1
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Yu D, Li T, Ding Q, Wu Y, Fu Z, Zhan X, Yang L, Jia Y. Maintenance of delay-period activity in working memory task is modulated by local network structure. PLoS Comput Biol 2024; 20:e1012415. [PMID: 39226309 PMCID: PMC11398668 DOI: 10.1371/journal.pcbi.1012415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Revised: 09/13/2024] [Accepted: 08/14/2024] [Indexed: 09/05/2024] Open
Abstract
Revealing the relationship between neural network structure and function is one central theme of neuroscience. In the context of working memory (WM), anatomical data suggested that the topological structure of microcircuits within WM gradient network may differ, and the impact of such structural heterogeneity on WM activity remains unknown. Here, we proposed a spiking neural network model that can replicate the fundamental characteristics of WM: delay-period neural activity involves association cortex but not sensory cortex. First, experimentally observed receptor expression gradient along the WM gradient network is reproduced by our network model. Second, by analyzing the correlation between different local structures and duration of WM activity, we demonstrated that small-worldness, excitation-inhibition balance, and cycle structures play crucial roles in sustaining WM-related activity. To elucidate the relationship between the structure and functionality of neural networks, structural circuit gradients in brain should also be subject to further measurement. Finally, combining anatomical data, we simulated the duration of WM activity across different brain regions, its maintenance relies on the interaction between local and distributed networks. Overall, network structural gradient and interaction between local and distributed networks are of great significance for WM.
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Affiliation(s)
- Dong Yu
- Institute of Biophysics, Central China Normal University, Wuhan, China
- College of Physical Science and Technology, Central China Normal University, Wuhan, China
| | - Tianyu Li
- Institute of Biophysics, Central China Normal University, Wuhan, China
- College of Physical Science and Technology, Central China Normal University, Wuhan, China
| | - Qianming Ding
- Institute of Biophysics, Central China Normal University, Wuhan, China
- College of Physical Science and Technology, Central China Normal University, Wuhan, China
| | - Yong Wu
- Institute of Biophysics, Central China Normal University, Wuhan, China
- College of Physical Science and Technology, Central China Normal University, Wuhan, China
| | - Ziying Fu
- Institute of Biophysics, Central China Normal University, Wuhan, China
- School of Life Sciences, Central China Normal University, Wuhan, China
| | - Xuan Zhan
- Institute of Biophysics, Central China Normal University, Wuhan, China
- College of Physical Science and Technology, Central China Normal University, Wuhan, China
| | - Lijian Yang
- Institute of Biophysics, Central China Normal University, Wuhan, China
- College of Physical Science and Technology, Central China Normal University, Wuhan, China
| | - Ya Jia
- Institute of Biophysics, Central China Normal University, Wuhan, China
- College of Physical Science and Technology, Central China Normal University, Wuhan, China
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2
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McFarlan AR, Gomez I, Chou CYC, Alcolado A, Costa RP, Sjöström PJ. The short-term plasticity of VIP interneurons in motor cortex. Front Synaptic Neurosci 2024; 16:1433977. [PMID: 39267890 PMCID: PMC11390561 DOI: 10.3389/fnsyn.2024.1433977] [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: 05/16/2024] [Accepted: 08/14/2024] [Indexed: 09/15/2024] Open
Abstract
Short-term plasticity is an important feature in the brain for shaping neural dynamics and for information processing. Short-term plasticity is known to depend on many factors including brain region, cortical layer, and cell type. Here we focus on vasoactive-intestinal peptide (VIP) interneurons (INs). VIP INs play a key disinhibitory role in cortical circuits by inhibiting other IN types, including Martinotti cells (MCs) and basket cells (BCs). Despite this prominent role, short-term plasticity at synapses to and from VIP INs is not well described. In this study, we therefore characterized the short-term plasticity at inputs and outputs of genetically targeted VIP INs in mouse motor cortex. To explore inhibitory to inhibitory (I → I) short-term plasticity at layer 2/3 (L2/3) VIP IN outputs onto L5 MCs and BCs, we relied on a combination of whole-cell recording, 2-photon microscopy, and optogenetics, which revealed that VIP IN→MC/BC synapses were consistently short-term depressing. To explore excitatory (E) → I short-term plasticity at inputs to VIP INs, we used extracellular stimulation. Surprisingly, unlike VIP IN outputs, E → VIP IN synapses exhibited heterogeneous short-term dynamics, which we attributed to the target VIP IN cell rather than the input. Computational modeling furthermore linked the diversity in short-term dynamics at VIP IN inputs to a wide variability in probability of release. Taken together, our findings highlight how short-term plasticity at VIP IN inputs and outputs is specific to synapse type. We propose that the broad diversity in short-term plasticity of VIP IN inputs forms a basis to code for a broad range of contrasting signal dynamics.
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Affiliation(s)
- Amanda R McFarlan
- Centre for Research in Neuroscience, Brain Repair, and Integrative Neuroscience Program, Department of Neurology and Neurosurgery, The Research Institute of the McGill University Health Centre, Montreal General Hospital, Montreal, QC, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada
| | - Isabella Gomez
- Centre for Research in Neuroscience, Brain Repair, and Integrative Neuroscience Program, Department of Neurology and Neurosurgery, The Research Institute of the McGill University Health Centre, Montreal General Hospital, Montreal, QC, Canada
| | - Christina Y C Chou
- Centre for Research in Neuroscience, Brain Repair, and Integrative Neuroscience Program, Department of Neurology and Neurosurgery, The Research Institute of the McGill University Health Centre, Montreal General Hospital, Montreal, QC, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada
| | | | - Rui Ponte Costa
- Centre for Neural Circuits and Behaviour, Department of Physiology, Anatomy and Genetics, Medical Sciences Division, University of Oxford, Oxford, United Kingdom
| | - P Jesper Sjöström
- Centre for Research in Neuroscience, Brain Repair, and Integrative Neuroscience Program, Department of Neurology and Neurosurgery, The Research Institute of the McGill University Health Centre, Montreal General Hospital, Montreal, QC, Canada
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3
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Weilenmann C, Ziogas AN, Zellweger T, Portner K, Mladenović M, Kaniselvan M, Moraitis T, Luisier M, Emboras A. Single neuromorphic memristor closely emulates multiple synaptic mechanisms for energy efficient neural networks. Nat Commun 2024; 15:6898. [PMID: 39138160 PMCID: PMC11322324 DOI: 10.1038/s41467-024-51093-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 07/27/2024] [Indexed: 08/15/2024] Open
Abstract
Biological neural networks do not only include long-term memory and weight multiplication capabilities, as commonly assumed in artificial neural networks, but also more complex functions such as short-term memory, short-term plasticity, and meta-plasticity - all collocated within each synapse. Here, we demonstrate memristive nano-devices based on SrTiO3 that inherently emulate all these synaptic functions. These memristors operate in a non-filamentary, low conductance regime, which enables stable and energy efficient operation. They can act as multi-functional hardware synapses in a class of bio-inspired deep neural networks (DNN) that make use of both long- and short-term synaptic dynamics and are capable of meta-learning or learning-to-learn. The resulting bio-inspired DNN is then trained to play the video game Atari Pong, a complex reinforcement learning task in a dynamic environment. Our analysis shows that the energy consumption of the DNN with multi-functional memristive synapses decreases by about two orders of magnitude as compared to a pure GPU implementation. Based on this finding, we infer that memristive devices with a better emulation of the synaptic functionalities do not only broaden the applicability of neuromorphic computing, but could also improve the performance and energy costs of certain artificial intelligence applications.
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Affiliation(s)
| | | | - Till Zellweger
- Integrated Systems Laboratory, ETH Zurich, Zurich, Switzerland
| | - Kevin Portner
- Integrated Systems Laboratory, ETH Zurich, Zurich, Switzerland
| | | | | | | | - Mathieu Luisier
- Integrated Systems Laboratory, ETH Zurich, Zurich, Switzerland
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4
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Ibañez S, Sengupta N, Luebke JI, Wimmer K, Weaver CM. Myelin dystrophy impairs signal transmission and working memory in a multiscale model of the aging prefrontal cortex. eLife 2024; 12:RP90964. [PMID: 39028036 PMCID: PMC11259433 DOI: 10.7554/elife.90964] [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] [Indexed: 07/20/2024] Open
Abstract
Normal aging leads to myelin alterations in the rhesus monkey dorsolateral prefrontal cortex (dlPFC), which are positively correlated with degree of cognitive impairment. It is hypothesized that remyelination with shorter and thinner myelin sheaths partially compensates for myelin degradation, but computational modeling has not yet explored these two phenomena together systematically. Here, we used a two-pronged modeling approach to determine how age-related myelin changes affect a core cognitive function: spatial working memory. First, we built a multicompartment pyramidal neuron model fit to monkey dlPFC empirical data, with an axon including myelinated segments having paranodes, juxtaparanodes, internodes, and tight junctions. This model was used to quantify conduction velocity (CV) changes and action potential (AP) failures after demyelination and subsequent remyelination. Next, we incorporated the single neuron results into a spiking neural network model of working memory. While complete remyelination nearly recovered axonal transmission and network function to unperturbed levels, our models predict that biologically plausible levels of myelin dystrophy, if uncompensated by other factors, can account for substantial working memory impairment with aging. The present computational study unites empirical data from ultrastructure up to behavior during normal aging, and has broader implications for many demyelinating conditions, such as multiple sclerosis or schizophrenia.
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Affiliation(s)
- Sara Ibañez
- Department of Anatomy & Neurobiology, Boston University Chobanian & Avedisian School of MedicineBostonUnited States
- Centre de Recerca Matemàtica, Edifici C, Campus BellaterraBellaterraSpain
- Departament de Matemàtiques, Universitat Autònoma de Barcelona, Edifici CBellaterraSpain
| | - Nilapratim Sengupta
- Department of Anatomy & Neurobiology, Boston University Chobanian & Avedisian School of MedicineBostonUnited States
- Department of Mathematics, Franklin and Marshall CollegeLancasterUnited States
| | - Jennifer I Luebke
- Department of Anatomy & Neurobiology, Boston University Chobanian & Avedisian School of MedicineBostonUnited States
| | - Klaus Wimmer
- Centre de Recerca Matemàtica, Edifici C, Campus BellaterraBellaterraSpain
- Departament de Matemàtiques, Universitat Autònoma de Barcelona, Edifici CBellaterraSpain
| | - Christina M Weaver
- Department of Mathematics, Franklin and Marshall CollegeLancasterUnited States
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5
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Zhou J, Gong L, Huang X, Mu C, Mi Y. The synaptic correlates of serial position effects in sequential working memory. Front Comput Neurosci 2024; 18:1430244. [PMID: 39077153 PMCID: PMC11284078 DOI: 10.3389/fncom.2024.1430244] [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: 05/09/2024] [Accepted: 06/26/2024] [Indexed: 07/31/2024] Open
Abstract
Sequential working memory (SWM), referring to the temporary storage and manipulation of information in order, plays a fundamental role in brain cognitive functions. The serial position effect refers to the phenomena that recall accuracy of an item is associated to the order of the item being presented. The neural mechanism underpinning the serial position effect remains unclear. The synaptic mechanism of working memory proposes that information is stored as hidden states in the form of facilitated neuronal synapse connections. Here, we build a continuous attractor neural network with synaptic short-term plasticity (STP) to explore the neural mechanism of the serial position effect. Using a delay recall task, our model reproduces the the experimental finding that as the maintenance period extends, the serial position effect transitions from the primacy to the recency effect. Using both numerical simulation and theoretical analysis, we show that the transition moment is determined by the parameters of STP and the interval between presented stimulus items. Our results highlight the pivotal role of STP in processing the order information in SWM.
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Affiliation(s)
- Jiaqi Zhou
- School of Medicine, Chongqing University, Chongqing, China
- College of Mathematics and Statistics, Chongqing University, Chongqing, China
- Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing, China
| | - Liping Gong
- School of Medicine, Chongqing University, Chongqing, China
- College of Mathematics and Statistics, Chongqing University, Chongqing, China
| | - Xiaodong Huang
- Department of Physics, South China University of Technology, Guangzhou, China
| | - Chunlai Mu
- School of Medicine, Chongqing University, Chongqing, China
- College of Mathematics and Statistics, Chongqing University, Chongqing, China
| | - Yuanyuan Mi
- Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing, China
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6
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Ross G, Radtke-Schuller S, Frohlich F. Ferret as a model system for studying the anatomy and function of the prefrontal cortex: A systematic review. Neurosci Biobehav Rev 2024; 162:105701. [PMID: 38718987 PMCID: PMC11162921 DOI: 10.1016/j.neubiorev.2024.105701] [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: 10/30/2023] [Revised: 04/12/2024] [Accepted: 05/01/2024] [Indexed: 05/19/2024]
Abstract
There is a lack of consensus on anatomical nomenclature, standards of documentation, and functional equivalence of the frontal cortex between species. There remains a major gap between human prefrontal function and interpretation of findings in the mouse brain that appears to lack several key prefrontal areas involved in cognition and psychiatric illnesses. The ferret is an emerging model organism that has gained traction as an intermediate model species for the study of top-down cognitive control and other higher-order brain functions. However, this research has yet to benefit from synthesis. Here, we provide a summary of all published research pertaining to the frontal and/or prefrontal cortex of the ferret across research scales. The targeted location within the ferret brain is summarized visually for each experiment, and the anatomical terminology used at time of publishing is compared to what would be the appropriate term to use presently. By doing so, we hope to improve clarity in the interpretation of both previous and future publications on the comparative study of frontal cortex.
