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Redman WT, Acosta-Mendoza S, Wei XX, Goard MJ. Robust variability of grid cell properties within individual grid modules enhances encoding of local space. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.27.582373. [PMID: 38915504 PMCID: PMC11195105 DOI: 10.1101/2024.02.27.582373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Although grid cells are one of the most well studied functional classes of neurons in the mammalian brain, the assumption that there is a single grid orientation and spacing per grid module has not been carefully tested. We investigate and analyze a recent large-scale recording of medial entorhinal cortex to characterize the presence and degree of heterogeneity of grid properties within individual modules. We find evidence for small, but robust, variability and hypothesize that this property of the grid code could enhance the ability of encoding local spatial information. Performing analysis on synthetic populations of grid cells, where we have complete control over the amount heterogeneity in grid properties, we demonstrate that variability, of a similar magnitude to the analyzed data, leads to significantly decreased decoding error, even when restricted to activity from a single module. Our results highlight how the heterogeneity of the neural response properties may benefit coding and opens new directions for theoretical and experimental analysis of grid cells.
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Affiliation(s)
- William T Redman
- Interdepartmental Graduate Program in Dynamical Neuroscience, University of California, Santa Barbara
- Intelligent Systems Center, Johns Hopkins University Applied Physics Lab
| | - Santiago Acosta-Mendoza
- Interdepartmental Graduate Program in Dynamical Neuroscience, University of California, Santa Barbara
| | - Xue-Xin Wei
- Department of Neuroscience, The University of Texas at Austin
- Department of Psychology, The University of Texas at Austin
- Center for Perceptual Systems, The University of Texas at Austin
- Center for Theoretical and Computational Neuroscience, The University of Texas at Austin
| | - Michael J Goard
- Department of Psychological and Brain Sciences, University of California, Santa Barbara
- Department of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara
- Neuroscience Research Institute, University of California Santa Barbara
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2
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Chen X, Yang Q, Wu J, Li H, Tan KC. A Hybrid Neural Coding Approach for Pattern Recognition With Spiking Neural Networks. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2024; 46:3064-3078. [PMID: 38055367 DOI: 10.1109/tpami.2023.3339211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/08/2023]
Abstract
Recently, brain-inspired spiking neural networks (SNNs) have demonstrated promising capabilities in solving pattern recognition tasks. However, these SNNs are grounded on homogeneous neurons that utilize a uniform neural coding for information representation. Given that each neural coding scheme possesses its own merits and drawbacks, these SNNs encounter challenges in achieving optimal performance such as accuracy, response time, efficiency, and robustness, all of which are crucial for practical applications. In this study, we argue that SNN architectures should be holistically designed to incorporate heterogeneous coding schemes. As an initial exploration in this direction, we propose a hybrid neural coding and learning framework, which encompasses a neural coding zoo with diverse neural coding schemes discovered in neuroscience. Additionally, it incorporates a flexible neural coding assignment strategy to accommodate task-specific requirements, along with novel layer-wise learning methods to effectively implement hybrid coding SNNs. We demonstrate the superiority of the proposed framework on image classification and sound localization tasks. Specifically, the proposed hybrid coding SNNs achieve comparable accuracy to state-of-the-art SNNs, while exhibiting significantly reduced inference latency and energy consumption, as well as high noise robustness. This study yields valuable insights into hybrid neural coding designs, paving the way for developing high-performance neuromorphic systems.
