amount of energy, computation, and memory. In contrast, spiking neural networks (SNNs) have the potential to improve both the efficiency and biological plausibility of object recognition systems. Here we present a SNN model that uses spike-latency coding and winner-take-all inhibition (WTA-I) to efficiently represent visual stimuli using multi-scale parallel processing. Mimicking neuronal response properties in early visual cortex, images were preprocessed with three different spatial frequency (SF) channels, before they were fed to a layer of spiking neurons whose synaptic weights were updated using spike-timing-dependent-plasticity. We investigate how the quality of the represented objects changes under different SF bands and WTA-I schemes. We demonstrate that a network of 200 spiking neurons tuned to three SFs can efficiently represent objects with as little as 15 spikes per neuron. Studying how core object recognition may be implemented using biologically plausible learning rules in SNNs may not only further our understanding of the brain, but also lead to novel and efficient artificial vision systems.
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Affiliation(s)
| | - Tushar Chauhan
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Boston, MA, USA
- CerCo CNRS UMR5549, Université de Toulouse III-Paul Sabatier, Toulouse, France
| | - Benoit R Cottereau
- CerCo CNRS UMR5549, Université de Toulouse III-Paul Sabatier, Toulouse, France
- IPAL, CNRS IRL 2955, Singapore, Singapore
| | - Michael Beyeler
- Department of Computer Science, University of California, Santa Barbara, CA, USA
- Department of Psychological & Brain Sciences, University of California, Santa Barbara, CA, USA
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Yamakou ME, Kuehn C. Combined effects of spike-timing-dependent plasticity and homeostatic structural plasticity on coherence resonance. Phys Rev E 2023; 107:044302. [PMID: 37198865 DOI: 10.1103/physreve.107.044302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 03/23/2023] [Indexed: 05/19/2023]
Abstract
Efficient processing and transfer of information in neurons have been linked to noise-induced resonance phenomena such as coherence resonance (CR), and adaptive rules in neural networks have been mostly linked to two prevalent mechanisms: spike-timing-dependent plasticity (STDP) and homeostatic structural plasticity (HSP). Thus this paper investigates CR in small-world and random adaptive networks of Hodgkin-Huxley neurons driven by STDP and HSP. Our numerical study indicates that the degree of CR strongly depends, and in different ways, on the adjusting rate parameter P, which controls STDP, on the characteristic rewiring frequency parameter F, which controls HSP, and on the parameters of the network topology. In particular, we found two robust behaviors. (i) Decreasing P (which enhances the weakening effect of STDP on synaptic weights) and decreasing F (which slows down the swapping rate of synapses between neurons) always leads to higher degrees of CR in small-world and random networks, provided that the synaptic time delay parameter τ_{c} has some appropriate values. (ii) Increasing the synaptic time delay τ_{c} induces multiple CR (MCR)-the occurrence of multiple peaks in the degree of coherence as τ_{c} changes-in small-world and random networks, with MCR becoming more pronounced at smaller values of P and F. Our results imply that STDP and HSP can jointly play an essential role in enhancing the time precision of firing necessary for optimal information processing and transfer in neural systems and could thus have applications in designing networks of noisy artificial neural circuits engineered to use CR to optimize information processing and transfer.
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Affiliation(s)
- Marius E Yamakou
- Department of Data Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Cauerstr. 11, 91058 Erlangen, Germany
- Max-Planck-Institut für Mathematik in den Naturwissenschaften, Inselstr. 22, 04103 Leipzig, Germany
| | - Christian Kuehn
- Faculty of Mathematics, Technical University of Munich, Boltzmannstrasse 3, 85748 Garching bei München, Germany
- Complexity Science Hub Vienna, Josefstädter Strasse 39, 1080 Vienna, Austria
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Fang C, Aronov D, Abbott LF, Mackevicius EL. Neural learning rules for generating flexible predictions and computing the successor representation. eLife 2023; 12:e80680. [PMID: 36928104 PMCID: PMC10019889 DOI: 10.7554/elife.80680] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 10/26/2022] [Indexed: 03/18/2023] Open
Abstract
The predictive nature of the hippocampus is thought to be useful for memory-guided cognitive behaviors. Inspired by the reinforcement learning literature, this notion has been formalized as a predictive map called the successor representation (SR). The SR captures a number of observations about hippocampal activity. However, the algorithm does not provide a neural mechanism for how such representations arise. Here, we show the dynamics of a recurrent neural network naturally calculate the SR when the synaptic weights match the transition probability matrix. Interestingly, the predictive horizon can be flexibly modulated simply by changing the network gain. We derive simple, biologically plausible learning rules to learn the SR in a recurrent network. We test our model with realistic inputs and match hippocampal data recorded during random foraging. Taken together, our results suggest that the SR is more accessible in neural circuits than previously thought and can support a broad range of cognitive functions.