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Affiliation(s)
- Grace Ross
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Carolina Center for Neurostimulation, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Neuroscience Center, University of North Carolina, Chapel Hill, NC, USA
| | - Susanne Radtke-Schuller
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Carolina Center for Neurostimulation, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Flavio Frohlich
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Carolina Center for Neurostimulation, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Neuroscience Center, University of North Carolina, Chapel Hill, NC, USA; Department of Cell Biology and Physiology, University of North Carolina, Chapel Hill, NC, USA; Department of Biomedical Engineering, University of North Carolina, Chapel Hill, NC, USA; Department of Neurology, University of North Carolina, Chapel Hill, NC, USA.
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7
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Jiang Y, Xu L, Cao Y, Meng F, Jiang S, Yang M, Zheng Z, Zhang Y, Yang L, Wang M, Sun G, Liu J, Li C, Cui M. Effects of Interleukin-19 overexpression in the medial prefrontal cortex on anxiety-related behaviors, BDNF expression and p38/JNK/ERK pathways. Brain Res Bull 2024; 212:110952. [PMID: 38636611 DOI: 10.1016/j.brainresbull.2024.110952] [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: 01/05/2024] [Revised: 03/27/2024] [Accepted: 04/14/2024] [Indexed: 04/20/2024]
Abstract
Anxiety is a prevalent mental illness known for its high incidence, comorbidity, and tendency to recur, posing significant societal and individual burdens. Studies have highlighted Interleukin-19 (IL-19) as having potential relevance in neuropsychiatric disorders. Our previous research revealed that IL-19 overexpression in colonies exacerbated anxiety-related behaviors induced by dextran sodium sulfate/stress. However, the precise role and molecular mechanisms of IL-19 in anxiety regulation remain uncertain. In this study, we initiated an acute restraint stress (ARS)-induced anxious mouse model and identified heightened expression of IL-19 and IL-20Rα in the medial prefrontal cortex (mPFC) of ARS mice. Notably, IL-19 and IL-20Rα were predominantly present in the excitatory pyramidal neurons of the mPFC under both basal and ARS conditions. Utilizing the adeno-associated virus (AAV) strategy, we demonstrated that IL-19 overexpression in the mPFC induced anxiety-related behaviors and elevated stress susceptibility. Additionally, we observed decreased protein levels of brain-derived neurotrophic factor (BDNF) and postsynaptic density protein 95 (PSD95) in the mPFC of IL-19 overexpression mice, accompanied by reduced phosphorylation of in the p38, JNK, and Erk signaling pathways. These findings emphasize the role of IL-19 in modulating anxiety-related behaviors within the mPFC and suggest its potential as a pathological gene and therapeutic target for anxiety.
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Affiliation(s)
- Yuting Jiang
- Department of Psychology, Binzhou Medical University Hospital, Binzhou, Shandong, China; Medical Research Center, Binzhou Medical University Hospital, Binzhou, Shandong, China
| | - Lihong Xu
- Department of Psychology, Binzhou Medical University Hospital, Binzhou, Shandong, China; Medical Research Center, Binzhou Medical University Hospital, Binzhou, Shandong, China
| | - Yifan Cao
- Department of Psychology, Binzhou Medical University Hospital, Binzhou, Shandong, China; Medical Research Center, Binzhou Medical University Hospital, Binzhou, Shandong, China
| | - Fantao Meng
- Department of Psychology, Binzhou Medical University Hospital, Binzhou, Shandong, China; Medical Research Center, Binzhou Medical University Hospital, Binzhou, Shandong, China
| | - Shujun Jiang
- Department of Physiology, Binzhou Medical University, Shandong, China
| | - Mengyu Yang
- Department of Psychology, Binzhou Medical University Hospital, Binzhou, Shandong, China; Medical Research Center, Binzhou Medical University Hospital, Binzhou, Shandong, China
| | - Ziteng Zheng
- Department of Psychology, Binzhou Medical University Hospital, Binzhou, Shandong, China; Medical Research Center, Binzhou Medical University Hospital, Binzhou, Shandong, China
| | - Yi Zhang
- Department of Psychology, Binzhou Medical University Hospital, Binzhou, Shandong, China; Medical Research Center, Binzhou Medical University Hospital, Binzhou, Shandong, China
| | - Lu Yang
- Department of Psychology, Binzhou Medical University Hospital, Binzhou, Shandong, China; Medical Research Center, Binzhou Medical University Hospital, Binzhou, Shandong, China
| | - Meiqin Wang
- Medical Research Center, Binzhou Medical University Hospital, Binzhou, Shandong, China; Department of Physiology, Binzhou Medical University, Shandong, China
| | - Guizhi Sun
- Department of Psychology, Binzhou Medical University Hospital, Binzhou, Shandong, China
| | - Jing Liu
- Department of Psychology, Binzhou Medical University Hospital, Binzhou, Shandong, China; Medical Research Center, Binzhou Medical University Hospital, Binzhou, Shandong, China.
| | - Chen Li
- Department of Psychology, Binzhou Medical University Hospital, Binzhou, Shandong, China; Medical Research Center, Binzhou Medical University Hospital, Binzhou, Shandong, China.
| | - Minghu Cui
- Department of Psychology, Binzhou Medical University Hospital, Binzhou, Shandong, China; Medical Research Center, Binzhou Medical University Hospital, Binzhou, Shandong, China.
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8
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Diesburg DA, Wessel JR, Jones SR. Biophysical Modeling of Frontocentral ERP Generation Links Circuit-Level Mechanisms of Action-Stopping to a Behavioral Race Model. J Neurosci 2024; 44:e2016232024. [PMID: 38561227 PMCID: PMC11097283 DOI: 10.1523/jneurosci.2016-23.2024] [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: 10/25/2023] [Revised: 02/09/2024] [Accepted: 03/08/2024] [Indexed: 04/04/2024] Open
Abstract
Human frontocentral event-related potentials (FC-ERPs) are ubiquitous neural correlates of cognition and control, but their generating multiscale mechanisms remain mostly unknown. We used the Human Neocortical Neurosolver's biophysical model of a canonical neocortical circuit under exogenous thalamic and cortical drive to simulate the cell and circuit mechanisms underpinning the P2, N2, and P3 features of the FC-ERP observed after Stop-Signals in the Stop-Signal task (SST; N = 234 humans, 137 female). We demonstrate that a sequence of simulated external thalamocortical and corticocortical drives can produce the FC-ERP, similar to what has been shown for primary sensory cortices. We used this model of the FC-ERP to examine likely circuit-mechanisms underlying FC-ERP features that distinguish between successful and failed action-stopping. We also tested their adherence to the predictions of the horse-race model of the SST, with specific hypotheses motivated by theoretical links between the P3 and Stop process. These simulations revealed that a difference in P3 onset between successful and failed Stops is most likely due to a later arrival of thalamocortical drive in failed Stops, rather than, for example, a difference in the effective strength of the input. In contrast, the same model predicted that early thalamocortical drives underpinning the P2 and N2 differed in both strength and timing across stopping accuracy conditions. Overall, this model generates novel testable predictions of the thalamocortical dynamics underlying FC-ERP generation during action-stopping. Moreover, it provides a detailed cellular and circuit-level interpretation that supports links between these macroscale signatures and predictions of the behavioral race model.
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Affiliation(s)
- Darcy A Diesburg
- Department of Neuroscience, Brown University, Providence, Rhode Island 02912
| | - Jan R Wessel
- Department of Psychological and Brain Sciences, University of Iowa, Iowa City, Iowa 52242
- Department of Neurology, Carver College of Medicine, University of Iowa Hospitals and Clinics, Iowa City, Iowa 52242
| | - Stephanie R Jones
- Department of Neuroscience, Brown University, Providence, Rhode Island 02912
- Center for Neurorestoration and Neurotechnology, Providence VA Medical Center, Providence, Rhode Island 02908
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9
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Tian Y, Murphy MJH, Steiner LA, Kalia SK, Hodaie M, Lozano AM, Hutchison WD, Popovic MR, Milosevic L, Lankarany M. Modeling Instantaneous Firing Rate of Deep Brain Stimulation Target Neuronal Ensembles in the Basal Ganglia and Thalamus. Neuromodulation 2024; 27:464-475. [PMID: 37140523 DOI: 10.1016/j.neurom.2023.03.012] [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: 11/13/2022] [Revised: 01/27/2023] [Accepted: 03/02/2023] [Indexed: 05/05/2023]
Abstract
OBJECTIVE Deep brain stimulation (DBS) is an effective treatment for movement disorders, including Parkinson disease and essential tremor. However, the underlying mechanisms of DBS remain elusive. Despite the capability of existing models in interpreting experimental data qualitatively, there are very few unified computational models that quantitatively capture the dynamics of the neuronal activity of varying stimulated nuclei-including subthalamic nucleus (STN), substantia nigra pars reticulata (SNr), and ventral intermediate nucleus (Vim)-across different DBS frequencies. MATERIALS AND METHODS Both synthetic and experimental data were used in the model fitting; the synthetic data were generated by an established spiking neuron model that was reported in our previous work, and the experimental data were provided using single-unit microelectrode recordings (MERs) during DBS (microelectrode stimulation). Based on these data, we developed a novel mathematical model to represent the firing rate of neurons receiving DBS, including neurons in STN, SNr, and Vim-across different DBS frequencies. In our model, the DBS pulses were filtered through a synapse model and a nonlinear transfer function to formulate the firing rate variability. For each DBS-targeted nucleus, we fitted a single set of optimal model parameters consistent across varying DBS frequencies. RESULTS Our model accurately reproduced the firing rates observed and calculated from both synthetic and experimental data. The optimal model parameters were consistent across different DBS frequencies. CONCLUSIONS The result of our model fitting was in agreement with experimental single-unit MER data during DBS. Reproducing neuronal firing rates of different nuclei of the basal ganglia and thalamus during DBS can be helpful to further understand the mechanisms of DBS and to potentially optimize stimulation parameters based on their actual effects on neuronal activity.
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Affiliation(s)
- Yupeng Tian
- Krembil Research Institute - University Health Network, Toronto, ON, Canada; Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada; KITE Research Institute, Toronto Rehabilitation Institute - University Health Network, Toronto, ON, Canada; CRANIA, University Health Network and University of Toronto, Toronto, ON, Canada
| | | | - Leon A Steiner
- Krembil Research Institute - University Health Network, Toronto, ON, Canada; Berlin Institute of Health, Berlin, Germany; Department of Surgery, University of Toronto, Toronto, ON, Canada; Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Suneil K Kalia
- Krembil Research Institute - University Health Network, Toronto, ON, Canada; KITE Research Institute, Toronto Rehabilitation Institute - University Health Network, Toronto, ON, Canada; CRANIA, University Health Network and University of Toronto, Toronto, ON, Canada; Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Neurosurgery, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
| | - Mojgan Hodaie
- Krembil Research Institute - University Health Network, Toronto, ON, Canada; CRANIA, University Health Network and University of Toronto, Toronto, ON, Canada; Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Neurosurgery, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
| | - Andres M Lozano
- Krembil Research Institute - University Health Network, Toronto, ON, Canada; CRANIA, University Health Network and University of Toronto, Toronto, ON, Canada; Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Neurosurgery, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
| | - William D Hutchison
- CRANIA, University Health Network and University of Toronto, Toronto, ON, Canada; Department of Surgery, University of Toronto, Toronto, ON, Canada; Department of Physiology, University of Toronto, Toronto, ON, Canada
| | - Milos R Popovic
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada; KITE Research Institute, Toronto Rehabilitation Institute - University Health Network, Toronto, ON, Canada; CRANIA, University Health Network and University of Toronto, Toronto, ON, Canada
| | - Luka Milosevic
- Krembil Research Institute - University Health Network, Toronto, ON, Canada; Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada; KITE Research Institute, Toronto Rehabilitation Institute - University Health Network, Toronto, ON, Canada; CRANIA, University Health Network and University of Toronto, Toronto, ON, Canada
| | - Milad Lankarany
- Krembil Research Institute - University Health Network, Toronto, ON, Canada; Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada; KITE Research Institute, Toronto Rehabilitation Institute - University Health Network, Toronto, ON, Canada; CRANIA, University Health Network and University of Toronto, Toronto, ON, Canada; Department of Physiology, University of Toronto, Toronto, ON, Canada.
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10
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De Zeeuw CI, Koppen J, Bregman GG, Runge M, Narain D. Heterogeneous encoding of temporal stimuli in the cerebellar cortex. Nat Commun 2023; 14:7581. [PMID: 37989740 PMCID: PMC10663630 DOI: 10.1038/s41467-023-43139-9] [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: 04/20/2023] [Accepted: 11/01/2023] [Indexed: 11/23/2023] Open
Abstract
Local feedforward and recurrent connectivity are rife in the frontal areas of the cerebral cortex, which gives rise to rich heterogeneous dynamics observed in such areas. Recently, similar local connectivity motifs have been discovered among Purkinje and molecular layer interneurons of the cerebellar cortex, however, task-related activity in these neurons has often been associated with relatively simple facilitation and suppression dynamics. Here, we show that the rodent cerebellar cortex supports heterogeneity in task-related neuronal activity at a scale similar to the cerebral cortex. We provide a computational model that inculcates recent anatomical insights into local microcircuit motifs to show the putative basis for such heterogeneity. We also use cell-type specific chronic viral lesions to establish the involvement of cerebellar lobules in associative learning behaviors. Functional heterogeneity in neuronal profiles may not merely be the remit of the associative cerebral cortex, similar principles may be at play in subcortical areas, even those with seemingly crystalline and homogenous cytoarchitectures like the cerebellum.