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3
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Mohammadi M, Carriot J, Mackrous I, Cullen KE, Chacron MJ. Neural populations within macaque early vestibular pathways are adapted to encode natural self-motion. PLoS Biol 2024; 22:e3002623. [PMID: 38687807 PMCID: PMC11086886 DOI: 10.1371/journal.pbio.3002623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 05/10/2024] [Accepted: 04/11/2024] [Indexed: 05/02/2024] Open
Abstract
How the activities of large neural populations are integrated in the brain to ensure accurate perception and behavior remains a central problem in systems neuroscience. Here, we investigated population coding of naturalistic self-motion by neurons within early vestibular pathways in rhesus macaques (Macacca mulatta). While vestibular neurons displayed similar dynamic tuning to self-motion, inspection of their spike trains revealed significant heterogeneity. Further analysis revealed that, during natural but not artificial stimulation, heterogeneity resulted primarily from variability across neurons as opposed to trial-to-trial variability. Interestingly, vestibular neurons displayed different correlation structures during naturalistic and artificial self-motion. Specifically, while correlations due to the stimulus (i.e., signal correlations) did not differ, correlations between the trial-to-trial variabilities of neural responses (i.e., noise correlations) were instead significantly positive during naturalistic but not artificial stimulation. Using computational modeling, we show that positive noise correlations during naturalistic stimulation benefits information transmission by heterogeneous vestibular neural populations. Taken together, our results provide evidence that neurons within early vestibular pathways are adapted to the statistics of natural self-motion stimuli at the population level. We suggest that similar adaptations will be found in other systems and species.
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Affiliation(s)
- Mohammad Mohammadi
- Department of Biological and Biomedical Engineering, McGill University, Montreal, Canada
| | - Jerome Carriot
- Department of Physiology, McGill University, Montreal, Canada
| | | | - Kathleen E. Cullen
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
- Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, Maryland, United States of America
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4
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Guo Y, Lv M, Wang C, Ma J. Energy controls wave propagation in a neural network with spatial stimuli. Neural Netw 2024; 171:1-13. [PMID: 38091753 DOI: 10.1016/j.neunet.2023.11.042] [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: 09/07/2023] [Revised: 10/16/2023] [Accepted: 11/19/2023] [Indexed: 01/29/2024]
Abstract
Nervous system has distinct anisotropy and some intrinsic biophysical properties enable neurons present various firing modes in neural activities. In presence of realistic electromagnetic fields, non-uniform radiation activates these neurons with energy diversity. By using a feasible model, energy function is obtained to predict the growth of synaptic connections of these neurons. Distribution of average value of the Hamilton energy function vs. intensity of noisy disturbance can predict the occurrence of coherence resonance, which the neural activities show high regularity by applying noisy disturbance with moderate intensity. From physical viewpoint, the average energy value has similar role average power for the neuron. Non-uniform spatial disturbance is applied and energy is injected into the neural network, statistical synchronization factor is calculated to predict the network synchronization stability and wave propagation. The intensity for field coupling is adaptively controlled by energy diversity between adjacent neurons. Local energy balance will terminate further growth of the coupling intensity; otherwise, heterogeneity is formed in the network due to energy diversity. Furthermore, memristive channel current is introduced into the neuron model for perceiving the effect of electromagnetic induction and radiation, and a memristive neuron is obtained. The circuit implement of memristive circuit depends on the connection to a magnetic flux-controlled memristor into the mentioned neural circuit in an additive branch circuit. The connection and activation of this memristive neural network are controlled under external spatial electromagnetic radiation by capturing enough field energy. Continuous energy collection and exchange generate energy diversity and synaptic connection is created to regulate the synchronous firing patterns and energy balance.
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Affiliation(s)
- Yitong Guo
- College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, 730050, Gansu, PR China
| | - Mi Lv
- Faculty of Engineering, China University of Petroleum-Beijing at Karamay, Karamay, 834000, Xinjiang, PR China
| | - Chunni Wang
- Department of Physics, Lanzhou University of Technology, Lanzhou, 730050, Gansu, PR China.