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Affiliation(s)
- Ching Fang
- Zuckerman Institute, Department of Neuroscience, Columbia UniversityNew YorkUnited States
| | - Dmitriy Aronov
- Zuckerman Institute, Department of Neuroscience, Columbia UniversityNew YorkUnited States
| | - LF Abbott
- Zuckerman Institute, Department of Neuroscience, Columbia UniversityNew YorkUnited States
| | - Emily L Mackevicius
- Zuckerman Institute, Department of Neuroscience, Columbia UniversityNew YorkUnited States
- Basis Research InstituteNew YorkUnited States
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36
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Fok KL, Kaneko N, Tajali S, Masani K. Paired associative stimulation on the soleus H-Reflex using motor point and peripheral nerve stimulation. Neurosci Lett 2023; 797:137070. [PMID: 36641045 DOI: 10.1016/j.neulet.2023.137070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 12/28/2022] [Accepted: 01/10/2023] [Indexed: 01/13/2023]
Abstract
Paired associative stimulation (PAS) has been shown to modulate the corticospinal excitability via spike timing dependent plasticity (STDP). In this study, we aimed to suppress the spinal H-Reflex using PAS. We paired two stimulation modalities, i.e., peripheral nerve stimulation (PNS) and motor point stimulation (MPS). We used PNS to dominantly activate the Ia sensory axon, and we used MPS to dominantly activate the α-motoneuron cell body antidromically. Thus, we applied both PNS and MPS such that the α-motoneuron cell body was activated 5 ms before the activation of the Ia sensory axon ending at the Ia-α motoneuron synapse. If the spinal reflexes can be modulated by STDP, and a combination of MPS and PNS is timed appropriately, then the H-Reflex amplitude will decrease while no change in H-Reflex amplitude is expected for MPS or PNS only. To test this hypothesis, six young healthy participants (5M/1F: 26.8 ± 4.1 yrs) received one of the three following conditions on days separated by at least 24 hr: 1) PAS, 2) MPS only or 3) PNS only. The H-Reflex and M-wave recruitment curves of the soleus were measured immediately prior to, immediately after, 30 min and 60 min after the intervention. The normalized H-Reflex amplitudes were then compared across conditions and times using a two-way ANOVA (3 conditions × 4 times). No main effects of condition or time, or interaction effect were found. These results suggest that relying solely on STDP may be insufficient to inhibit the soleus H-Reflex.
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Affiliation(s)
- Kai Lon Fok
- Institute of Biomedical Engineering, University of Toronto, 164 College Street, Toronto, ON M5S 3G9, Canada; KITE, Toronto Rehabilitation Institute, University Health Network, 520 Sutherland Drive, Toronto, ON M4G 3V9, Canada
| | - Naotsugu Kaneko
- Institute of Biomedical Engineering, University of Toronto, 164 College Street, Toronto, ON M5S 3G9, Canada; KITE, Toronto Rehabilitation Institute, University Health Network, 520 Sutherland Drive, Toronto, ON M4G 3V9, Canada; Japan Society for the Promotion of Science, Tokyo 102-0083, Japan; Department of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo 153-8902, Japan
| | - Shirin Tajali
- KITE, Toronto Rehabilitation Institute, University Health Network, 520 Sutherland Drive, Toronto, ON M4G 3V9, Canada
| | - Kei Masani
- Institute of Biomedical Engineering, University of Toronto, 164 College Street, Toronto, ON M5S 3G9, Canada; KITE, Toronto Rehabilitation Institute, University Health Network, 520 Sutherland Drive, Toronto, ON M4G 3V9, Canada.
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37
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Gao Y. A computational model of learning flexible navigation in a maze by layout-conforming replay of place cells. Front Comput Neurosci 2023; 17:1053097. [PMID: 36846726 PMCID: PMC9947252 DOI: 10.3389/fncom.2023.1053097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Accepted: 01/16/2023] [Indexed: 02/11/2023] Open
Abstract
Recent experimental observations have shown that the reactivation of hippocampal place cells (PC) during sleep or wakeful immobility depicts trajectories that can go around barriers and can flexibly adapt to a changing maze layout. However, existing computational models of replay fall short of generating such layout-conforming replay, restricting their usage to simple environments, like linear tracks or open fields. In this paper, we propose a computational model that generates layout-conforming replay and explains how such replay drives the learning of flexible navigation in a maze. First, we propose a Hebbian-like rule to learn the inter-PC synaptic strength during exploration. Then we use a continuous attractor network (CAN) with feedback inhibition to model the interaction among place cells and hippocampal interneurons. The activity bump of place cells drifts along paths in the maze, which models layout-conforming replay. During replay in sleep, the synaptic strengths from place cells to striatal medium spiny neurons (MSN) are learned by a novel dopamine-modulated three-factor rule to store place-reward associations. During goal-directed navigation, the CAN periodically generates replay trajectories from the animal's location for path planning, and the trajectory leading to a maximal MSN activity is followed by the animal. We have implemented our model into a high-fidelity virtual rat in the MuJoCo physics simulator. Extensive experiments have demonstrated that its superior flexibility during navigation in a maze is due to a continuous re-learning of inter-PC and PC-MSN synaptic strength.