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Affiliation(s)
- Chris I De Zeeuw
- Department of Neuroscience, Erasmus University Medical Center, Rotterdam, The Netherlands
- Netherlands Institute of Neuroscience, Amsterdam, The Netherlands
| | - Julius Koppen
- Department of Neuroscience, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - George G Bregman
- Department of Neuroscience, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Marit Runge
- Department of Neuroscience, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Devika Narain
- Department of Neuroscience, Erasmus University Medical Center, Rotterdam, The Netherlands.
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11
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Huang Q, Luo M, Mi Y, Luo H. "Leader-Follower" Dynamic Perturbation Manipulates Multi-Item Working Memory in Humans. eNeuro 2023; 10:ENEURO.0472-22.2023. [PMID: 37914409 PMCID: PMC10668215 DOI: 10.1523/eneuro.0472-22.2023] [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: 11/21/2022] [Revised: 10/22/2023] [Accepted: 10/25/2023] [Indexed: 11/03/2023] Open
Abstract
Manipulating working memory (WM) is a central yet challenging notion. Previous studies suggest that WM items with varied memory strengths reactivate at different latencies, supporting a time-based mechanism. Motivated by this view, here we developed a purely bottom-up "Leader-Follower" behavioral approach to manipulate WM in humans. Specifically, task-irrelevant flickering color disks that are bound to each of the memorized items are presented during the delay period, and the ongoing luminance sequences of the color disks follow a Leader-Follower relationship, that is, a hundreds of milliseconds temporal lag. We show that this dynamic behavioral approach leads to better memory performance for the item associated with the temporally advanced luminance sequence (Leader) than the item with the temporally lagged luminance sequence (Follower), yet with limited effectiveness. Together, our findings constitute evidence for the essential role of temporal dynamics in WM operation and offer a promising, noninvasive WM manipulation approach.
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Affiliation(s)
- Qiaoli Huang
- School of Psychological and Cognitive Sciences, Peking University, Beijing 100871, China
- PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China
- Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing 100871, China
- Department of Psychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig 04103, Germany
| | - Minghao Luo
- School of Psychological and Cognitive Sciences, Peking University, Beijing 100871, China
- PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China
- Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing 100871, China
| | - Yuanyuan Mi
- Department of Psychology, School of Social Sciences, Tsinghua University, Beijing 100084, China
| | - Huan Luo
- School of Psychological and Cognitive Sciences, Peking University, Beijing 100871, China
- PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China
- Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing 100871, China
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12
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Diesburg DA, Wessel JR, Jones SR. Biophysical modeling of frontocentral ERP generation links circuit-level mechanisms of action-stopping to a behavioral race model. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.25.564020. [PMID: 37961333 PMCID: PMC10634895 DOI: 10.1101/2023.10.25.564020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Human frontocentral event-related potentials (FC-ERPs) are ubiquitous neural correlates of cognition and control, but their generating multiscale mechanisms remain mostly unknown. We used the Human Neocortical Neurosolver(HNN)'s biophysical model of a canonical neocortical circuit under exogenous thalamic and cortical drive to simulate the cell and circuit mechanisms underpinning the P2, N2, and P3 features of the FC-ERP observed after Stop-Signals in the Stop-Signal task (SST). We demonstrate that a sequence of simulated external thalamocortical and cortico-cortical drives can produce the FC-ERP, similar to what has been shown for primary sensory cortices. We used this model of the FC-ERP to examine likely circuit-mechanisms underlying FC-ERP features that distinguish between successful and failed action-stopping. We also tested their adherence to the predictions of the horse-race model of the SST, with specific hypotheses motivated by theoretical links between the P3 and Stop process. These simulations revealed that a difference in P3 onset between successful and failed Stops is most likely due to a later arrival of thalamocortical drive in failed Stops, rather than, for example, a difference in effective strength of the input. In contrast, the same model predicted that early thalamocortical drives underpinning the P2 and N2 differed in both strength and timing across stopping accuracy conditions. Overall, this model generates novel testable predictions of the thalamocortical dynamics underlying FC-ERP generation during action-stopping. Moreover, it provides a detailed cellular and circuit-level interpretation that supports links between these macroscale signatures and predictions of the behavioral race model.
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Affiliation(s)
| | - Jan R. Wessel
- Department of Psychological and Brain Sciences, University of Iowa, Iowa City, IA, USA
- Department of Neurology, Carver College of Medicine, University of Iowa Hospitals and Clinics, Iowa City, IA, USA
| | - Stephanie R. Jones
- Department of Neuroscience, Brown University, Providence, RI, USA
- Center for Neurorestoration and Neurotechnology, Providence VA Medical Center, RI, USA
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13
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Li S, Rosen MC, Chang S, David S, Freedman DJ. Alterations of neural activity in the prefrontal cortex associated with deficits in working memory performance. Front Behav Neurosci 2023; 17:1213435. [PMID: 37915531 PMCID: PMC10616307 DOI: 10.3389/fnbeh.2023.1213435] [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: 04/27/2023] [Accepted: 08/31/2023] [Indexed: 11/03/2023] Open
Abstract
Working memory (WM), a core cognitive function, enables the temporary holding and manipulation of information in mind to support ongoing behavior. Neurophysiological recordings conducted in nonhuman primates have revealed neural correlates of this process in a network of higher-order cortical regions, particularly the prefrontal cortex (PFC). Here, we review the circuit mechanisms and functional importance of WM-related activity in these areas. Recent neurophysiological data indicates that the absence of these neural correlates at different stages of WM is accompanied by distinct behavioral deficits, which are characteristic of various disease states/normal aging and which we review here. Finally, we discuss emerging evidence of electrical stimulation ameliorating these WM deficits in both humans and non-human primates. These results are important for a basic understanding of the neural mechanisms supporting WM, as well as for translational efforts to developing therapies capable of enhancing healthy WM ability or restoring WM from dysfunction.
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Affiliation(s)
- Sihai Li
- Department of Neurobiology, The University of Chicago, Chicago, IL, United States
| | - Matthew C. Rosen
- Department of Neurobiology, The University of Chicago, Chicago, IL, United States
| | - Suha Chang
- Department of Neurobiology, The University of Chicago, Chicago, IL, United States
| | - Samuel David
- Department of Neurobiology, The University of Chicago, Chicago, IL, United States
| | - David J. Freedman
- Department of Neurobiology, The University of Chicago, Chicago, IL, United States
- Neuroscience Institute, The University of Chicago, Chicago, IL, United States
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14
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Bliss DP, Rahnev D, Mackey WE, Curtis CE, D'Esposito M. Stimulation along the anterior-posterior axis of lateral frontal cortex reduces visual serial dependence. J Vis 2023; 23:1. [PMID: 37395704 PMCID: PMC10324416 DOI: 10.1167/jov.23.7.1] [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: 12/07/2022] [Accepted: 06/07/2023] [Indexed: 07/04/2023] Open
Abstract
Serial dependence is an attractive pull that recent perceptual history exerts on current judgments. Theory suggests that this bias is due to a form of short-term plasticity prevalent specifically in the frontal lobe. We sought to test the importance of the frontal lobe to serial dependence by disrupting neural activity along its lateral surface during two tasks with distinct perceptual and motor demands. In our first experiment, stimulation of the lateral prefrontal cortex (LPFC) during an oculomotor delayed response task decreased serial dependence only in the first saccade to the target, whereas stimulation posterior to the LPFC decreased serial dependence only in adjustments to eye position after the first saccade. In our second experiment, which used an orientation discrimination task, stimulation anterior to, in, and posterior to the LPFC all caused equivalent decreases in serial dependence. In this experiment, serial dependence occurred only between stimuli at the same location; an alternation bias was observed across hemifields. Frontal stimulation had no effect on the alternation bias. Transcranial magnetic stimulation to parietal cortex had no effect on serial dependence in either experiment. In summary, our experiments provide evidence for both functional differentiation (Experiment 1) and redundancy (Experiment 2) in frontal cortex with respect to serial dependence.
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Affiliation(s)
- Daniel P Bliss
- Citizen Science Program, Bard College, Annandale-on-Hudson, NY, USA
| | - Dobromir Rahnev
- School of Psychology, Georgia Institute of Technology, Atlanta, GA, USA
| | - Wayne E Mackey
- Department of Psychology, New York University, New York, NY, USA
| | - Clayton E Curtis
- Department of Psychology, New York University, New York, NY, USA
- Center for Neural Science, New York University, New York, NY, USA
| | - Mark D'Esposito
- Helen Wills Neuroscience Institute and Department of Psychology, University of California, Berkeley, Berkeley, CA, USA
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15
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Sawicki J, Berner R, Loos SAM, Anvari M, Bader R, Barfuss W, Botta N, Brede N, Franović I, Gauthier DJ, Goldt S, Hajizadeh A, Hövel P, Karin O, Lorenz-Spreen P, Miehl C, Mölter J, Olmi S, Schöll E, Seif A, Tass PA, Volpe G, Yanchuk S, Kurths J. Perspectives on adaptive dynamical systems. CHAOS (WOODBURY, N.Y.) 2023; 33:071501. [PMID: 37486668 DOI: 10.1063/5.0147231] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 05/24/2023] [Indexed: 07/25/2023]
Abstract
Adaptivity is a dynamical feature that is omnipresent in nature, socio-economics, and technology. For example, adaptive couplings appear in various real-world systems, such as the power grid, social, and neural networks, and they form the backbone of closed-loop control strategies and machine learning algorithms. In this article, we provide an interdisciplinary perspective on adaptive systems. We reflect on the notion and terminology of adaptivity in different disciplines and discuss which role adaptivity plays for various fields. We highlight common open challenges and give perspectives on future research directions, looking to inspire interdisciplinary approaches.
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Affiliation(s)
- Jakub Sawicki
- Potsdam Institute for Climate Impact Research, Telegrafenberg, 14473 Potsdam, Germany
- Akademie Basel, Fachhochschule Nordwestschweiz FHNW, Leonhardsstrasse 6, 4009 Basel, Switzerland
| | - Rico Berner
- Department of Physics, Humboldt-Universität zu Berlin, Newtonstraße 15, 12489 Berlin, Germany
| | - Sarah A M Loos
- DAMTP, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, United Kingdom
| | - Mehrnaz Anvari
- Potsdam Institute for Climate Impact Research, Telegrafenberg, 14473 Potsdam, Germany
- Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven, 53757 Sankt-Augustin, Germany
| | - Rolf Bader
- Institute of Systematic Musicology, University of Hamburg, Hamburg, Germany
| | - Wolfram Barfuss
- Transdisciplinary Research Area: Sustainable Futures, University of Bonn, 53113 Bonn, Germany
- Center for Development Research (ZEF), University of Bonn, 53113 Bonn, Germany
| | - Nicola Botta
- Potsdam Institute for Climate Impact Research, Telegrafenberg, 14473 Potsdam, Germany
- Department of Computer Science and Engineering, Chalmers University of Technology, 412 96 Göteborg, Sweden
| | - Nuria Brede
- Potsdam Institute for Climate Impact Research, Telegrafenberg, 14473 Potsdam, Germany
- Department of Computer Science, University of Potsdam, An der Bahn 2, 14476 Potsdam, Germany
| | - Igor Franović
- Scientific Computing Laboratory, Center for the Study of Complex Systems, Institute of Physics Belgrade, University of Belgrade, Pregrevica 118, 11080 Belgrade, Serbia
| | - Daniel J Gauthier
- Potsdam Institute for Climate Impact Research, Telegrafenberg, 14473 Potsdam, Germany
| | - Sebastian Goldt
- Department of Physics, International School of Advanced Studies (SISSA), Trieste, Italy
| | - Aida Hajizadeh
- Research Group Comparative Neuroscience, Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Philipp Hövel
- Potsdam Institute for Climate Impact Research, Telegrafenberg, 14473 Potsdam, Germany
| | - Omer Karin
- Department of Mathematics, Imperial College London, London SW7 2AZ, United Kingdom
| | - Philipp Lorenz-Spreen
- Center for Adaptive Rationality, Max Planck Institute for Human Development, Lentzeallee 94, 14195 Berlin, Germany
| | - Christoph Miehl
- Akademie Basel, Fachhochschule Nordwestschweiz FHNW, Leonhardsstrasse 6, 4009 Basel, Switzerland
| | - Jan Mölter
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Boltzmannstraße 3, 85748 Garching bei München, Germany
| | - Simona Olmi
- Akademie Basel, Fachhochschule Nordwestschweiz FHNW, Leonhardsstrasse 6, 4009 Basel, Switzerland
| | - Eckehard Schöll
- Potsdam Institute for Climate Impact Research, Telegrafenberg, 14473 Potsdam, Germany
- Akademie Basel, Fachhochschule Nordwestschweiz FHNW, Leonhardsstrasse 6, 4009 Basel, Switzerland
| | - Alireza Seif
- Pritzker School of Molecular Engineering, The University of Chicago, Chicago, Illinois 60637, USA
| | - Peter A Tass
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, California 94304, USA
| | - Giovanni Volpe
- Department of Physics, University of Gothenburg, Gothenburg, Sweden
| | - Serhiy Yanchuk
- Potsdam Institute for Climate Impact Research, Telegrafenberg, 14473 Potsdam, Germany
- Department of Physics, Humboldt-Universität zu Berlin, Newtonstraße 15, 12489 Berlin, Germany
| | - Jürgen Kurths
- Potsdam Institute for Climate Impact Research, Telegrafenberg, 14473 Potsdam, Germany
- Department of Physics, Humboldt-Universität zu Berlin, Newtonstraße 15, 12489 Berlin, Germany
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16
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Zhang Y, Ji W, Jiang F, Wu F, Li G, Hu Y, Zhang W, Wang J, Fan X, Wei X, Manza P, Tomasi D, Volkow ND, Gao X, Wang GJ, Zhang Y. Associations among body mass index, working memory performance, gray matter volume, and brain activation in healthy children. Cereb Cortex 2023; 33:6335-6344. [PMID: 36573454 PMCID: PMC10422922 DOI: 10.1093/cercor/bhac507] [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: 09/27/2022] [Revised: 12/01/2022] [Accepted: 12/02/2022] [Indexed: 12/28/2022] Open
Abstract
To investigate the neural mechanisms underlying the association between poorer working memory performance and higher body mass index (BMI) in children. We employed structural-(sMRI) and functional magnetic resonance imaging (fMRI) with a 2-back working memory task to examine brain abnormalities and their associations with BMI and working memory performance in 232 children with overweight/obesity (OW/OB) and 244 normal weight children (NW) from the Adolescent Brain Cognitive Development dataset. OW/OB had lower working memory accuracy, which was associated with higher BMI. They showed smaller gray matter (GM) volumes in the left superior frontal gyrus (SFG_L), dorsal anterior cingulate cortex, medial orbital frontal cortex, and medial superior frontal gyrus, which were associated with lower working memory accuracy. During the working memory task, OW/OB relative to NW showed weaker activation in the left superior temporal pole, amygdala, insula, and bilateral caudate. In addition, caudate activation mediated the relationship between higher BMI and lower working memory accuracy. Higher BMI is associated with smaller GM volumes and weaker brain activation in regions involved with working memory. Task-related caudate dysfunction may account for lower working memory accuracy in children with higher BMI.