| | - Jun Ma
- College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, 730050, Gansu, PR China; Department of Physics, Lanzhou University of Technology, Lanzhou, 730050, Gansu, PR China
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5
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Zheng H, Zheng Z, Hu R, Xiao B, Wu Y, Yu F, Liu X, Li G, Deng L. Temporal dendritic heterogeneity incorporated with spiking neural networks for learning multi-timescale dynamics. Nat Commun 2024; 15:277. [PMID: 38177124 PMCID: PMC10766638 DOI: 10.1038/s41467-023-44614-z] [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: 07/31/2023] [Accepted: 12/21/2023] [Indexed: 01/06/2024] Open
Abstract
It is widely believed the brain-inspired spiking neural networks have the capability of processing temporal information owing to their dynamic attributes. However, how to understand what kind of mechanisms contributing to the learning ability and exploit the rich dynamic properties of spiking neural networks to satisfactorily solve complex temporal computing tasks in practice still remains to be explored. In this article, we identify the importance of capturing the multi-timescale components, based on which a multi-compartment spiking neural model with temporal dendritic heterogeneity, is proposed. The model enables multi-timescale dynamics by automatically learning heterogeneous timing factors on different dendritic branches. Two breakthroughs are made through extensive experiments: the working mechanism of the proposed model is revealed via an elaborated temporal spiking XOR problem to analyze the temporal feature integration at different levels; comprehensive performance benefits of the model over ordinary spiking neural networks are achieved on several temporal computing benchmarks for speech recognition, visual recognition, electroencephalogram signal recognition, and robot place recognition, which shows the best-reported accuracy and model compactness, promising robustness and generalization, and high execution efficiency on neuromorphic hardware. This work moves neuromorphic computing a significant step toward real-world applications by appropriately exploiting biological observations.
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Affiliation(s)
- Hanle Zheng
- Center for Brain Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University, Beijing, China
| | - Zhong Zheng
- Center for Brain Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University, Beijing, China
| | - Rui Hu
- Center for Brain Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University, Beijing, China
| | - Bo Xiao
- Center for Brain Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University, Beijing, China
| | - Yujie Wu
- Institute of Theoretical Computer Science, Graz University of Technology, Graz, Austria
| | - Fangwen Yu
- Center for Brain Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University, Beijing, China
| | - Xue Liu
- Center for Brain Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University, Beijing, China
| | - Guoqi Li
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Lei Deng
- Center for Brain Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University, Beijing, China.
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6
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Hutt A, Trotter D, Pariz A, Valiante TA, Lefebvre J. Diversity-induced trivialization and resilience of neural dynamics. CHAOS (WOODBURY, N.Y.) 2024; 34:013147. [PMID: 38285722 DOI: 10.1063/5.0165773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 01/01/2024] [Indexed: 01/31/2024]
Abstract
Heterogeneity is omnipresent across all living systems. Diversity enriches the dynamical repertoire of these systems but remains challenging to reconcile with their manifest robustness and dynamical persistence over time, a fundamental feature called resilience. To better understand the mechanism underlying resilience in neural circuits, we considered a nonlinear network model, extracting the relationship between excitability heterogeneity and resilience. To measure resilience, we quantified the number of stationary states of this network, and how they are affected by various control parameters. We analyzed both analytically and numerically gradient and non-gradient systems modeled as non-linear sparse neural networks evolving over long time scales. Our analysis shows that neuronal heterogeneity quenches the number of stationary states while decreasing the susceptibility to bifurcations: a phenomenon known as trivialization. Heterogeneity was found to implement a homeostatic control mechanism enhancing network resilience to changes in network size and connection probability by quenching the system's dynamic volatility.