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Affiliation(s)
- Yuanxiang Gao
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China,CAS Key Laboratory of Theoretical Physics, Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing, China,*Correspondence: Yuanxiang Gao ✉
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38
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Wu X, Wang R, Wu Q, Liao C, Zhang J, Jiao H, Chen B, Wang S, Liu R. The effects of combined high-frequency repetitive transcranial magnetic stimulation and cervical nerve root magnetic stimulation on upper extremity motor recovery following stroke. Front Neurosci 2023; 17:1100464. [PMID: 36845428 PMCID: PMC9951778 DOI: 10.3389/fnins.2023.1100464] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 01/16/2023] [Indexed: 02/10/2023] Open
Abstract
Introduction Upper limb motor impairments after stroke cause patients partial or total loss of the capability of performing daily living, working, and social activities, which significantly affects the quality of life (QoL) of patients and brings a heavy burden to their families and society. As a non-invasive neuromodulation technique, transcranial magnetic stimulation (TMS) can act not only on the cerebral cortex, but also on peripheral nerves, nerve roots, and muscle tissues. Previous studies have shown that magnetic stimulation on the cerebral cortex and peripheral tissues has a positive effect on the recovery of upper limb motor function after stroke, however, few studies have reported the combination of the two. Objective This study was to investigate whether high frequency repetitive transcranial magnetic stimulation (HF-rTMS) combined with cervical nerve root magnetic stimulation more effectively ameliorates upper limb motor function in stroke patients. We hypothesized that the combination of the two can achieve a synergistic effect and further promotes functional recovery. Methods Sixty patients with stroke were randomly divided into four groups and received real or sham rTMS stimulation and cervical nerve root magnetic stimulation consecutively before other therapies, once daily over five fractions per week for a total of 15 times. We evaluated the upper limb motor function and activities of daily living of the patients at the time of pre-treatment, post-treatment, and 3-month follow up. Results All patients completed study procedures without any adverse effects. The upper limb motor function and activities of daily living improved in patients of each group were improved after treatment (post 1) and 3 months after treatment (post 2). Combination treatment was significantly better than single treatments alone or sham. Conclusion Both rTMS and cervical nerve root magnetic stimulation effectively promoted upper limb motor recovery in patients with stroke. The protocol combining the two is more beneficial for motor improvement and patients can easily tolerate it. Clinical trial registration https://www.chictr.org.cn/, identifier ChiCTR2100048558.
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Affiliation(s)
- Xiaofang Wu
- Department of Rehabilitation, Tangdu Hospital, Air Force Medical University, Xi’an, China,Graduate School of Xi’an Medical College, Xi’an, China
| | - Rui Wang
- Medical Department of Tangdu Hospital, Air Force Medical University, Xi’an, China
| | - Qunqiang Wu
- Department of Rehabilitation, Tangdu Hospital, Air Force Medical University, Xi’an, China
| | - Chunhua Liao
- Department of Rehabilitation, Tangdu Hospital, Air Force Medical University, Xi’an, China
| | - Jianshe Zhang
- Department of Rehabilitation, Tangdu Hospital, Air Force Medical University, Xi’an, China
| | - Huiduo Jiao
- Department of Rehabilitation, Tangdu Hospital, Air Force Medical University, Xi’an, China
| | - Baolin Chen
- Department of Rehabilitation, Tangdu Hospital, Air Force Medical University, Xi’an, China
| | - Shuyan Wang
- Department of Rehabilitation, Tangdu Hospital, Air Force Medical University, Xi’an, China
| | - Rui Liu
- Department of Rehabilitation, Tangdu Hospital, Air Force Medical University, Xi’an, China,*Correspondence: Rui Liu,
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39
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Li XW, Ren Y, Shi DQ, Qi L, Xu F, Xiao Y, Lau PM, Bi GQ. Biphasic Cholinergic Modulation of Reverberatory Activity in Neuronal Networks. Neurosci Bull 2023; 39:731-744. [PMID: 36670292 PMCID: PMC10170002 DOI: 10.1007/s12264-022-01012-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 09/04/2022] [Indexed: 01/22/2023] Open
Abstract
Acetylcholine (ACh) is an important neuromodulator in various cognitive functions. However, it is unclear how ACh influences neural circuit dynamics by altering cellular properties. Here, we investigated how ACh influences reverberatory activity in cultured neuronal networks. We found that ACh suppressed the occurrence of evoked reverberation at low to moderate doses, but to a much lesser extent at high doses. Moreover, high doses of ACh caused a longer duration of evoked reverberation, and a higher occurrence of spontaneous activity. With whole-cell recording from single neurons, we found that ACh inhibited excitatory postsynaptic currents (EPSCs) while elevating neuronal firing in a dose-dependent manner. Furthermore, all ACh-induced cellular and network changes were blocked by muscarinic, but not nicotinic receptor antagonists. With computational modeling, we found that simulated changes in EPSCs and the excitability of single cells mimicking the effects of ACh indeed modulated the evoked network reverberation similar to experimental observations. Thus, ACh modulates network dynamics in a biphasic fashion, probably by inhibiting excitatory synaptic transmission and facilitating neuronal excitability through muscarinic signaling pathways.