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Affiliation(s)
- Yaqi Zhang
- Center for Brain Imaging, School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, No. 266, Xifeng Road, Xi'an, Shaanxi 710126, China
- International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment & Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information, School of Life Science and Technology, Xidian University, No. 266, Xifeng Road, Xi'an, Shaanxi 710126, China
| | - Weibin Ji
- Center for Brain Imaging, School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, No. 266, Xifeng Road, Xi'an, Shaanxi 710126, China
- International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment & Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information, School of Life Science and Technology, Xidian University, No. 266, Xifeng Road, Xi'an, Shaanxi 710126, China
| | - Fukun Jiang
- Center for Brain Imaging, School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, No. 266, Xifeng Road, Xi'an, Shaanxi 710126, China
- International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment & Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information, School of Life Science and Technology, Xidian University, No. 266, Xifeng Road, Xi'an, Shaanxi 710126, China
| | - Feifei Wu
- Center for Brain Imaging, School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, No. 266, Xifeng Road, Xi'an, Shaanxi 710126, China
- International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment & Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information, School of Life Science and Technology, Xidian University, No. 266, Xifeng Road, Xi'an, Shaanxi 710126, China
| | - Guanya Li
- Center for Brain Imaging, School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, No. 266, Xifeng Road, Xi'an, Shaanxi 710126, China
- International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment & Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information, School of Life Science and Technology, Xidian University, No. 266, Xifeng Road, Xi'an, Shaanxi 710126, China
| | - Yang Hu
- Center for Brain Imaging, School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, No. 266, Xifeng Road, Xi'an, Shaanxi 710126, China
- International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment & Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information, School of Life Science and Technology, Xidian University, No. 266, Xifeng Road, Xi'an, Shaanxi 710126, China
| | - Wenchao Zhang
- Center for Brain Imaging, School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, No. 266, Xifeng Road, Xi'an, Shaanxi 710126, China
- International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment & Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information, School of Life Science and Technology, Xidian University, No. 266, Xifeng Road, Xi'an, Shaanxi 710126, China
| | - Jia Wang
- Center for Brain Imaging, School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, No. 266, Xifeng Road, Xi'an, Shaanxi 710126, China
- International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment & Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information, School of Life Science and Technology, Xidian University, No. 266, Xifeng Road, Xi'an, Shaanxi 710126, China
| | - Xiao Fan
- Center for Brain Imaging, School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, No. 266, Xifeng Road, Xi'an, Shaanxi 710126, China
- International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment & Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information, School of Life Science and Technology, Xidian University, No. 266, Xifeng Road, Xi'an, Shaanxi 710126, China
| | - Xiaorong Wei
- Kindergarten affiliated to Air Force Medical University, No. 127, Changle West Road, Xi'an, Shaanxi 710032, China
| | - Peter Manza
- Laboratory of Neuroimaging, National Institute on Alcohol Abuse and Alcoholism, 10 Center Drive, MSC1013, Building 10, Room B2L304, Bethesda, MD 20892, United States
| | - Dardo Tomasi
- Laboratory of Neuroimaging, National Institute on Alcohol Abuse and Alcoholism, 10 Center Drive, MSC1013, Building 10, Room B2L304, Bethesda, MD 20892, United States
| | - Nora D Volkow
- Laboratory of Neuroimaging, National Institute on Alcohol Abuse and Alcoholism, 10 Center Drive, MSC1013, Building 10, Room B2L304, Bethesda, MD 20892, United States
| | - Xinbo Gao
- Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, No. 2, Chongwen Road, Chongqing 400065, China
- Chongqing Institute for Brain and Intelligence, Guangyang Bay Laboratory, No. 2, Chongwen Road, Chongqing 400064, China
| | - Gene-Jack Wang
- Laboratory of Neuroimaging, National Institute on Alcohol Abuse and Alcoholism, 10 Center Drive, MSC1013, Building 10, Room B2L304, Bethesda, MD 20892, United States
| | - Yi Zhang
- Center for Brain Imaging, School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, No. 266, Xifeng Road, Xi'an, Shaanxi 710126, China
- International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment & Xi'an Key Laboratory of Intelligent Sensing and Regulation of trans-Scale Life Information, School of Life Science and Technology, Xidian University, No. 266, Xifeng Road, Xi'an, Shaanxi 710126, China
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17
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McCarthy CI, Mustafá ER, Cornejo MP, Yaneff A, Rodríguez SS, Perello M, Raingo J. Chlorpromazine, an Inverse Agonist of D1R-Like, Differentially Targets Voltage-Gated Calcium Channel (Ca V) Subtypes in mPFC Neurons. Mol Neurobiol 2023; 60:2644-2660. [PMID: 36694048 DOI: 10.1007/s12035-023-03221-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 01/04/2023] [Indexed: 01/26/2023]
Abstract
The dopamine receptor type 1 (D1R) and the dopamine receptor type 5 (D5R), which are often grouped as D1R-like due to their sequence and signaling similarities, exhibit high levels of constitutive activity. The molecular basis for this agonist-independent activation has been well characterized through biochemical and mutagenesis in vitro studies. In this regard, it was reported that many antipsychotic drugs act as inverse agonists of D1R-like constitutive activity. On the other hand, D1R is highly expressed in the medial prefrontal cortex (mPFC), a brain area with important functions such as working memory. Here, we studied the impact of D1R-like constitutive activity and chlorpromazine (CPZ), an antipsychotic drug and D1R-like inverse agonist, on various neuronal CaV conductances, and we explored its effect on calcium-dependent neuronal functions in the mouse medial mPFC. Using ex vivo brain slices containing the mPFC and transfected HEK293T cells, we found that CPZ reduces CaV2.2 currents by occluding D1R-like constitutive activity, in agreement with a mechanism previously reported by our lab, whereas CPZ directly inhibits CaV1 currents in a D1R-like activity independent manner. In contrast, CPZ and D1R constitutive activity did not affect CaV2.1, CaV2.3, or CaV3 currents. Finally, we found that CPZ reduces excitatory postsynaptic responses in mPFC neurons. Our results contribute to understanding CPZ molecular targets in neurons and describe a novel physiological consequence of CPZ non-canonical action as a D1R-like inverse agonist in the mouse brain.
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Affiliation(s)
- Clara Inés McCarthy
- Electrophysiology Laboratory of the Multidisciplinary Institute of Cell Biology (Argentine Research Council CONICET, Scientific Research Commission of the Buenos Aires Province and National University of La Plata), La Plata, Buenos Aires, Argentina
| | - Emilio Román Mustafá
- Electrophysiology Laboratory of the Multidisciplinary Institute of Cell Biology (Argentine Research Council CONICET, Scientific Research Commission of the Buenos Aires Province and National University of La Plata), La Plata, Buenos Aires, Argentina
| | - María Paula Cornejo
- Neurophysiology Laboratory of the Multidisciplinary Institute of Cell Biology (Argentine Research Council CONICET, Scientific Research Commission of the Buenos Aires Province and National University of La Plata), La Plata, Buenos Aires, Argentina
| | - Agustín Yaneff
- Instituto de Investigaciones Farmacológicas, Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Silvia Susana Rodríguez
- Electrophysiology Laboratory of the Multidisciplinary Institute of Cell Biology (Argentine Research Council CONICET, Scientific Research Commission of the Buenos Aires Province and National University of La Plata), La Plata, Buenos Aires, Argentina
| | - Mario Perello
- Neurophysiology Laboratory of the Multidisciplinary Institute of Cell Biology (Argentine Research Council CONICET, Scientific Research Commission of the Buenos Aires Province and National University of La Plata), La Plata, Buenos Aires, Argentina
- Department of Surgical Sciences, Functional Pharmacology and Neuroscience, University of Uppsala, Uppsala, Sweden
| | - Jesica Raingo
- Electrophysiology Laboratory of the Multidisciplinary Institute of Cell Biology (Argentine Research Council CONICET, Scientific Research Commission of the Buenos Aires Province and National University of La Plata), La Plata, Buenos Aires, Argentina.
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18
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Goz RU, Hooks BM. Correlated Somatosensory Input in Parvalbumin/Pyramidal Cells in Mouse Motor Cortex. eNeuro 2023; 10:ENEURO.0488-22.2023. [PMID: 37094939 PMCID: PMC10167893 DOI: 10.1523/eneuro.0488-22.2023] [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: 11/30/2022] [Revised: 04/02/2023] [Accepted: 04/18/2023] [Indexed: 04/26/2023] Open
Abstract
In mammalian cortex, feedforward excitatory connections recruit feedforward inhibition. This is often carried by parvalbumin (PV+) interneurons, which may densely connect to local pyramidal (Pyr) neurons. Whether this inhibition affects all local excitatory cells indiscriminately or is targeted to specific subnetworks is unknown. Here, we test how feedforward inhibition is recruited by using two-channel circuit mapping to excite cortical and thalamic inputs to PV+ interneurons and Pyr neurons to mouse primary vibrissal motor cortex (M1). Single Pyr and PV+ neurons receive input from both cortex and thalamus. Connected pairs of PV+ interneurons and excitatory Pyr neurons receive correlated cortical and thalamic inputs. While PV+ interneurons are more likely to form local connections to Pyr neurons, Pyr neurons are much more likely to form reciprocal connections with PV+ interneurons that inhibit them. This suggests that Pyr and PV ensembles may be organized based on their local and long-range connections, an organization that supports the idea of local subnetworks for signal transduction and processing. Excitatory inputs to M1 can thus target inhibitory networks in a specific pattern which permits recruitment of feedforward inhibition to specific subnetworks within the cortical column.
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Affiliation(s)
- Roman U Goz
- Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213
| | - Bryan M Hooks
- Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213
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19
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Abdalaziz M, Redding ZV, Fiebelkorn IC. Rhythmic temporal coordination of neural activity prevents representational conflict during working memory. Curr Biol 2023; 33:1855-1863.e3. [PMID: 37100058 DOI: 10.1016/j.cub.2023.03.088] [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: 12/05/2022] [Revised: 02/27/2023] [Accepted: 03/31/2023] [Indexed: 04/28/2023]
Abstract
Selective attention1 is characterized by alternating states associated with either attentional sampling or attentional shifting, helping to prevent functional conflicts by isolating function-specific neural activity in time.2,3,4,5 We hypothesized that such rhythmic temporal coordination might also help to prevent representational conflicts during working memory.6 Multiple items can be simultaneously held in working memory, and these items can be represented by overlapping neural populations.7,8,9 Traditional theories propose that the short-term storage of to-be-remembered items occurs through persistent neural activity,10,11,12 but when neurons are simultaneously representing multiple items, persistent activity creates a potential for representational conflicts. In comparison, more recent, "activity-silent" theories of working memory propose that synaptic changes also contribute to short-term storage of to-be-remembered items.13,14,15,16 Transient bursts in neural activity,17 rather than persistent activity, could serve to occasionally refresh these synaptic changes. Here, we used EEG and response times to test whether rhythmic temporal coordination helps to isolate neural activity associated with different to-be-remembered items, thereby helping to prevent representational conflicts. Consistent with this hypothesis, we report that the relative strength of different item representations alternates over time as a function of the frequency-specific phase. Although RTs were linked to theta (∼6 Hz) and beta (∼25 Hz) phases during a memory delay, the relative strength of item representations only alternated as a function of the beta phase. The present findings (1) are consistent with rhythmic temporal coordination being a general mechanism for preventing functional or representational conflicts during cognitive processes and (2) inform models describing the role of oscillatory dynamics in organizing working memory.13,18,19,20,21.
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Affiliation(s)
- Miral Abdalaziz
- Department of Neuroscience and Del Monte Institute for Neuroscience, University of Rochester, Rochester, NY 14627, USA
| | - Zach V Redding
- Department of Neuroscience and Del Monte Institute for Neuroscience, University of Rochester, Rochester, NY 14627, USA
| | - Ian C Fiebelkorn
- Department of Neuroscience and Del Monte Institute for Neuroscience, University of Rochester, Rochester, NY 14627, USA.