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Affiliation(s)
- Axel Hutt
- MLMS, MIMESIS, Université de Strasbourg, CNRS, Inria, ICube, 67000 Strasbourg, France
| | - Daniel Trotter
- Department of Physics, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
- Krembil Brain Institute, University Health Network, Toronto, Ontario M5T 0S8, Canada
| | - Aref Pariz
- Krembil Brain Institute, University Health Network, Toronto, Ontario M5T 0S8, Canada
- Department of Biology, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
| | - Taufik A Valiante
- Krembil Brain Institute, University Health Network, Toronto, Ontario M5T 0S8, Canada
- Department of Electrical and Computer Engineering, Institute of Medical Science, Institute of Biomedical Engineering, Division of Neurosurgery, Department of Surgery, CRANIA (Center for Advancing Neurotechnological Innovation to Application), Max Planck-University of Toronto Center for Neural Science and Technology, University of Toronto, Toronto, Ontario M5S 3G8, Canada
| | - Jérémie Lefebvre
- Department of Physics, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
- Krembil Brain Institute, University Health Network, Toronto, Ontario M5T 0S8, Canada
- Department of Biology, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
- Department of Mathematics, University of Toronto, Toronto, Ontario M5S 2E4, Canada
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7
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Stern M, Istrate N, Mazzucato L. A reservoir of timescales emerges in recurrent circuits with heterogeneous neural assemblies. eLife 2023; 12:e86552. [PMID: 38084779 PMCID: PMC10810607 DOI: 10.7554/elife.86552] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 12/07/2023] [Indexed: 01/26/2024] Open
Abstract
The temporal activity of many physical and biological systems, from complex networks to neural circuits, exhibits fluctuations simultaneously varying over a large range of timescales. Long-tailed distributions of intrinsic timescales have been observed across neurons simultaneously recorded within the same cortical circuit. The mechanisms leading to this striking temporal heterogeneity are yet unknown. Here, we show that neural circuits, endowed with heterogeneous neural assemblies of different sizes, naturally generate multiple timescales of activity spanning several orders of magnitude. We develop an analytical theory using rate networks, supported by simulations of spiking networks with cell-type specific connectivity, to explain how neural timescales depend on assembly size and show that our model can naturally explain the long-tailed timescale distribution observed in the awake primate cortex. When driving recurrent networks of heterogeneous neural assemblies by a time-dependent broadband input, we found that large and small assemblies preferentially entrain slow and fast spectral components of the input, respectively. Our results suggest that heterogeneous assemblies can provide a biologically plausible mechanism for neural circuits to demix complex temporal input signals by transforming temporal into spatial neural codes via frequency-selective neural assemblies.
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Affiliation(s)
- Merav Stern
- Institute of Neuroscience, University of OregonEugeneUnited States
- Faculty of Medicine, The Hebrew University of JerusalemJerusalemIsrael
| | - Nicolae Istrate
- Institute of Neuroscience, University of OregonEugeneUnited States
- Departments of Physics, University of OregonEugeneUnited States
| | - Luca Mazzucato
- Institute of Neuroscience, University of OregonEugeneUnited States
- Departments of Physics, University of OregonEugeneUnited States
- Mathematics and Biology, University of OregonEugeneUnited States
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8
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Choudhary A, Radhakrishnan A, Lindner JF, Sinha S, Ditto WL. Neuronal diversity can improve machine learning for physics and beyond. Sci Rep 2023; 13:13962. [PMID: 37634029 PMCID: PMC10460398 DOI: 10.1038/s41598-023-40766-6] [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: 04/12/2023] [Accepted: 08/16/2023] [Indexed: 08/28/2023] Open
Abstract
Diversity conveys advantages in nature, yet homogeneous neurons typically comprise the layers of artificial neural networks. Here we construct neural networks from neurons that learn their own activation functions, quickly diversify, and subsequently outperform their homogeneous counterparts on image classification and nonlinear regression tasks. Sub-networks instantiate the neurons, which meta-learn especially efficient sets of nonlinear responses. Examples include conventional neural networks classifying digits and forecasting a van der Pol oscillator and physics-informed Hamiltonian neural networks learning Hénon-Heiles stellar orbits and the swing of a video recorded pendulum clock. Such learned diversity provides examples of dynamical systems selecting diversity over uniformity and elucidates the role of diversity in natural and artificial systems.
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Affiliation(s)
- Anshul Choudhary
- Nonlinear Artificial Intelligence Laboratory, Physics Department, North Carolina State University, Raleigh, NC, 27607, USA.
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA.
| | - Anil Radhakrishnan
- Nonlinear Artificial Intelligence Laboratory, Physics Department, North Carolina State University, Raleigh, NC, 27607, USA
| | - John F Lindner
- Nonlinear Artificial Intelligence Laboratory, Physics Department, North Carolina State University, Raleigh, NC, 27607, USA.