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Affiliation(s)
- Xiao-Wei Li
- CAS Key Laboratory of Brain Function and Disease, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230027, China
| | - Yi Ren
- CAS Key Laboratory of Brain Function and Disease, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230027, China
| | - Dong-Qing Shi
- CAS Key Laboratory of Brain Function and Disease, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230027, China
| | - Lei Qi
- CAS Key Laboratory of Brain Function and Disease, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230027, China
| | - Fang Xu
- CAS Key Laboratory of Brain Connectome and Manipulation, Interdisciplinary Center for Brain Information, the Brain Cognition and Brain Disease Institute (BCBDI), Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences; Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, 518055, China
| | - Yanyang Xiao
- CAS Key Laboratory of Brain Connectome and Manipulation, Interdisciplinary Center for Brain Information, the Brain Cognition and Brain Disease Institute (BCBDI), Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences; Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, 518055, China.
| | - Pak-Ming Lau
- CAS Key Laboratory of Brain Function and Disease, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230027, China. .,CAS Key Laboratory of Brain Connectome and Manipulation, Interdisciplinary Center for Brain Information, the Brain Cognition and Brain Disease Institute (BCBDI), Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences; Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, 518055, China.
| | - Guo-Qiang Bi
- CAS Key Laboratory of Brain Function and Disease, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230027, China.,CAS Key Laboratory of Brain Connectome and Manipulation, Interdisciplinary Center for Brain Information, the Brain Cognition and Brain Disease Institute (BCBDI), Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences; Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, 518055, China
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40
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Asp AJ, Chintaluru Y, Hillan S, Lujan JL. Targeted neuroplasticity in spatiotemporally patterned invasive neuromodulation therapies for improving clinical outcomes. Front Neuroinform 2023; 17:1150157. [PMID: 37035718 PMCID: PMC10080034 DOI: 10.3389/fninf.2023.1150157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 03/06/2023] [Indexed: 04/11/2023] Open
Affiliation(s)
- Anders J. Asp
- Mayo Clinic Graduate School of Biomedical Sciences, Mayo Clinic, Rochester, MN, United States
| | - Yaswanth Chintaluru
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, United States
- Department of Neurology and Neurosurgery, University of Colorado Anschutz School of Medicine, Aurora, CO, United States
| | - Sydney Hillan
- Mayo Clinic Graduate School of Biomedical Sciences, Mayo Clinic, Rochester, MN, United States
| | - J. Luis Lujan
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, United States
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, United States
- *Correspondence: J. Luis Lujan
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41
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Cline HT, Lau M, Hiramoto M. Activity-dependent Organization of Topographic Neural Circuits. Neuroscience 2023; 508:3-18. [PMID: 36470479 PMCID: PMC9839526 DOI: 10.1016/j.neuroscience.2022.11.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 11/25/2022] [Accepted: 11/28/2022] [Indexed: 12/12/2022]
Abstract
Sensory information in the brain is organized into spatial representations, including retinotopic, somatotopic, and tonotopic maps, as well as ocular dominance columns. The spatial representation of sensory inputs is thought to be a fundamental organizational principle that is important for information processing. Topographic maps are plastic throughout an animal's life, reflecting changes in development and aging of brain circuitry, changes in the periphery and sensory input, and changes in circuitry, for instance in response to experience and learning. Here, we review mechanisms underlying the role of activity in the development, stability and plasticity of topographic maps, focusing on recent work suggesting that the spatial information in the visual field, and the resulting spatiotemporal patterns of activity, provide instructive cues that organize visual projections.
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Affiliation(s)
- Hollis T Cline
- Department of Neuroscience and the Dorris Neuroscience Center, The Scripps Research Institute, La Jolla, CA, USA.
| | - Melissa Lau
- Department of Neuroscience and the Dorris Neuroscience Center, The Scripps Research Institute, La Jolla, CA, USA
| | - Masaki Hiramoto
- Department of Neuroscience and the Dorris Neuroscience Center, The Scripps Research Institute, La Jolla, CA, USA
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42
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Guo R, Vaughan DT, Rojo ALA, Huang YH. Sleep-mediated regulation of reward circuits: implications in substance use disorders. Neuropsychopharmacology 2023; 48:61-78. [PMID: 35710601 PMCID: PMC9700806 DOI: 10.1038/s41386-022-01356-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 05/22/2022] [Accepted: 05/27/2022] [Indexed: 12/11/2022]
Abstract
Our modern society suffers from both pervasive sleep loss and substance abuse-what may be the indications for sleep on substance use disorders (SUDs), and could sleep contribute to the individual variations in SUDs? Decades of research in sleep as well as in motivated behaviors have laid the foundation for us to begin to answer these questions. This review is intended to critically summarize the circuit, cellular, and molecular mechanisms by which sleep influences reward function, and to reveal critical challenges for future studies. The review also suggests that improving sleep quality may serve as complementary therapeutics for treating SUDs, and that formulating sleep metrics may be useful for predicting individual susceptibility to SUDs and other reward-associated psychiatric diseases.
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Affiliation(s)
- Rong Guo
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, 15219, USA
- Allen Institute, Seattle, WA, 98109, USA
| | - Dylan Thomas Vaughan
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, 15219, USA
- The Center for Neuroscience at the University of Pittsburgh, Pittsburgh, PA, USA
| | - Ana Lourdes Almeida Rojo
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, 15219, USA
- The Center for Neuroscience at the University of Pittsburgh, Pittsburgh, PA, USA
| | - Yanhua H Huang
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, 15219, USA.
- The Center for Neuroscience at the University of Pittsburgh, Pittsburgh, PA, USA.