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20
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Printz Y, Patil P, Mahn M, Benjamin A, Litvin A, Levy R, Bringmann M, Yizhar O. Determinants of functional synaptic connectivity among amygdala-projecting prefrontal cortical neurons in male mice. Nat Commun 2023; 14:1667. [PMID: 36966143 PMCID: PMC10039875 DOI: 10.1038/s41467-023-37318-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 03/13/2023] [Indexed: 03/27/2023] Open
Abstract
The medial prefrontal cortex (mPFC) mediates a variety of complex cognitive functions via its vast and diverse connections with cortical and subcortical structures. Understanding the patterns of synaptic connectivity that comprise the mPFC local network is crucial for deciphering how this circuit processes information and relays it to downstream structures. To elucidate the synaptic organization of the mPFC, we developed a high-throughput optogenetic method for mapping large-scale functional synaptic connectivity in acute brain slices. We show that in male mice, mPFC neurons that project to the basolateral amygdala (BLA) display unique spatial patterns of local-circuit synaptic connectivity, which distinguish them from the general mPFC cell population. When considering synaptic connections between pairs of mPFC neurons, the intrinsic properties of the postsynaptic cell and the anatomical positions of both cells jointly account for ~7.5% of the variation in the probability of connection. Moreover, anatomical distance and laminar position explain most of this fraction in variation. Our findings reveal the factors determining connectivity in the mPFC and delineate the architecture of synaptic connections in the BLA-projecting subnetwork.
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Affiliation(s)
- Yoav Printz
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, 76100, Israel
| | - Pritish Patil
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, 76100, Israel
| | - Mathias Mahn
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, 76100, Israel
| | - Asaf Benjamin
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, 76100, Israel
| | - Anna Litvin
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, 76100, Israel
| | - Rivka Levy
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, 76100, Israel
| | - Max Bringmann
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, 76100, Israel
| | - Ofer Yizhar
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, 76100, Israel.
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21
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Kasai H, Ucar H, Morimoto Y, Eto F, Okazaki H. Mechanical transmission at spine synapses: Short-term potentiation and working memory. Curr Opin Neurobiol 2023; 80:102706. [PMID: 36931116 DOI: 10.1016/j.conb.2023.102706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 11/17/2022] [Accepted: 02/15/2023] [Indexed: 03/17/2023]
Abstract
Do dendritic spines, which comprise the postsynaptic component of most excitatory synapses, exist only for their structural dynamics, receptor trafficking, and chemical and electrical compartmentation? The answer is no. Simultaneous investigation of both spine and presynaptic terminals has recently revealed a novel feature of spine synapses. Spine enlargement pushes the presynaptic terminals with muscle-like force and augments the evoked glutamate release for up to 20 min. We now summarize the evidence that such mechanical transmission shares critical features in common with short-term potentiation (STP) and may represent the cellular basis of short-term and working memory. Thus, spine synapses produce the force of learning to leave structural traces for both short and long-term memories.
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Affiliation(s)
- Haruo Kasai
- International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, Bunkyo-ku, Tokyo, Japan; Laboratory of Structural Physiology, Center for Disease Biology and Integrative Medicine, Faculty of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, Japan.
| | - Hasan Ucar
- International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, Bunkyo-ku, Tokyo, Japan; Laboratory of Structural Physiology, Center for Disease Biology and Integrative Medicine, Faculty of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Yuichi Morimoto
- International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, Bunkyo-ku, Tokyo, Japan; Laboratory of Structural Physiology, Center for Disease Biology and Integrative Medicine, Faculty of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Fumihiro Eto
- International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, Bunkyo-ku, Tokyo, Japan; Laboratory of Structural Physiology, Center for Disease Biology and Integrative Medicine, Faculty of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Hitoshi Okazaki
- International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, Bunkyo-ku, Tokyo, Japan; Laboratory of Structural Physiology, Center for Disease Biology and Integrative Medicine, Faculty of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
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22
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Goldt S, Krzakala F, Zdeborová L, Brunel N. Bayesian reconstruction of memories stored in neural networks from their connectivity. PLoS Comput Biol 2023; 19:e1010813. [PMID: 36716332 PMCID: PMC9910750 DOI: 10.1371/journal.pcbi.1010813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 02/09/2023] [Accepted: 12/12/2022] [Indexed: 02/01/2023] Open
Abstract
The advent of comprehensive synaptic wiring diagrams of large neural circuits has created the field of connectomics and given rise to a number of open research questions. One such question is whether it is possible to reconstruct the information stored in a recurrent network of neurons, given its synaptic connectivity matrix. Here, we address this question by determining when solving such an inference problem is theoretically possible in specific attractor network models and by providing a practical algorithm to do so. The algorithm builds on ideas from statistical physics to perform approximate Bayesian inference and is amenable to exact analysis. We study its performance on three different models, compare the algorithm to standard algorithms such as PCA, and explore the limitations of reconstructing stored patterns from synaptic connectivity.
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Affiliation(s)
- Sebastian Goldt
- International School of Advanced Studies (SISSA), Trieste, Italy
- * E-mail:
| | - Florent Krzakala
- IdePHICS laboratory, Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland
| | - Lenka Zdeborová
- SPOC laboratory, Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland
| | - Nicolas Brunel
- Department of Neurobiology, Duke University, Durham, North Carolina, United States of America
- Department of Physics, Duke University, Durham, North Carolina, United States of America
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23
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Angular gyrus: an anatomical case study for association cortex. Brain Struct Funct 2023; 228:131-143. [PMID: 35906433 DOI: 10.1007/s00429-022-02537-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 07/05/2022] [Indexed: 01/07/2023]
Abstract
The angular gyrus is associated with a spectrum of higher order cognitive functions. This mini-review undertakes a broad survey of putative neuroanatomical substrates, guided by the premise that area-specific specializations derive from a combination of extrinsic connections and intrinsic area properties. Three levels of spatial resolution are discussed: cellular, supracellular connectivity, and synaptic micro-scale, with examples necessarily drawn mainly from experimental work with nonhuman primates. A significant factor in the functional specialization of the human parietal cortex is the pronounced enlargement. In addition to "more" cells, synapses, and connections, however, the heterogeneity itself can be considered an important property. Multiple anatomical features support the idea of overlapping and temporally dynamic membership in several brain wide subnetworks, but how these features operate in the context of higher cognitive functions remains for continued investigations.
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24
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Barri A, Wiechert MT, Jazayeri M, DiGregorio DA. Synaptic basis of a sub-second representation of time in a neural circuit model. Nat Commun 2022; 13:7902. [PMID: 36550115 PMCID: PMC9780315 DOI: 10.1038/s41467-022-35395-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 11/29/2022] [Indexed: 12/24/2022] Open
Abstract
Temporal sequences of neural activity are essential for driving well-timed behaviors, but the underlying cellular and circuit mechanisms remain elusive. We leveraged the well-defined architecture of the cerebellum, a brain region known to support temporally precise actions, to explore theoretically whether the experimentally observed diversity of short-term synaptic plasticity (STP) at the input layer could generate neural dynamics sufficient for sub-second temporal learning. A cerebellar circuit model equipped with dynamic synapses produced a diverse set of transient granule cell firing patterns that provided a temporal basis set for learning precisely timed pauses in Purkinje cell activity during simulated delay eyelid conditioning and Bayesian interval estimation. The learning performance across time intervals was influenced by the temporal bandwidth of the temporal basis, which was determined by the input layer synaptic properties. The ubiquity of STP throughout the brain positions it as a general, tunable cellular mechanism for sculpting neural dynamics and fine-tuning behavior.
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Affiliation(s)
- A. Barri
- grid.508487.60000 0004 7885 7602Institut Pasteur, Université Paris Cité, Synapse and Circuit Dynamics Laboratory, CNRS UMR 3571 Paris, France
| | - M. T. Wiechert
- grid.508487.60000 0004 7885 7602Institut Pasteur, Université Paris Cité, Synapse and Circuit Dynamics Laboratory, CNRS UMR 3571 Paris, France
| | - M. Jazayeri
- grid.116068.80000 0001 2341 2786McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA USA ,grid.116068.80000 0001 2341 2786Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA USA
| | - D. A. DiGregorio
- grid.508487.60000 0004 7885 7602Institut Pasteur, Université Paris Cité, Synapse and Circuit Dynamics Laboratory, CNRS UMR 3571 Paris, France
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25
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Bocincova A, Buschman TJ, Stokes MG, Manohar SG. Neural signature of flexible coding in prefrontal cortex. Proc Natl Acad Sci U S A 2022; 119:e2200400119. [PMID: 36161948 PMCID: PMC9546590 DOI: 10.1073/pnas.2200400119] [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: 01/09/2022] [Accepted: 08/15/2022] [Indexed: 11/18/2022] Open
Abstract
The ability of prefrontal cortex to quickly encode novel associations is crucial for adaptive behavior and central to working memory. Fast Hebbian changes in synaptic strength permit forming new associations, but neuronal signatures of this have been elusive. We devised a trialwise index of pattern similarity to look for rapid changes in population codes. Based on a computational model of working memory, we hypothesized that synaptic strength-and consequently, the tuning of neurons-could change if features of a subsequent stimulus need to be "reassociated," i.e., if bindings between features need to be broken to encode the new item. As a result, identical stimuli might elicit different neural responses. As predicted, neural response similarity dropped following rebinding, but only in prefrontal cortex. The history-dependent changes were expressed on top of traditional, fixed selectivity and were not explainable by carryover of previous firing into the current trial or by neural adaptation.
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Affiliation(s)
- Andrea Bocincova
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, United Kingdom
| | - Timothy J. Buschman
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08540
- Department of Psychology, Princeton University, Princeton, NJ 08540
| | - Mark G. Stokes
- Department of Experimental Psychology, University of Oxford, Oxford OX2 6GG, United Kingdom
| | - Sanjay G. Manohar
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, United Kingdom
- Department of Experimental Psychology, University of Oxford, Oxford OX2 6GG, United Kingdom
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26
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Tiddia G, Golosio B, Fanti V, Paolucci PS. Simulations of working memory spiking networks driven by short-term plasticity. Front Integr Neurosci 2022; 16:972055. [PMID: 36262372 PMCID: PMC9574057 DOI: 10.3389/fnint.2022.972055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 08/29/2022] [Indexed: 11/15/2022] Open
Abstract
Working Memory (WM) is a cognitive mechanism that enables temporary holding and manipulation of information in the human brain. This mechanism is mainly characterized by a neuronal activity during which neuron populations are able to maintain an enhanced spiking activity after being triggered by a short external cue. In this study, we implement, using the NEST simulator, a spiking neural network model in which the WM activity is sustained by a mechanism of short-term synaptic facilitation related to presynaptic calcium kinetics. The model, which is characterized by leaky integrate-and-fire neurons with exponential postsynaptic currents, is able to autonomously show an activity regime in which the memory information can be stored in a synaptic form as a result of synaptic facilitation, with spiking activity functional to facilitation maintenance. The network is able to simultaneously keep multiple memories by showing an alternated synchronous activity which preserves the synaptic facilitation within the neuron populations holding memory information. The results shown in this study confirm that a WM mechanism can be sustained by synaptic facilitation.
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Affiliation(s)
- Gianmarco Tiddia
- Department of Physics, University of Cagliari, Monserrato, Italy
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Cagliari, Monserrato, Italy
| | - Bruno Golosio
- Department of Physics, University of Cagliari, Monserrato, Italy
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Cagliari, Monserrato, Italy
- *Correspondence: Bruno Golosio
| | - Viviana Fanti
- Department of Physics, University of Cagliari, Monserrato, Italy
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Cagliari, Monserrato, Italy
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27
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Lundqvist M, Rose J, Brincat SL, Warden MR, Buschman TJ, Herman P, Miller EK. Reduced variability of bursting activity during working memory. Sci Rep 2022; 12:15050. [PMID: 36064880 PMCID: PMC9445015 DOI: 10.1038/s41598-022-18577-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 08/16/2022] [Indexed: 12/03/2022] Open
Abstract
Working memories have long been thought to be maintained by persistent spiking. However, mounting evidence from multiple-electrode recording (and single-trial analyses) shows that the underlying spiking is better characterized by intermittent bursts of activity. A counterargument suggested this intermittent activity is at odds with observations that spike-time variability reduces during task performance. However, this counterargument rests on assumptions, such as randomness in the timing of the bursts, which may not be correct. Thus, we analyzed spiking and LFPs from monkeys' prefrontal cortex (PFC) to determine if task-related reductions in variability can co-exist with intermittent spiking. We found that it does because both spiking and associated gamma bursts were task-modulated, not random. In fact, the task-related reduction in spike variability could largely be explained by a related reduction in gamma burst variability. Our results provide further support for the intermittent activity models of working memory as well as novel mechanistic insights into how spike variability is reduced during cognitive tasks.
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Affiliation(s)
- Mikael Lundqvist
- Department of Psychology, Department of Clinical Neuroscience, Karolinska Institute, Solna, Sweden.