- Physics Department, The College of Wooster, Wooster, OH, 44691, USA.
| | - Sudeshna Sinha
- Indian Institute of Science Education and Research Mohali, Knowledge City, SAS Nagar, Sector 81, Manauli, Punjab, 140 306, India
| | - William L Ditto
- Nonlinear Artificial Intelligence Laboratory, Physics Department, North Carolina State University, Raleigh, NC, 27607, USA.
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9
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Hutt A, Rich S, Valiante TA, Lefebvre J. Intrinsic neural diversity quenches the dynamic volatility of neural networks. Proc Natl Acad Sci U S A 2023; 120:e2218841120. [PMID: 37399421 PMCID: PMC10334753 DOI: 10.1073/pnas.2218841120] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 05/19/2023] [Indexed: 07/05/2023] Open
Abstract
Heterogeneity is the norm in biology. The brain is no different: Neuronal cell types are myriad, reflected through their cellular morphology, type, excitability, connectivity motifs, and ion channel distributions. While this biophysical diversity enriches neural systems' dynamical repertoire, it remains challenging to reconcile with the robustness and persistence of brain function over time (resilience). To better understand the relationship between excitability heterogeneity (variability in excitability within a population of neurons) and resilience, we analyzed both analytically and numerically a nonlinear sparse neural network with balanced excitatory and inhibitory connections evolving over long time scales. Homogeneous networks demonstrated increases in excitability, and strong firing rate correlations-signs of instability-in response to a slowly varying modulatory fluctuation. Excitability heterogeneity tuned network stability in a context-dependent way by restraining responses to modulatory challenges and limiting firing rate correlations, while enriching dynamics during states of low modulatory drive. Excitability heterogeneity was found to implement a homeostatic control mechanism enhancing network resilience to changes in population size, connection probability, strength and variability of synaptic weights, by quenching the volatility (i.e., its susceptibility to critical transitions) of its dynamics. Together, these results highlight the fundamental role played by cell-to-cell heterogeneity in the robustness of brain function in the face of change.
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Affiliation(s)
- Axel Hutt
- Université de Strasbourg, CNRS, Inria, ICube, MLMS, MIMESIS, StrasbourgF-67000, France
| | - Scott Rich
- Krembil Brain Institute, Division of Clinical and Computational Neuroscience, University Health Network, Toronto, ONM5T 0S8, Canada
| | - Taufik A. Valiante
- Krembil Brain Institute, Division of Clinical and Computational Neuroscience, University Health Network, Toronto, ONM5T 0S8, Canada
- Department of Electrical and Computer Engineering, University of Toronto, Toronto, ONM5S 3G8, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ONM5S 3G9, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ONM5S 1A8, Canada
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ONM5G 2C4, Canada
- Center for Advancing Neurotechnological Innovation to Application, University of Toronto, Toronto, ONM5G 2A2, Canada
- Max Planck-University of Toronto Center for Neural Science and Technology, University of Toronto, Toronto, ONM5S 3G8, Canada
| | - Jérémie Lefebvre
- Krembil Brain Institute, Division of Clinical and Computational Neuroscience, University Health Network, Toronto, ONM5T 0S8, Canada
- Department of Biology, University of Ottawa, Ottawa, ONK1N 6N5, Canada
- Department of Mathematics, University of Toronto, Toronto, ONM5S 2E4, Canada
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10
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Harvey RE, Robinson HL, Liu C, Oliva A, Fernandez-Ruiz A. Hippocampo-cortical circuits for selective memory encoding, routing, and replay. Neuron 2023; 111:2076-2090.e9. [PMID: 37196658 PMCID: PMC11146684 DOI: 10.1016/j.neuron.2023.04.015] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 03/15/2023] [Accepted: 04/12/2023] [Indexed: 05/19/2023]
Abstract
Traditionally considered a homogeneous cell type, hippocampal pyramidal cells have been recently shown to be highly diverse. However, how this cellular diversity relates to the different hippocampal network computations that support memory-guided behavior is not yet known. We show that the anatomical identity of pyramidal cells is a major organizing principle of CA1 assembly dynamics, the emergence of memory replay, and cortical projection patterns in rats. Segregated pyramidal cell subpopulations encoded trajectory and choice-specific information or tracked changes in reward configuration respectively, and their activity was selectively read out by different cortical targets. Furthermore, distinct hippocampo-cortical assemblies coordinated the reactivation of complementary memory representations. These findings reveal the existence of specialized hippocampo-cortical subcircuits and provide a cellular mechanism that supports the computational flexibility and memory capacities of these structures.