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43
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Scott DN, Frank MJ. Adaptive control of synaptic plasticity integrates micro- and macroscopic network function. Neuropsychopharmacology 2023; 48:121-144. [PMID: 36038780 PMCID: PMC9700774 DOI: 10.1038/s41386-022-01374-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 06/23/2022] [Accepted: 06/24/2022] [Indexed: 11/09/2022]
Abstract
Synaptic plasticity configures interactions between neurons and is therefore likely to be a primary driver of behavioral learning and development. How this microscopic-macroscopic interaction occurs is poorly understood, as researchers frequently examine models within particular ranges of abstraction and scale. Computational neuroscience and machine learning models offer theoretically powerful analyses of plasticity in neural networks, but results are often siloed and only coarsely linked to biology. In this review, we examine connections between these areas, asking how network computations change as a function of diverse features of plasticity and vice versa. We review how plasticity can be controlled at synapses by calcium dynamics and neuromodulatory signals, the manifestation of these changes in networks, and their impacts in specialized circuits. We conclude that metaplasticity-defined broadly as the adaptive control of plasticity-forges connections across scales by governing what groups of synapses can and can't learn about, when, and to what ends. The metaplasticity we discuss acts by co-opting Hebbian mechanisms, shifting network properties, and routing activity within and across brain systems. Asking how these operations can go awry should also be useful for understanding pathology, which we address in the context of autism, schizophrenia and Parkinson's disease.
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Affiliation(s)
- Daniel N Scott
- Cognitive Linguistic, and Psychological Sciences, Brown University, Providence, RI, USA.
- Carney Institute for Brain Science, Brown University, Providence, RI, USA.
| | - Michael J Frank
- Cognitive Linguistic, and Psychological Sciences, Brown University, Providence, RI, USA.
- Carney Institute for Brain Science, Brown University, Providence, RI, USA.
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44
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Hirai H, Sakaba T, Hashimotodani Y. Subcortical glutamatergic inputs exhibit a Hebbian form of long-term potentiation in the dentate gyrus. Cell Rep 2022; 41:111871. [PMID: 36577371 DOI: 10.1016/j.celrep.2022.111871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 09/19/2022] [Accepted: 12/01/2022] [Indexed: 12/28/2022] Open
Abstract
The hippocampus receives glutamatergic and GABAergic inputs from subcortical regions. Despite the important roles of these subcortical inputs in the regulation of hippocampal circuit, it has not been explored whether associative activation of the subcorticohippocampal pathway induces Hebbian plasticity of subcortical inputs. Here, we demonstrate that the hypothalamic supramammillary nucleus (SuM) to the dentate granule cell (GC) synapses, which co-release glutamate and GABA, undergo associative long-term potentiation (LTP) of glutamatergic, but not GABAergic, co-transmission. This LTP is induced by pairing of SuM inputs with GC spikes. We found that this Hebbian LTP is input-specific, requires NMDA receptors and CaMKII activation, and is expressed postsynaptically. By the net increase in excitatory drive of SuM inputs following LTP induction, associative inputs of SuM and the perforant path effectively discharge GCs. Our results highlight the important role of associative plasticity at SuM-GC synapses in the regulation of dentate gyrus activity and for the encoding of SuM-related information.
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Affiliation(s)
- Himawari Hirai
- Graduate School of Brain Science, Doshisha University, Kyoto 610-0394, Japan
| | - Takeshi Sakaba
- Graduate School of Brain Science, Doshisha University, Kyoto 610-0394, Japan
| | - Yuki Hashimotodani
- Graduate School of Brain Science, Doshisha University, Kyoto 610-0394, Japan.
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45
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Blumberg MS, Dooley JC, Tiriac A. Sleep, plasticity, and sensory neurodevelopment. Neuron 2022; 110:3230-3242. [PMID: 36084653 PMCID: PMC9588561 DOI: 10.1016/j.neuron.2022.08.005] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 07/04/2022] [Accepted: 08/11/2022] [Indexed: 11/18/2022]
Abstract
A defining feature of early infancy is the immense neural plasticity that enables animals to develop a brain that is functionally integrated with a growing body. Early infancy is also defined as a period dominated by sleep. Here, we describe three conceptual frameworks that vary in terms of whether and how they incorporate sleep as a factor in the activity-dependent development of sensory and sensorimotor systems. The most widely accepted framework is exemplified by the visual system where retinal waves seemingly occur independent of sleep-wake states. An alternative framework is exemplified by the sensorimotor system where sensory feedback from sleep-specific movements activates the brain. We prefer a third framework that encompasses the first two but also captures the diverse ways in which sleep modulates activity-dependent development throughout the nervous system. Appreciation of the third framework will spur progress toward a more comprehensive and cohesive understanding of both typical and atypical neurodevelopment.
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Affiliation(s)
- Mark S Blumberg
- Department of Psychological & Brain Sciences, University of Iowa, Iowa City, IA 52242, USA; Iowa Neuroscience Institute, University of Iowa, Iowa City, IA 52242, USA.
| | - James C Dooley
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA.
| | - Alexandre Tiriac
- Department of Biological Sciences, Vanderbilt University, Nashville, TN 37235, USA.