- The Picower Institute for Learning and Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
| | - Jonas Rose
- The Picower Institute for Learning and Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Faculty of Psychology, Neural Basis of Learning, Ruhr University Bochum, 44801, Bochum, Germany
| | - Scott L Brincat
- The Picower Institute for Learning and Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Melissa R Warden
- The Picower Institute for Learning and Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Department of Neurobiology and Behavior, Cornell University, Ithaca, NY, 14853, USA
| | - Timothy J Buschman
- The Picower Institute for Learning and Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Princeton Neuroscience Institute, Princeton University, Washington Rd., Princeton, NJ, 08540, USA
| | - Pawel Herman
- Department of Computational Science and Technology, School of Electrical Engineering and Computer Science and Digital Futures, KTH Royal Institute of Technology, 100 44, Stockholm, Sweden
| | - Earl K Miller
- The Picower Institute for Learning and Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
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28
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Chrysanthidis N, Fiebig F, Lansner A, Herman P. Traces of semantization - from episodic to semantic memory in a spiking cortical network model. eNeuro 2022; 9:ENEURO.0062-22.2022. [PMID: 35803714 PMCID: PMC9347313 DOI: 10.1523/eneuro.0062-22.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Revised: 05/05/2022] [Accepted: 05/28/2022] [Indexed: 11/21/2022] Open
Abstract
Episodic memory is a recollection of past personal experiences associated with particular times and places. This kind of memory is commonly subject to loss of contextual information or" semantization", which gradually decouples the encoded memory items from their associated contexts while transforming them into semantic or gist-like representations. Novel extensions to the classical Remember/Know behavioral paradigm attribute the loss of episodicity to multiple exposures of an item in different contexts. Despite recent advancements explaining semantization at a behavioral level, the underlying neural mechanisms remain poorly understood. In this study, we suggest and evaluate a novel hypothesis proposing that Bayesian-Hebbian synaptic plasticity mechanisms might cause semantization of episodic memory. We implement a cortical spiking neural network model with a Bayesian-Hebbian learning rule called Bayesian Confidence Propagation Neural Network (BCPNN), which captures the semantization phenomenon and offers a mechanistic explanation for it. Encoding items across multiple contexts leads to item-context decoupling akin to semantization. We compare BCPNN plasticity with the more commonly used spike-timing dependent plasticity (STDP) learning rule in the same episodic memory task. Unlike BCPNN, STDP does not explain the decontextualization process. We further examine how selective plasticity modulation of isolated salient events may enhance preferential retention and resistance to semantization. Our model reproduces important features of episodicity on behavioral timescales under various biological constraints whilst also offering a novel neural and synaptic explanation for semantization, thereby casting new light on the interplay between episodic and semantic memory processes.Significance StatementRemembering single episodes is a fundamental attribute of cognition. Difficulties recollecting contextual information is a key sign of episodic memory loss or semantization. Behavioral studies demonstrate that semantization of episodic memory can occur rapidly, yet the neural mechanisms underlying this effect are insufficiently investigated. In line with recent behavioral findings, we show that multiple stimulus exposures in different contexts may advance item-context decoupling. We suggest a Bayesian-Hebbian synaptic plasticity hypothesis of memory semantization and further show that a transient modulation of plasticity during salient events may disrupt the decontextualization process by strengthening memory traces, and thus, enhancing preferential retention. The proposed cortical network-of-networks model thus bridges micro and mesoscale synaptic effects with network dynamics and behavior.
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Affiliation(s)
- Nikolaos Chrysanthidis
- Division of Computational Science and Technology, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, 10044 Stockholm, Sweden
| | - Florian Fiebig
- Division of Computational Science and Technology, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, 10044 Stockholm, Sweden
| | - Anders Lansner
- Division of Computational Science and Technology, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, 10044 Stockholm, Sweden
- Department of Mathematics, Stockholm University, 10691 Stockholm, Sweden
| | - Pawel Herman
- Division of Computational Science and Technology, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, 10044 Stockholm, Sweden
- Digital Futures, Stockholm, Sweden
- Swedish e-Science Research Centre, Stockholm, Sweden
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29
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Peng X, Lin W. Complex Dynamics of Noise-Perturbed Excitatory-Inhibitory Neural Networks With Intra-Correlative and Inter-Independent Connections. Front Physiol 2022; 13:915511. [PMID: 35812336 PMCID: PMC9263264 DOI: 10.3389/fphys.2022.915511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 05/09/2022] [Indexed: 11/24/2022] Open
Abstract
Real neural system usually contains two types of neurons, i.e., excitatory neurons and inhibitory ones. Analytical and numerical interpretation of dynamics induced by different types of interactions among the neurons of two types is beneficial to understanding those physiological functions of the brain. Here, we articulate a model of noise-perturbed random neural networks containing both excitatory and inhibitory (E&I) populations. Particularly, both intra-correlatively and inter-independently connected neurons in two populations are taken into account, which is different from the most existing E&I models only considering the independently-connected neurons. By employing the typical mean-field theory, we obtain an equivalent system of two dimensions with an input of stationary Gaussian process. Investigating the stationary autocorrelation functions along the obtained system, we analytically find the parameters’ conditions under which the synchronized behaviors between the two populations are sufficiently emergent. Taking the maximal Lyapunov exponent as an index, we also find different critical values of the coupling strength coefficients for the chaotic excitatory neurons and for the chaotic inhibitory ones. Interestingly, we reveal that the noise is able to suppress chaotic dynamics of the random neural networks having neurons in two populations, while an appropriate amount of correlation coefficient in intra-coupling strengths can enhance chaos occurrence. Finally, we also detect a previously-reported phenomenon where the parameters region corresponds to neither linearly stable nor chaotic dynamics; however, the size of the region area crucially depends on the populations’ parameters.
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Affiliation(s)
- Xiaoxiao Peng
- Shanghai Center for Mathematical Sciences, School of Mathematical Sciences, and LMNS, Fudan University, Shanghai, China
- Research Institute of Intelligent Complex Systemsand Center for Computational Systems Biology, Fudan University, Shanghai, China
- *Correspondence: Xiaoxiao Peng, ; Wei Lin,
| | - Wei Lin
- Shanghai Center for Mathematical Sciences, School of Mathematical Sciences, and LMNS, Fudan University, Shanghai, China
- Research Institute of Intelligent Complex Systemsand Center for Computational Systems Biology, Fudan University, Shanghai, China
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, and Institutes of Brain Science, Fudan University, Shanghai, China
- *Correspondence: Xiaoxiao Peng, ; Wei Lin,
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30
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Affiliation(s)
- Johannes Alt
- Section of Mathematics, University of Geneva, 24, rue du Général Dufour, Case postale 64, 1211 Genève 4, Switzerland
| | - Torben Krüger
- Department of Mathematical Sciences, University of Copenhagen, Universitetsparken 5, 2100 København, Denmark
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31
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Constraints on Persistent Activity in a Biologically Detailed Network Model of the Prefrontal Cortex with Heterogeneities. Prog Neurobiol 2022; 215:102287. [DOI: 10.1016/j.pneurobio.2022.102287] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Revised: 02/25/2022] [Accepted: 05/04/2022] [Indexed: 11/18/2022]
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32
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Attili SM, Moradi K, Wheeler DW, Ascoli GA. Quantification of neuron types in the rodent hippocampal formation by data mining and numerical optimization. Eur J Neurosci 2022; 55:1724-1741. [PMID: 35301768 PMCID: PMC10026515 DOI: 10.1111/ejn.15639] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 01/25/2022] [Accepted: 02/28/2022] [Indexed: 11/29/2022]
Abstract
Quantifying the population sizes of distinct neuron types in different anatomical regions is an essential step towards establishing a brain cell census. Although estimates exist for the total neuronal populations in different species, the number and definition of each specific neuron type are still intensively investigated. Hippocampome.org is an open-source knowledge base with morphological, physiological and molecular information for 122 neuron types in the rodent hippocampal formation. While such framework identifies all known neuron types in this system, their relative abundances remain largely unknown. This work quantitatively estimates the counts of all Hippocampome.org neuron types by literature mining and numerical optimization. We report the number of neurons in each type identified by main neurotransmitter (glutamate or GABA) and axonal-dendritic patterns throughout 26 subregions and layers of the dentate gyrus, Ammon's horn, subiculum and entorhinal cortex. We produce by sensitivity analysis reliable numerical ranges for each type and summarize the amounts across broad neuronal families defined by biomarkers expression and firing dynamics. Study of density distributions indicates that the number of dendritic-targeting interneurons, but not of other neuronal classes, is independent of anatomical volumes. All extracted values, experimental evidence and related software code are released on Hippocampome.org.
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Affiliation(s)
- Sarojini M Attili
- Center for Neural Informatics, Structures, and Plasticity, Interdisciplinary Neuroscience Program, Krasnow Institute for Advanced Study, Fairfax, Virginia, USA
| | - Keivan Moradi
- Center for Neural Informatics, Structures, and Plasticity, Interdisciplinary Neuroscience Program, Krasnow Institute for Advanced Study, Fairfax, Virginia, USA
| | - Diek W Wheeler
- Bioengineering Department and Volgenau School of Engineering, George Mason University, Fairfax, Virginia, USA
| | - Giorgio A Ascoli
- Center for Neural Informatics, Structures, and Plasticity, Interdisciplinary Neuroscience Program, Krasnow Institute for Advanced Study, Fairfax, Virginia, USA
- Bioengineering Department and Volgenau School of Engineering, George Mason University, Fairfax, Virginia, USA
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33
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Grossman CD, Cohen JY. Neuromodulation and Neurophysiology on the Timescale of Learning and Decision-Making. Annu Rev Neurosci 2022; 45:317-337. [PMID: 35363533 DOI: 10.1146/annurev-neuro-092021-125059] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Nervous systems evolved to effectively navigate the dynamics of the environment to achieve their goals. One framework used to study this fundamental problem arose in the study of learning and decision-making. In this framework, the demands of effective behavior require slow dynamics-on the scale of seconds to minutes-of networks of neurons. Here, we review the phenomena and mechanisms involved. Using vignettes from a few species and areas of the nervous system, we view neuromodulators as key substrates for temporal scaling of neuronal dynamics. Expected final online publication date for the Annual Review of Neuroscience, Volume 45 is July 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Cooper D Grossman
- The Solomon H. Snyder Department of Neuroscience, Brain Science Institute, and Kavli Neuroscience Discovery Institute, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA;
| | - Jeremiah Y Cohen
- The Solomon H. Snyder Department of Neuroscience, Brain Science Institute, and Kavli Neuroscience Discovery Institute, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA;
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Turner NL, Macrina T, Bae JA, Yang R, Wilson AM, Schneider-Mizell C, Lee K, Lu R, Wu J, Bodor AL, Bleckert AA, Brittain D, Froudarakis E, Dorkenwald S, Collman F, Kemnitz N, Ih D, Silversmith WM, Zung J, Zlateski A, Tartavull I, Yu SC, Popovych S, Mu S, Wong W, Jordan CS, Castro M, Buchanan J, Bumbarger DJ, Takeno M, Torres R, Mahalingam G, Elabbady L, Li Y, Cobos E, Zhou P, Suckow S, Becker L, Paninski L, Polleux F, Reimer J, Tolias AS, Reid RC, da Costa NM, Seung HS. Reconstruction of neocortex: Organelles, compartments, cells, circuits, and activity. Cell 2022; 185:1082-1100.e24. [PMID: 35216674 PMCID: PMC9337909 DOI: 10.1016/j.cell.2022.01.023] [Citation(s) in RCA: 77] [Impact Index Per Article: 38.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Revised: 07/26/2021] [Accepted: 01/27/2022] [Indexed: 12/31/2022]
Abstract
We assembled a semi-automated reconstruction of L2/3 mouse primary visual cortex from ∼250 × 140 × 90 μm3 of electron microscopic images, including pyramidal and non-pyramidal neurons, astrocytes, microglia, oligodendrocytes and precursors, pericytes, vasculature, nuclei, mitochondria, and synapses. Visual responses of a subset of pyramidal cells are included. The data are publicly available, along with tools for programmatic and three-dimensional interactive access. Brief vignettes illustrate the breadth of potential applications relating structure to function in cortical circuits and neuronal cell biology. Mitochondria and synapse organization are characterized as a function of path length from the soma. Pyramidal connectivity motif frequencies are predicted accurately using a configuration model of random graphs. Pyramidal cells receiving more connections from nearby cells exhibit stronger and more reliable visual responses. Sample code shows data access and analysis.
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Affiliation(s)
- Nicholas L Turner
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA
| | - Thomas Macrina
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA
| | - J Alexander Bae
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Electrical and Computer Engineering Department, Princeton University, Princeton, NJ 08544, USA
| | - Runzhe Yang
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA
| | - Alyssa M Wilson
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | | | - Kisuk Lee
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Brain & Cognitive Sciences Department, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Ran Lu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Jingpeng Wu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Agnes L Bodor
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | | | - Emmanouil Froudarakis
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX 77030, USA
| | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA
| | | | - Nico Kemnitz
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Dodam Ih
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | | | - Jonathan Zung
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA
| | - Aleksandar Zlateski
- Electrical Engineering and Computer Science Department, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Ignacio Tartavull
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Szi-Chieh Yu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Sergiy Popovych
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA
| | - Shang Mu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - William Wong
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Chris S Jordan
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Manuel Castro
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - JoAnn Buchanan
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Marc Takeno
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Russel Torres
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Leila Elabbady
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Yang Li
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Erick Cobos
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX 77030, USA
| | - Pengcheng Zhou
- Department of Statistics, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA; Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA
| | - Shelby Suckow
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Lynne Becker
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Liam Paninski
- Department of Statistics, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA; Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Department of Neuroscience, Columbia University, New York, NY 10027, USA; Kavli Institute for Brain Science at Columbia University, New York, NY 10027, USA
| | - Franck Polleux
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Department of Neuroscience, Columbia University, New York, NY 10027, USA; Kavli Institute for Brain Science at Columbia University, New York, NY 10027, USA
| | - Jacob Reimer
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX 77030, USA
| | - Andreas S Tolias
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX 77030, USA; Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA
| | - R Clay Reid
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - H Sebastian Seung
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA.