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Affiliation(s)
- Ryan E Harvey
- Department of Neurobiology & Behavior, Cornell University, Ithaca, NY, USA
| | - Heath L Robinson
- Department of Neurobiology & Behavior, Cornell University, Ithaca, NY, USA
| | - Can Liu
- Department of Neurobiology & Behavior, Cornell University, Ithaca, NY, USA
| | - Azahara Oliva
- Department of Neurobiology & Behavior, Cornell University, Ithaca, NY, USA.
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11
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Schneider M, Bird AD, Gidon A, Triesch J, Jedlicka P, Cuntz H. Biological complexity facilitates tuning of the neuronal parameter space. PLoS Comput Biol 2023; 19:e1011212. [PMID: 37399220 DOI: 10.1371/journal.pcbi.1011212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 05/24/2023] [Indexed: 07/05/2023] Open
Abstract
The electrical and computational properties of neurons in our brains are determined by a rich repertoire of membrane-spanning ion channels and elaborate dendritic trees. However, the precise reason for this inherent complexity remains unknown, given that simpler models with fewer ion channels are also able to functionally reproduce the behaviour of some neurons. Here, we stochastically varied the ion channel densities of a biophysically detailed dentate gyrus granule cell model to produce a large population of putative granule cells, comparing those with all 15 original ion channels to their reduced but functional counterparts containing only 5 ion channels. Strikingly, valid parameter combinations in the full models were dramatically more frequent at -6% vs. -1% in the simpler model. The full models were also more stable in the face of perturbations to channel expression levels. Scaling up the numbers of ion channels artificially in the reduced models recovered these advantages confirming the key contribution of the actual number of ion channel types. We conclude that the diversity of ion channels gives a neuron greater flexibility and robustness to achieve a target excitability.
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Affiliation(s)
- Marius Schneider
- Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany
- Ernst Strüngmann Institute (ESI) for Neuroscience in cooperation with the Max Planck Society, Frankfurt am Main, Germany
- ICAR3R-Interdisciplinary Centre for 3Rs in Animal Research, Justus Liebig University Giessen, Giessen, Germany
- Faculty of Physics, Goethe University, Frankfurt/Main, Frankfurt am Main, Germany
| | - Alexander D Bird
- Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany
- Ernst Strüngmann Institute (ESI) for Neuroscience in cooperation with the Max Planck Society, Frankfurt am Main, Germany
- ICAR3R-Interdisciplinary Centre for 3Rs in Animal Research, Justus Liebig University Giessen, Giessen, Germany
| | - Albert Gidon
- Institute for Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Jochen Triesch
- Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany
- Faculty of Physics, Goethe University, Frankfurt/Main, Frankfurt am Main, Germany
- Faculty of Computer Science and Mathematics, Goethe University, Frankfurt am Main, Germany
| | - Peter Jedlicka
- Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany
- ICAR3R-Interdisciplinary Centre for 3Rs in Animal Research, Justus Liebig University Giessen, Giessen, Germany
- Institute of Clinical Neuroanatomy, Neuroscience Center, Goethe University, Frankfurt am Main, Germany
| | - Hermann Cuntz
- Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany
- Ernst Strüngmann Institute (ESI) for Neuroscience in cooperation with the Max Planck Society, Frankfurt am Main, Germany
- ICAR3R-Interdisciplinary Centre for 3Rs in Animal Research, Justus Liebig University Giessen, Giessen, Germany
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12
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Xu Q, Liu T, Ding S, Bao H, Li Z, Chen B. Extreme multistability and phase synchronization in a heterogeneous bi-neuron Rulkov network with memristive electromagnetic induction. Cogn Neurodyn 2023; 17:755-766. [PMID: 37265650 PMCID: PMC10229522 DOI: 10.1007/s11571-022-09866-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 07/13/2022] [Accepted: 07/18/2022] [Indexed: 11/03/2022] Open