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46
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Chauhan K, Khaledi-Nasab A, Neiman AB, Tass PA. Dynamics of phase oscillator networks with synaptic weight and structural plasticity. Sci Rep 2022; 12:15003. [PMID: 36056151 PMCID: PMC9440105 DOI: 10.1038/s41598-022-19417-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 08/29/2022] [Indexed: 11/08/2022] Open
Abstract
We study the dynamics of Kuramoto oscillator networks with two distinct adaptation processes, one varying the coupling strengths and the other altering the network structure. Such systems model certain networks of oscillatory neurons where the neuronal dynamics, synaptic weights, and network structure interact with and shape each other. We model synaptic weight adaptation with spike-timing-dependent plasticity (STDP) that runs on a longer time scale than neuronal spiking. Structural changes that include addition and elimination of contacts occur at yet a longer time scale than the weight adaptations. First, we study the steady-state dynamics of Kuramoto networks that are bistable and can settle in synchronized or desynchronized states. To compare the impact of adding structural plasticity, we contrast the network with only STDP to one with a combination of STDP and structural plasticity. We show that the inclusion of structural plasticity optimizes the synchronized state of a network by allowing for synchronization with fewer links than a network with STDP alone. With non-identical units in the network, the addition of structural plasticity leads to the emergence of correlations between the oscillators' natural frequencies and node degrees. In the desynchronized regime, the structural plasticity decreases the number of contacts, leading to a sparse network. In this way, adding structural plasticity strengthens both synchronized and desynchronized states of a network. Second, we use desynchronizing coordinated reset stimulation and synchronizing periodic stimulation to induce desynchronized and synchronized states, respectively. Our findings indicate that a network with a combination of STDP and structural plasticity may require stronger and longer stimulation to switch between the states than a network with STDP only.
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Affiliation(s)
- Kanishk Chauhan
- Department of Physics and Astronomy, Ohio University, Athens, OH, 45701, USA.
- Neuroscience Program, Ohio University, Athens, OH, 45701, USA.
| | - Ali Khaledi-Nasab
- Department of Neurosurgery, Stanford University, Stanford, CA, 94305, USA
| | - Alexander B Neiman
- Department of Physics and Astronomy, Ohio University, Athens, OH, 45701, USA
- Neuroscience Program, Ohio University, Athens, OH, 45701, USA
| | - Peter A Tass
- Department of Neurosurgery, Stanford University, Stanford, CA, 94305, USA
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47
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Sakai Y, Sakai Y, Abe Y, Narumoto J, Tanaka SC. Memory trace imbalance in reinforcement and punishment systems can reinforce implicit choices leading to obsessive-compulsive behavior. Cell Rep 2022; 40:111275. [PMID: 36044850 DOI: 10.1016/j.celrep.2022.111275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 06/09/2022] [Accepted: 08/05/2022] [Indexed: 11/03/2022] Open
Abstract
We may view most of our daily activities as rational action selections; however, we sometimes reinforce maladaptive behaviors despite having explicit environmental knowledge. In this study, we model obsessive-compulsive disorder (OCD) symptoms as implicitly learned maladaptive behaviors. Simulations in the reinforcement learning framework show that agents implicitly learn to respond to intrusive thoughts when the memory trace signal for past actions decays differently for positive and negative prediction errors. Moreover, this model extends our understanding of therapeutic effects of behavioral therapy in OCD. Using empirical data, we confirm that patients with OCD show extremely imbalanced traces, which are normalized by serotonin enhancers. We find that healthy participants also vary in their obsessive-compulsive tendencies, consistent with the degree of imbalanced traces. These behavioral characteristics can be generalized to variations in the healthy population beyond the spectrum of clinical phenotypes.
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Affiliation(s)
- Yuki Sakai
- ATR Brain Information Communication Research Laboratory Group, 2-2-2 Hikaridai Seika-Cho, Soraku-Gun, Kyoto 619-0288, Japan; Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, 465 Kajii-Cho, Kawaramachi-Hirokoji, Kamigyo-Ku, Kyoto 602-8566, Japan
| | - Yutaka Sakai
- Brain Science Institute, Tamagawa University, 6-1-1, Tamagawa-Gakuen, Machida, Tokyo 194-8610, Japan
| | - Yoshinari Abe
- Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, 465 Kajii-Cho, Kawaramachi-Hirokoji, Kamigyo-Ku, Kyoto 602-8566, Japan
| | - Jin Narumoto
- Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, 465 Kajii-Cho, Kawaramachi-Hirokoji, Kamigyo-Ku, Kyoto 602-8566, Japan
| | - Saori C Tanaka
- ATR Brain Information Communication Research Laboratory Group, 2-2-2 Hikaridai Seika-Cho, Soraku-Gun, Kyoto 619-0288, Japan; Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama-Cho, Ikoma, Nara 630-0192, Japan.
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48
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Xie B, Xiong T, Li W, Gao T, Zong J, Liu Y, Yu P. Perspective on Nanofluidic Memristors: from Mechanism to Application. Chem Asian J 2022; 17:e202200682. [PMID: 35994236 DOI: 10.1002/asia.202200682] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/19/2022] [Indexed: 11/11/2022]
Abstract
Nanofluidic memristors are memory resistors based on nanoconfined fluidic systems exhibiting history-dependent ion conductivity. Toward establishing powerful computing systems beyond the Harvard architecture, these ion-based neuromorphic devices attracted enormous research attention owing to the unique characteristics of ion-based conductors. However, the design of nanofluidic memristor is still at a primary state and a systematic guidance on the rational design of nanofluidic system is desperately required for the development of nanofluidic-based neuromorphic devices. Herein, we proposed a systematic review on the history, main mechanism and potential application of nanofluidic memristors in order to give a prospective view on the design principle of memristors based on nanofluidic systems. Furthermore, based on the present status of these devices, some fundamental challenges for this promising area were further discussed to show the possible application of these ion-based devices.