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35
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Input rate encoding and gain control in dendrites of neocortical pyramidal neurons. Cell Rep 2022; 38:110382. [PMID: 35172157 PMCID: PMC8967317 DOI: 10.1016/j.celrep.2022.110382] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 11/15/2021] [Accepted: 01/23/2022] [Indexed: 01/06/2023] Open
Abstract
Elucidating how neurons encode network activity is essential to understanding how the brain processes information. Neocortical pyramidal cells receive excitatory input onto spines distributed along dendritic branches. Local dendritic branch nonlinearities can boost the response to spatially clustered and synchronous input, but how this translates into the integration of patterns of ongoing activity remains unclear. To examine dendritic integration under naturalistic stimulus regimes, we use two-photon glutamate uncaging to repeatedly activate multiple dendritic spines at random intervals. In the proximal dendrites of two populations of layer 5 pyramidal neurons in the mouse motor cortex, spatially restricted synchrony is not a prerequisite for dendritic boosting. Branches encode afferent inputs with distinct rate sensitivities depending upon cell and branch type. Thus, inputs distributed along a dendritic branch can recruit supralinear boosting and the window of this nonlinearity may provide a mechanism by which dendrites can preferentially amplify slow-frequency network oscillations.
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Short-Term Synaptic Plasticity: Microscopic Modelling and (Some) Computational Implications. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1359:105-121. [DOI: 10.1007/978-3-030-89439-9_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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37
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Yan Z, Rein B. Mechanisms of synaptic transmission dysregulation in the prefrontal cortex: pathophysiological implications. Mol Psychiatry 2022; 27:445-465. [PMID: 33875802 PMCID: PMC8523584 DOI: 10.1038/s41380-021-01092-3] [Citation(s) in RCA: 113] [Impact Index Per Article: 56.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Revised: 03/13/2021] [Accepted: 03/29/2021] [Indexed: 02/02/2023]
Abstract
The prefrontal cortex (PFC) serves as the chief executive officer of the brain, controlling the highest level cognitive and emotional processes. Its local circuits among glutamatergic principal neurons and GABAergic interneurons, as well as its long-range connections with other brain regions, have been functionally linked to specific behaviors, ranging from working memory to reward seeking. The efficacy of synaptic signaling in the PFC network is profundedly influenced by monoaminergic inputs via the activation of dopamine, adrenergic, or serotonin receptors. Stress hormones and neuropeptides also exert complex effects on the synaptic structure and function of PFC neurons. Dysregulation of PFC synaptic transmission is strongly linked to social deficits, affective disturbance, and memory loss in brain disorders, including autism, schizophrenia, depression, and Alzheimer's disease. Critical neural circuits, biological pathways, and molecular players that go awry in these mental illnesses have been revealed by integrated electrophysiological, optogenetic, biochemical, and transcriptomic studies of PFC. Novel epigenetic mechanism-based strategies are proposed as potential avenues of therapeutic intervention for PFC-involved diseases. This review provides an overview of PFC network organization and synaptic modulation, as well as the mechanisms linking PFC dysfunction to the pathophysiology of neurodevelopmental, neuropsychiatric, and neurodegenerative diseases. Insights from the preclinical studies offer the potential for discovering new medical treatments for human patients with these brain disorders.
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Affiliation(s)
- Zhen Yan
- Department of Physiology and Biophysics, State University of New York at Buffalo, Jacobs School of Medicine and Biomedical Sciences, Buffalo, NY, USA.
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38
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Kasanetz F, Nevian T. Increased burst coding in deep layers of the ventral anterior cingulate cortex during neuropathic pain. Sci Rep 2021; 11:24240. [PMID: 34930957 PMCID: PMC8688462 DOI: 10.1038/s41598-021-03652-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 12/08/2021] [Indexed: 11/27/2022] Open
Abstract
Neuropathic pain induces changes in neuronal excitability and synaptic connectivity in deep layers of the anterior cingulate cortex (ACC) that play a central role in the sensory, emotional and affective consequences of the disease. However, how this impacts ACC in vivo activity is not completely understood. Using a mouse model, we found that neuropathic pain caused an increase in ACC in vivo activity, as measured by the indirect activity marker c-Fos and juxtacellular electrophysiological recordings. The enhanced firing rate of ACC neurons in lesioned animals was based on a change in the firing pattern towards bursting activity. Despite the proportion of ACC neurons recruited by noxious stimuli was unchanged during neuropathic pain, responses to noxious stimuli were characterized by increased bursting. Thus, this change in coding pattern may have important implications for the processing of nociceptive information in the ACC and could be of great interest to guide the search for new treatment strategies for chronic pain.
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Affiliation(s)
- Fernando Kasanetz
- Department of Physiology, University of Bern, Bühlplatz 5, 3012, Bern, Switzerland.
- Grupo de Neurociencias de Sistemas, IFIBIO Houssay - CONICET, Universidad de Buenos Aires, Paraguay 2155 piso 7, (1121), Buenos Aires, Argentina.
| | - Thomas Nevian
- Department of Physiology, University of Bern, Bühlplatz 5, 3012, Bern, Switzerland.
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39
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Stein H, Barbosa J, Compte A. Towards biologically constrained attractor models of schizophrenia. Curr Opin Neurobiol 2021; 70:171-181. [PMID: 34839146 DOI: 10.1016/j.conb.2021.10.013] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 10/19/2021] [Accepted: 10/27/2021] [Indexed: 12/31/2022]
Abstract
Alterations in neuromodulation or synaptic transmission in biophysical attractor network models, as proposed by the dominant dopaminergic and glutamatergic theories of schizophrenia, successfully mimic working memory (WM) deficits in people with schizophrenia (PSZ). Yet, multiple, often opposing alterations in memory circuits can lead to the same behavioral patterns in these network models. Here, we critically revise the computational and experimental literature that links NMDAR hypofunction to WM precision loss in PSZ. We show in network simulations that currently available experimental evidence cannot set apart competing biophysical accounts. Critical points to resolve are the effects of increases vs. decreases in E/I ratio (e.g. through NMDAR blockade) on firing rate tuning and shared noise modulations and possible concomitant deficits in short-term plasticity. We argue that these concerted experimental and computational efforts will lead to a better understanding of the neurobiology underlying cognitive deficits in PSZ.
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Affiliation(s)
- Heike Stein
- Laboratoire de Neurosciences Cognitives et Computationnelles, Département d'Études Cognitives, École Normale Supérieure, INSERM U960, PSL University, Paris, France
| | - Joao Barbosa
- Laboratoire de Neurosciences Cognitives et Computationnelles, Département d'Études Cognitives, École Normale Supérieure, INSERM U960, PSL University, Paris, France
| | - Albert Compte
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.
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40
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Liu Y, Li Q, Tang C, Qin S, Tu Y. Short-Term Plasticity Regulates Both Divisive Normalization and Adaptive Responses in Drosophila Olfactory System. Front Comput Neurosci 2021; 15:730431. [PMID: 34744674 PMCID: PMC8568954 DOI: 10.3389/fncom.2021.730431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 09/23/2021] [Indexed: 11/23/2022] Open
Abstract
In Drosophila, olfactory information received by olfactory receptor neurons (ORNs) is first processed by an incoherent feed forward neural circuit in the antennal lobe (AL) that consists of ORNs (input), inhibitory local neurons (LNs), and projection neurons (PNs). This “early” olfactory information processing has two important characteristics. First, response of a PN to its cognate ORN is normalized by the overall activity of other ORNs, a phenomenon termed “divisive normalization.” Second, PNs respond strongly to the onset of ORN activities, but they adapt to prolonged or continuously varying inputs. Despite the importance of these characteristics for learning and memory, their underlying mechanisms are not fully understood. Here, we develop a circuit model for describing the ORN-LN-PN dynamics by including key neuron-neuron interactions such as short-term plasticity (STP) and presynaptic inhibition (PI). By fitting our model to experimental data quantitatively, we show that a strong STP balanced between short-term facilitation (STF) and short-term depression (STD) is responsible for the observed nonlinear divisive normalization in Drosophila. Our circuit model suggests that either STP or PI alone can lead to adaptive response. However, by comparing our model results with experimental data, we find that both STP and PI work together to achieve a strong and robust adaptive response. Our model not only helps reveal the mechanisms underlying two main characteristics of the early olfactory process, it can also be used to predict PN responses to arbitrary time-dependent signals and to infer microscopic properties of the circuit (such as the strengths of STF and STD) from the measured input-output relation. Our circuit model may be useful for understanding the role of STP in other sensory systems.
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Affiliation(s)
- Yuxuan Liu
- School of Physics, Peking University, Beijing, China
| | - Qianyi Li
- Integrated Science Program, Yuanpei College, Peking University, Beijing, China.,Biophysics Graduate Program, Harvard University, Cambridge, MA, United States
| | - Chao Tang
- School of Physics, Peking University, Beijing, China.,Center for Quantitative Biology, Peking University, Beijing, China.,Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Shanshan Qin
- Center for Quantitative Biology, Peking University, Beijing, China.,John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, United States
| | - Yuhai Tu
- Physical Sciences Department, IBM T. J. Watson Research Center, Yorktown Heights, NY, United States
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41
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Wang XJ. 50 years of mnemonic persistent activity: quo vadis? Trends Neurosci 2021; 44:888-902. [PMID: 34654556 PMCID: PMC9087306 DOI: 10.1016/j.tins.2021.09.001] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 08/27/2021] [Accepted: 09/07/2021] [Indexed: 10/20/2022]
Abstract
Half a century ago persistent spiking activity in the neocortex was discovered to be a neural substrate of working memory. Since then scientists have sought to understand this core cognitive function across biological and computational levels. Studies are reviewed here that cumulatively lend support to a synaptic theory of recurrent circuits for mnemonic persistent activity that depends on various cellular and network substrates and is mathematically described by a multiple-attractor network model. Crucially, a mnemonic attractor state of the brain is consistent with temporal variations and heterogeneity across neurons in a subspace of population activity. Persistent activity should be broadly understood as a contrast to decaying transients. Mechanisms in the absence of neural firing ('activity-silent state') are suitable for passive short-term memory but not for working memory - which is characterized by executive control for filtering out distractors, limited capacity, and internal manipulation of information.
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Affiliation(s)
- Xiao-Jing Wang
- Center for Neural Science, New York University, 4 Washington Place, New York, NY 20003, USA.
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42
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Gerlei KZ, Brown CM, Sürmeli G, Nolan MF. Deep entorhinal cortex: from circuit organization to spatial cognition and memory. Trends Neurosci 2021; 44:876-887. [PMID: 34593254 DOI: 10.1016/j.tins.2021.08.003] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 08/05/2021] [Accepted: 08/08/2021] [Indexed: 10/20/2022]
Abstract
The deep layers of the entorhinal cortex are important for spatial cognition, as well as memory storage, consolidation and retrieval. A long-standing hypothesis is that deep-layer neurons relay spatial and memory-related signals between the hippocampus and telencephalon. We review the implications of recent circuit-level analyses that suggest more complex roles. The organization of deep entorhinal layers is consistent with multi-stage processing by specialized cell populations; in this framework, hippocampal, neocortical, and subcortical inputs are integrated to generate representations for use by targets in the telencephalon and for feedback to the superficial entorhinal cortex and hippocampus. Addressing individual sublayers of the deep entorhinal cortex in future experiments and models will be important for establishing systems-level mechanisms for spatial cognition and episodic memory.
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Affiliation(s)
- Klára Z Gerlei
- Centre for Discovery Brain Sciences, University of Edinburgh, Hugh Robson Building, George Square, Edinburgh EH8 9XD, UK
| | - Christina M Brown
- Centre for Discovery Brain Sciences, University of Edinburgh, Hugh Robson Building, George Square, Edinburgh EH8 9XD, UK
| | - Gülşen Sürmeli
- Centre for Discovery Brain Sciences, University of Edinburgh, Hugh Robson Building, George Square, Edinburgh EH8 9XD, UK; Simons Initiative for the Developing Brain, University of Edinburgh, Hugh Robson Building, George Square, Edinburgh EH8 9XD, UK
| | - Matthew F Nolan
- Centre for Discovery Brain Sciences, University of Edinburgh, Hugh Robson Building, George Square, Edinburgh EH8 9XD, UK; Simons Initiative for the Developing Brain, University of Edinburgh, Hugh Robson Building, George Square, Edinburgh EH8 9XD, UK.
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43
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Gutman-Wei AY, Brown SP. Mechanisms Underlying Target Selectivity for Cell Types and Subcellular Domains in Developing Neocortical Circuits. Front Neural Circuits 2021; 15:728832. [PMID: 34630048 PMCID: PMC8497978 DOI: 10.3389/fncir.2021.728832] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 08/25/2021] [Indexed: 11/25/2022] Open
Abstract
The cerebral cortex contains numerous neuronal cell types, distinguished by their molecular identity as well as their electrophysiological and morphological properties. Cortical function is reliant on stereotyped patterns of synaptic connectivity and synaptic function among these neuron types, but how these patterns are established during development remains poorly understood. Selective targeting not only of different cell types but also of distinct postsynaptic neuronal domains occurs in many brain circuits and is directed by multiple mechanisms. These mechanisms include the regulation of axonal and dendritic guidance and fine-scale morphogenesis of pre- and postsynaptic processes, lineage relationships, activity dependent mechanisms and intercellular molecular determinants such as transmembrane and secreted molecules, many of which have also been implicated in neurodevelopmental disorders. However, many studies of synaptic targeting have focused on circuits in which neuronal processes target different lamina, such that cell-type-biased connectivity may be confounded with mechanisms of laminar specificity. In the cerebral cortex, each cortical layer contains cell bodies and processes from intermingled neuronal cell types, an arrangement that presents a challenge for the development of target-selective synapse formation. Here, we address progress and future directions in the study of cell-type-biased synaptic targeting in the cerebral cortex. We highlight challenges to identifying developmental mechanisms generating stereotyped patterns of intracortical connectivity, recent developments in uncovering the determinants of synaptic target selection during cortical synapse formation, and current gaps in the understanding of cortical synapse specificity.