Abstract
Memristive electromagnetic induction effect has been widely explored in bi-neuron network with homogeneous neurons, but rarely in bi-neuron network with heterogeneous ones. This paper builds a bi-neuron network by coupling heterogeneous Rulkov neurons with memristor and investigates the memristive electromagnetic induction effect. Theoretical analysis discloses that the bi-neuron network possesses a line equilibrium state and its stability depends on the memristor coupling strength and initial condition. That is, the stability of the line equilibrium state has a transition between unstable saddle-focus and stable node-focus via Hopf bifurcation. By employing parameters located in the stable node-focus region, dynamical behaviors related to the memristor coupling strength and initial conditions are revealed by Julia- and MATLAB-based multiple numerical tools. Numerical results demonstrate that the proposed heterogeneous bi-neuron Rulkov network can generate point attractor, period, chaos, chaos crisis, and period-doubling bifurcation. Note that extreme multistability are disclosed with respect to initial conditions of memristor and gated ion concentration. Coexisting infinitely multiple firing patterns of periodic firing patterns with different periodicities and chaotic firing patterns for different memristor initial conditions are demonstrated by phase portrait and time-domain waveform. Besides, the phase synchronization related to the memristor coupling strength and its initial condition is explored, which suggests that the two heterogeneous neurons become phase synchronization with large memristor coupling strength and initial condition. This also reflects that the plasticity of memristor synapse enables adaptive regulation in keeping energy balance between the neurons. What's more, MCU-based hardware experiments are executed to further confirm the numerical simulations.
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Affiliation(s)
- Quan Xu
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, 213164 China
| | - Tong Liu
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, 213164 China
| | - Shoukui Ding
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, 213164 China
| | - Han Bao
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, 213164 China
| | - Ze Li
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, 213164 China
| | - Bei Chen
- School of Microelectronics and Control Engineering, Changzhou University, Changzhou, 213164 China
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13
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Yiling Y, Shapcott K, Peter A, Klon-Lipok J, Xuhui H, Lazar A, Singer W. Robust encoding of natural stimuli by neuronal response sequences in monkey visual cortex. Nat Commun 2023; 14:3021. [PMID: 37231014 DOI: 10.1038/s41467-023-38587-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 05/08/2023] [Indexed: 05/27/2023] Open
Abstract
Parallel multisite recordings in the visual cortex of trained monkeys revealed that the responses of spatially distributed neurons to natural scenes are ordered in sequences. The rank order of these sequences is stimulus-specific and maintained even if the absolute timing of the responses is modified by manipulating stimulus parameters. The stimulus specificity of these sequences was highest when they were evoked by natural stimuli and deteriorated for stimulus versions in which certain statistical regularities were removed. This suggests that the response sequences result from a matching operation between sensory evidence and priors stored in the cortical network. Decoders trained on sequence order performed as well as decoders trained on rate vectors but the former could decode stimulus identity from considerably shorter response intervals than the latter. A simulated recurrent network reproduced similarly structured stimulus-specific response sequences, particularly once it was familiarized with the stimuli through non-supervised Hebbian learning. We propose that recurrent processing transforms signals from stationary visual scenes into sequential responses whose rank order is the result of a Bayesian matching operation. If this temporal code were used by the visual system it would allow for ultrafast processing of visual scenes.