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Affiliation(s)
- Boyang Xie
- Chinese Academy of Sciences, Institute of Chemistry, No.2, 1st North Street, Zhongguancun, Beijing, China, 100190, Beijing, CHINA
| | - Tianyi Xiong
- Chinese Academy of Sciences, Institute of Chemistry, No.2, 1st North Street, Zhongguancun, Beijing, China, 100190, Beijing, CHINA
| | - Weiqi Li
- Chinese Academy of Sciences, Institute of Chemistry, No.2, 1st North Street Zhongguancun, Beijing, China, 100190, Beijing, CHINA
| | - Tienan Gao
- Chinese Academy of Sciences, Institute of Chemistry, No.2, 1st North Street Zhongguancun, Beijing, China, 100190, Beijing, CHINA
| | - Jianwei Zong
- Chinese Academy of Sciences, Institute of Chemistry, No.2, 1st North Street Zhongguancun, Beijing, 100190, Beijing, CHINA
| | - Ying Liu
- Chinese Academy of Sciences, Institute of Chemistry, No.2, 1st North Street Zhongguancun, Beijing, China, 100190, CHINA
| | - Ping Yu
- Chinese Academy of Sciences, Institute of Chemistry, North first street No. 2, zhonguancun, 100190, Beijing, CHINA
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49
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He A, Zhang C, Ke X, Yi Y, Yu Q, Zhang T, Yu H, Du H, Li H, Tian Q, Zhu LQ, Lu Y. VGLUT3 neurons in median raphe control the efficacy of spatial memory retrieval via ETV4 regulation of VGLUT3 transcription. SCIENCE CHINA. LIFE SCIENCES 2022; 65:1590-1607. [PMID: 35089530 DOI: 10.1007/s11427-021-2047-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 12/15/2021] [Indexed: 06/14/2023]
Abstract
The raphe nucleus is critical for feeding, rewarding and memory. However, how the heterogenous raphe neurons are molecularly and structurally organized to engage their divergent functions remains unknown. Here, we genetically target a subset of neurons expressing VGLUT3. VGLUT3 neurons control the efficacy of spatial memory retrieval by synapsing directly with parvalbumin-expressing GABA interneurons (PGIs) in the dentate gyrus. In a mouse model of Alzheimer's disease (AD mice), VGLUT3→PGIs synaptic transmission is impaired by ETV4 inhibition of VGLUT3 transcription. ETV4 binds to a promoter region of VGLUT3 and activates VGLUT3 transcription in VGLUT3 neurons. Strengthening VGLUT3→PGIs synaptic transmission by ETV4 activation of VGLUT3 transcription upscales the efficacy of spatial memory retrieval in AD mice. This study reports a novel circuit and molecular mechanism underlying the efficacy of spatial memory retrieval via ETV4 inhibition of VGLUT3 transcription and hence provides a promising target for therapeutic intervention of the disease progression.
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Affiliation(s)
- Aodi He
- Department of Physiology, School of Basic Medicine and Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
- Wuhan Center for Brain Science, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Chen Zhang
- Department of Physiology, School of Basic Medicine and Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
- Wuhan Center for Brain Science, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Xiao Ke
- Department of Physiology, School of Basic Medicine and Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
- Wuhan Center for Brain Science, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Yao Yi
- Department of Physiology, School of Basic Medicine and Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
- Wuhan Center for Brain Science, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Quntao Yu
- Department of Physiology, School of Basic Medicine and Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
- Wuhan Center for Brain Science, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Tongmei Zhang
- Department of Physiology, School of Basic Medicine and Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
- Wuhan Center for Brain Science, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Hongyan Yu
- Department of Physiology, School of Basic Medicine and Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
- Wuhan Center for Brain Science, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Huiyun Du
- Department of Physiology, School of Basic Medicine and Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
- Wuhan Center for Brain Science, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Hao Li
- Department of Physiology, School of Basic Medicine and Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
- Wuhan Center for Brain Science, Huazhong University of Science and Technology, Wuhan, 430030, China
- Department of Pathophysiology, School of Basic Medicine, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Qing Tian
- Department of Physiology, School of Basic Medicine and Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
- Wuhan Center for Brain Science, Huazhong University of Science and Technology, Wuhan, 430030, China
- Department of Pathophysiology, School of Basic Medicine, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Ling-Qiang Zhu
- Department of Physiology, School of Basic Medicine and Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
- Wuhan Center for Brain Science, Huazhong University of Science and Technology, Wuhan, 430030, China
- Department of Pathophysiology, School of Basic Medicine, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Youming Lu
- Department of Physiology, School of Basic Medicine and Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
- Wuhan Center for Brain Science, Huazhong University of Science and Technology, Wuhan, 430030, China.
- Department of Pathophysiology, School of Basic Medicine, Huazhong University of Science and Technology, Wuhan, 430030, China.