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Affiliation(s)
- Alan Y. Gutman-Wei
- Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Solange P. Brown
- Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Kavli Neuroscience Discovery Institute, Johns Hopkins University School of Medicine, Baltimore, MD, United States
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44
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Adaptation supports short-term memory in a visual change detection task. PLoS Comput Biol 2021; 17:e1009246. [PMID: 34534203 PMCID: PMC8480767 DOI: 10.1371/journal.pcbi.1009246] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 09/29/2021] [Accepted: 07/03/2021] [Indexed: 11/19/2022] Open
Abstract
The maintenance of short-term memories is critical for survival in a dynamically changing world. Previous studies suggest that this memory can be stored in the form of persistent neural activity or using a synaptic mechanism, such as with short-term plasticity. Here, we compare the predictions of these two mechanisms to neural and behavioral measurements in a visual change detection task. Mice were trained to respond to changes in a repeated sequence of natural images while neural activity was recorded using two-photon calcium imaging. We also trained two types of artificial neural networks on the same change detection task as the mice. Following fixed pre-processing using a pretrained convolutional neural network, either a recurrent neural network (RNN) or a feedforward neural network with short-term synaptic depression (STPNet) was trained to the same level of performance as the mice. While both networks are able to learn the task, the STPNet model contains units whose activity are more similar to the in vivo data and produces errors which are more similar to the mice. When images are omitted, an unexpected perturbation which was absent during training, mice often do not respond to the omission but are more likely to respond to the subsequent image. Unlike the RNN model, STPNet produces a similar pattern of behavior. These results suggest that simple neural adaptation mechanisms may serve as an important bottom-up memory signal in this task, which can be used by downstream areas in the decision-making process. Animals have to adapt to environments with rich dynamics and maintain multiple types of memories. In this study, we focus on a visual change detection task in mice which requires short-term memory. Learning which features need to be maintained in short-term memory can be realized in a recurrent neural network by changing connections in the network, resulting in memory maintenance through persistent activity. However, in biological networks, a large diversity of time-dependent intrinsic mechanisms are also available. As an alternative to persistent neural activity, we find that learning to make use of internal adapting dynamics better matches both the observed neural activity and behavior of animals in this simple task. The presence of a large diversity of temporal traces could be one of the reasons for the diversity of cells observed. We believe that both learning to keep representations of relevant stimuli in persistent activity and learning to make use of intrinsic time-dependent mechanisms exist, and their relative use will be dependent on the exact task.
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45
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Myers JC, Chinn LK, Sur S, Golob EJ. Widespread theta coherence during spatial cognitive control. Neuropsychologia 2021; 160:107979. [PMID: 34339719 DOI: 10.1016/j.neuropsychologia.2021.107979] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Revised: 07/06/2021] [Accepted: 07/25/2021] [Indexed: 11/28/2022]
Abstract
Cognitive control allows humans to process relevant sensory information while minimizing distractions from irrelevant stimuli. The neural basis of cognitive control is known to involve frontal regions of the brain such as the medial prefrontal cortex (mPFC) and anterior cingulate cortex (ACC), but the temporal dynamics of larger scale networks is unclear. Here we used EEG with source localization to identify how the neural oscillations localized to the mPFC and ACC coordinate with parietal, sensory, and motor areas during spatial cognitive control. Theta coherence (3-8 Hz) between the mPFC and ACC increased with task difficulty and predicted individual differences in reaction time. Individual differences in accuracy were predicted by earlier activation of ACC-motor coherence, highlighting the relationship between processing speed and task performance. Our results provide evidence that successful cognitive control requires dynamic coordination between a widespread network of brain regions. Long range theta coherence may be a key mechanism for efficient cognitive control across the neocortex.
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Affiliation(s)
- John C Myers
- Department of Psychology, Tulane University, New Orleans, LA, 70118, USA; Department of Psychology, University of Texas, San Antonio, TX, 78249, USA; Department of Neurosurgery, Baylor College of Medicine, Houston, TX, 77030, USA.
| | - Lisa K Chinn
- Department of Psychology, Tulane University, New Orleans, LA, 70118, USA; Texas Institute for Measurement, Evaluation, and Statistics, University of Houston, Houston, TX, 77004, USA
| | - Sandeepa Sur
- Department of Radiology, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Edward J Golob
- Department of Psychology, Tulane University, New Orleans, LA, 70118, USA; Department of Psychology, University of Texas, San Antonio, TX, 78249, USA
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Salaj D, Subramoney A, Kraisnikovic C, Bellec G, Legenstein R, Maass W. Spike frequency adaptation supports network computations on temporally dispersed information. eLife 2021; 10:e65459. [PMID: 34310281 PMCID: PMC8313230 DOI: 10.7554/elife.65459] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 06/29/2021] [Indexed: 11/13/2022] Open
Abstract
For solving tasks such as recognizing a song, answering a question, or inverting a sequence of symbols, cortical microcircuits need to integrate and manipulate information that was dispersed over time during the preceding seconds. Creating biologically realistic models for the underlying computations, especially with spiking neurons and for behaviorally relevant integration time spans, is notoriously difficult. We examine the role of spike frequency adaptation in such computations and find that it has a surprisingly large impact. The inclusion of this well-known property of a substantial fraction of neurons in the neocortex - especially in higher areas of the human neocortex - moves the performance of spiking neural network models for computations on network inputs that are temporally dispersed from a fairly low level up to the performance level of the human brain.
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Affiliation(s)
- Darjan Salaj
- Institute of Theoretical Computer Science, Graz University of TechnologyGrazAustria
| | - Anand Subramoney
- Institute of Theoretical Computer Science, Graz University of TechnologyGrazAustria
| | - Ceca Kraisnikovic
- Institute of Theoretical Computer Science, Graz University of TechnologyGrazAustria
| | - Guillaume Bellec
- Institute of Theoretical Computer Science, Graz University of TechnologyGrazAustria
- Laboratory of Computational Neuroscience, Ecole Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland
| | - Robert Legenstein
- Institute of Theoretical Computer Science, Graz University of TechnologyGrazAustria
| | - Wolfgang Maass
- Institute of Theoretical Computer Science, Graz University of TechnologyGrazAustria
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Curtis CE, Sprague TC. Persistent Activity During Working Memory From Front to Back. Front Neural Circuits 2021; 15:696060. [PMID: 34366794 PMCID: PMC8334735 DOI: 10.3389/fncir.2021.696060] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 06/28/2021] [Indexed: 01/06/2023] Open
Abstract
Working memory (WM) extends the duration over which information is available for processing. Given its importance in supporting a wide-array of high level cognitive abilities, uncovering the neural mechanisms that underlie WM has been a primary goal of neuroscience research over the past century. Here, we critically review what we consider the two major "arcs" of inquiry, with a specific focus on findings that were theoretically transformative. For the first arc, we briefly review classic studies that led to the canonical WM theory that cast the prefrontal cortex (PFC) as a central player utilizing persistent activity of neurons as a mechanism for memory storage. We then consider recent challenges to the theory regarding the role of persistent neural activity. The second arc, which evolved over the last decade, stemmed from sophisticated computational neuroimaging approaches enabling researchers to decode the contents of WM from the patterns of neural activity in many parts of the brain including early visual cortex. We summarize key findings from these studies, their implications for WM theory, and finally the challenges these findings pose. Our goal in doing so is to identify barriers to developing a comprehensive theory of WM that will require a unification of these two "arcs" of research.
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Affiliation(s)
- Clayton E. Curtis
- Department of Psychology, New York University, New York, NY, United States
- Center for Neural Science, New York University, New York, NY, United States
| | - Thomas C. Sprague
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, Santa Barbara, CA, United States
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48
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Guzman E, Cheng Z, Hansma PK, Tovar KR, Petzold LR, Kosik KS. Extracellular detection of neuronal coupling. Sci Rep 2021; 11:14733. [PMID: 34282275 PMCID: PMC8289866 DOI: 10.1038/s41598-021-94282-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 07/07/2021] [Indexed: 11/09/2022] Open
Abstract
We developed a method to non-invasively detect synaptic relationships among neurons from in vitro networks. Our method uses microelectrode arrays on which neurons are cultured and from which propagation of extracellular action potentials (eAPs) in single axons are recorded at multiple electrodes. Detecting eAP propagation bypasses ambiguity introduced by spike sorting. Our methods identify short latency spiking relationships between neurons with properties expected of synaptically coupled neurons, namely they were recapitulated by direct stimulation and were sensitive to changing the number of active synaptic sites. Our methods enabled us to assemble a functional subset of neuronal connectivity in our cultures.
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Affiliation(s)
- Elmer Guzman
- Department of Molecular, Cellular and Developmental Biology, University of California, Santa Barbara, Santa Barbara, CA, USA.,Neuroscience Research Institute, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Zhuowei Cheng
- Department of Computer Science, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Paul K Hansma
- Department of Physics, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Kenneth R Tovar
- Neuroscience Research Institute, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Linda R Petzold
- Department of Computer Science, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Kenneth S Kosik
- Department of Molecular, Cellular and Developmental Biology, University of California, Santa Barbara, Santa Barbara, CA, USA. .,Neuroscience Research Institute, University of California, Santa Barbara, Santa Barbara, CA, USA.
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49
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Bae JW, Jeong H, Yoon YJ, Bae CM, Lee H, Paik SB, Jung MW. Parallel processing of working memory and temporal information by distinct types of cortical projection neurons. Nat Commun 2021; 12:4352. [PMID: 34272368 PMCID: PMC8285375 DOI: 10.1038/s41467-021-24565-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Accepted: 06/18/2021] [Indexed: 02/06/2023] Open
Abstract
It is unclear how different types of cortical projection neurons work together to support diverse cortical functions. We examined the discharge characteristics and inactivation effects of intratelencephalic (IT) and pyramidal tract (PT) neurons-two major types of cortical excitatory neurons that project to cortical and subcortical structures, respectively-in the deep layer of the medial prefrontal cortex in mice performing a delayed response task. We found stronger target-dependent firing of IT than PT neurons during the delay period. We also found the inactivation of IT neurons, but not PT neurons, impairs behavioral performance. In contrast, PT neurons carry more temporal information than IT neurons during the delay period. Our results indicate a division of labor between IT and PT projection neurons in the prefrontal cortex for the maintenance of working memory and for tracking the passage of time, respectively.
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Affiliation(s)
- Jung Won Bae
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Korea
| | - Huijeong Jeong
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Korea
- Center for Synaptic Brain Dysfunctions, Institute for Basic Science, Daejeon, Korea
| | - Young Ju Yoon
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Korea
| | - Chan Mee Bae
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Korea
- Center for Synaptic Brain Dysfunctions, Institute for Basic Science, Daejeon, Korea
| | - Hyeonsu Lee
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea
| | - Se-Bum Paik
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea
- Program of Brain and Cognitive Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea
| | - Min Whan Jung
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Korea.
- Center for Synaptic Brain Dysfunctions, Institute for Basic Science, Daejeon, Korea.
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50
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Lee SE, Lee GH. Reelin Affects Signaling Pathways of a Group of Inhibitory Neurons and the Development of Inhibitory Synapses in Primary Neurons. Int J Mol Sci 2021; 22:ijms22147510. [PMID: 34299127 PMCID: PMC8305533 DOI: 10.3390/ijms22147510] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 06/26/2021] [Accepted: 07/10/2021] [Indexed: 11/16/2022] Open
Abstract
Reelin is a secretory protein involved in a variety of processes in forebrain development and function, including neuronal migration, dendrite growth, spine formation, and synaptic plasticity. Most of the function of Reelin is focused on excitatory neurons; however, little is known about its effects on inhibitory neurons and inhibitory synapses. In this study, we investigated the phosphatidylinositol 3-kinase/Akt pathway of Reelin in primary cortical and hippocampal neurons. Individual neurons were visualized using immunofluorescence to distinguish inhibitory neurons from excitatory neurons. Reelin-rich protein supplementation significantly induced the phosphorylation of Akt and ribosomal S6 protein in excitatory neurons, but not in most inhibitory neurons. In somatostatin-expressing inhibitory neurons, one of major subtypes of inhibitory neurons, Reelin-rich protein supplementation induced the phosphorylation of S6. Subsequently, we investigated whether or not Reelin-rich protein supplementation affected dendrite development in cultured inhibitory neurons. Reelin-rich protein supplementation did not change the total length of dendrites in inhibitory neurons in vitro. Finally, we examined the development of inhibitory synapses in primary hippocampal neurons and found that Reelin-rich protein supplementation significantly reduced the density of gephyrin-VGAT-positive clusters in the dendritic regions without changing the expression levels of several inhibitory synapse-related proteins. These findings indicate a new role for Reelin in specific groups of inhibitory neurons and the development of inhibitory synapses, which may contribute to the underlying cellular mechanisms of RELN-associated neurological disorders.
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