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50
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Li H, Namburi P, Olson JM, Borio M, Lemieux ME, Beyeler A, Calhoon GG, Hitora-Imamura N, Coley AA, Libster A, Bal A, Jin X, Wang H, Jia C, Choudhury SR, Shi X, Felix-Ortiz AC, de la Fuente V, Barth VP, King HO, Izadmehr EM, Revanna JS, Batra K, Fischer KB, Keyes LR, Padilla-Coreano N, Siciliano CA, McCullough KM, Wichmann R, Ressler KJ, Fiete IR, Zhang F, Li Y, Tye KM. Neurotensin orchestrates valence assignment in the amygdala. Nature 2022; 608:586-592. [PMID: 35859170 PMCID: PMC9583860 DOI: 10.1038/s41586-022-04964-y] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2018] [Accepted: 06/10/2022] [Indexed: 02/03/2023]
Abstract
The ability to associate temporally segregated information and assign positive or negative valence to environmental cues is paramount for survival. Studies have shown that different projections from the basolateral amygdala (BLA) are potentiated following reward or punishment learning1-7. However, we do not yet understand how valence-specific information is routed to the BLA neurons with the appropriate downstream projections, nor do we understand how to reconcile the sub-second timescales of synaptic plasticity8-11 with the longer timescales separating the predictive cues from their outcomes. Here we demonstrate that neurotensin (NT)-expressing neurons in the paraventricular nucleus of the thalamus (PVT) projecting to the BLA (PVT-BLA:NT) mediate valence assignment by exerting NT concentration-dependent modulation in BLA during associative learning. We found that optogenetic activation of the PVT-BLA:NT projection promotes reward learning, whereas PVT-BLA projection-specific knockout of the NT gene (Nts) augments punishment learning. Using genetically encoded calcium and NT sensors, we further revealed that both calcium dynamics within the PVT-BLA:NT projection and NT concentrations in the BLA are enhanced after reward learning and reduced after punishment learning. Finally, we showed that CRISPR-mediated knockout of the Nts gene in the PVT-BLA pathway blunts BLA neural dynamics and attenuates the preference for active behavioural strategies to reward and punishment predictive cues. In sum, we have identified NT as a neuropeptide that signals valence in the BLA, and showed that NT is a critical neuromodulator that orchestrates positive and negative valence assignment in amygdala neurons by extending valence-specific plasticity to behaviourally relevant timescales.
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Affiliation(s)
- Hao Li
- Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Praneeth Namburi
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jacob M Olson
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- Neuroscience Program, Department of Psychology, Volen National Center for Complex Systems, Brandeis University, Waltham, MA, USA
| | - Matilde Borio
- Salk Institute for Biological Studies, La Jolla, CA, USA
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Mackenzie E Lemieux
- Salk Institute for Biological Studies, La Jolla, CA, USA
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Anna Beyeler
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- University of Bordeaux, Neurocentre Magendie, INSERM 1215, Bordeaux, France
| | - Gwendolyn G Calhoon
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- Neuroscience Program, Bates College, Lewiston, ME, USA
| | - Natsuko Hitora-Imamura
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Pharmacology, Graduate School of Pharmaceutical Sciences, Hokkaido University, Sapporo, Japan
- Graduate School of Pharmaceutical Sciences, Kumamoto University, Kumamoto, Japan
| | - Austin A Coley
- Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Avraham Libster
- Salk Institute for Biological Studies, La Jolla, CA, USA
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Aneesh Bal
- Salk Institute for Biological Studies, La Jolla, CA, USA
- Behavioral Neuroscience, Department of Psychology, Michigan State University, East Lansing, MI, USA
| | - Xin Jin
- Society of Fellows, Harvard University, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Huan Wang
- State Key Laboratory of Membrane Biology, Peking University School of Life Sciences, Peking-Tsinghua Center for Life Science, IDG/McGovern Institute for Brain Research at PKU, Beijing, China
| | - Caroline Jia
- Salk Institute for Biological Studies, La Jolla, CA, USA
- Neuroscience Graduate Program, University of California San Diego, La Jolla, CA, USA
| | | | - Xi Shi
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ada C Felix-Ortiz
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Verónica de la Fuente
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- Instituto de Fisiología, Biología Molecular y Neurociencias (IFIBYNE-UBA-CONICET), Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
- Departamento de Fisiología, Biología Molecular y Celular, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Vanessa P Barth
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Electrical and Computer Engineering, Technical University of Munich, Munich, Germany
| | - Hunter O King
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- Whitehead Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ehsan M Izadmehr
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jasmin S Revanna
- Salk Institute for Biological Studies, La Jolla, CA, USA
- Biological Sciences Graduate Program, University of California San Diego, La Jolla, CA, USA
| | - Kanha Batra
- Salk Institute for Biological Studies, La Jolla, CA, USA
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA
| | - Kyle B Fischer
- Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Laurel R Keyes
- Salk Institute for Biological Studies, La Jolla, CA, USA
| | | | - Cody A Siciliano
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- Vanderbilt Center for Addiction Research, Department of Pharmacology, Vanderbilt University, Nashville, TN, USA
| | - Kenneth M McCullough
- Division of Depression and Anxiety Disorders, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Romy Wichmann
- Salk Institute for Biological Studies, La Jolla, CA, USA
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kerry J Ressler
- Division of Depression and Anxiety Disorders, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Ila R Fiete
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Feng Zhang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Cambridge, MA, USA
| | - Yulong Li
- State Key Laboratory of Membrane Biology, Peking University School of Life Sciences, Peking-Tsinghua Center for Life Science, IDG/McGovern Institute for Brain Research at PKU, Beijing, China
| | - Kay M Tye
- Salk Institute for Biological Studies, La Jolla, CA, USA.
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Systems Neuroscience Laboratory and Howard Hughes Medical Institute, Salk Institute for Biological Studies, La Jolla, CA, USA.
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