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Affiliation(s)
- Yang Yiling
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528, Frankfurt am Main, Germany
- International Max Planck Research School (IMPRS) for Neural Circuits, 60438, Frankfurt am Main, Germany
- Faculty of Biological Sciences, Goethe-University Frankfurt am Main, 60438, Frankfurt am Main, Germany
| | - Katharine Shapcott
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528, Frankfurt am Main, Germany
- Frankfurt Institute for Advanced Studies, 60438, Frankfurt am Main, Germany
| | - Alina Peter
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528, Frankfurt am Main, Germany
- International Max Planck Research School (IMPRS) for Neural Circuits, 60438, Frankfurt am Main, Germany
- Faculty of Biological Sciences, Goethe-University Frankfurt am Main, 60438, Frankfurt am Main, Germany
| | - Johanna Klon-Lipok
- Max Planck Institute for Brain Research, 60438, Frankfurt am Main, Germany
| | - Huang Xuhui
- Intelligent Science and Technology Academy, China Aerospace Science and Industry Corporation (CASIC), 100144, Beijing, China
- Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
| | - Andreea Lazar
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528, Frankfurt am Main, Germany
| | - Wolf Singer
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, 60528, Frankfurt am Main, Germany.
- Frankfurt Institute for Advanced Studies, 60438, Frankfurt am Main, Germany.
- Max Planck Institute for Brain Research, 60438, Frankfurt am Main, Germany.
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14
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Glasgow NG, Chen Y, Korngreen A, Kass RE, Urban NN. A biophysical and statistical modeling paradigm for connecting neural physiology and function. J Comput Neurosci 2023; 51:263-282. [PMID: 37140691 PMCID: PMC10182162 DOI: 10.1007/s10827-023-00847-x] [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: 01/31/2022] [Revised: 02/10/2023] [Accepted: 02/16/2023] [Indexed: 05/05/2023]
Abstract
To understand single neuron computation, it is necessary to know how specific physiological parameters affect neural spiking patterns that emerge in response to specific stimuli. Here we present a computational pipeline combining biophysical and statistical models that provides a link between variation in functional ion channel expression and changes in single neuron stimulus encoding. More specifically, we create a mapping from biophysical model parameters to stimulus encoding statistical model parameters. Biophysical models provide mechanistic insight, whereas statistical models can identify associations between spiking patterns and the stimuli they encode. We used public biophysical models of two morphologically and functionally distinct projection neuron cell types: mitral cells (MCs) of the main olfactory bulb, and layer V cortical pyramidal cells (PCs). We first simulated sequences of action potentials according to certain stimuli while scaling individual ion channel conductances. We then fitted point process generalized linear models (PP-GLMs), and we constructed a mapping between the parameters in the two types of models. This framework lets us detect effects on stimulus encoding of changing an ion channel conductance. The computational pipeline combines models across scales and can be applied as a screen of channels, in any cell type of interest, to identify ways that channel properties influence single neuron computation.
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Affiliation(s)
- Nathan G Glasgow
- Department of Neurobiology and Center for Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, Pittsburgh, PA, USA
| | - Yu Chen
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Alon Korngreen
- The Leslie and Susan Gonda Interdisciplinary Brain Research Centre, Bar-Ilan University, Ramat Gan, Israel
- The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan, Israel
| | - Robert E Kass
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA.
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA.
- Department of Statistics, Carnegie Mellon University, Pittsburgh, PA, USA.
| | - Nathan N Urban
- Department of Biological Sciences, Lehigh University, Bethlehem, PA, USA
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15
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Zeraati R, Shi YL, Steinmetz NA, Gieselmann MA, Thiele A, Moore T, Levina A, Engel TA. Intrinsic timescales in the visual cortex change with selective attention and reflect spatial connectivity. Nat Commun 2023; 14:1858. [PMID: 37012299 PMCID: PMC10070246 DOI: 10.1038/s41467-023-37613-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 03/24/2023] [Indexed: 04/05/2023] Open
Abstract
Intrinsic timescales characterize dynamics of endogenous fluctuations in neural activity. Variation of intrinsic timescales across the neocortex reflects functional specialization of cortical areas, but less is known about how intrinsic timescales change during cognitive tasks. We measured intrinsic timescales of local spiking activity within columns of area V4 in male monkeys performing spatial attention tasks. The ongoing spiking activity unfolded across at least two distinct timescales, fast and slow. The slow timescale increased when monkeys attended to the receptive fields location and correlated with reaction times. By evaluating predictions of several network models, we found that spatiotemporal correlations in V4 activity were best explained by the model in which multiple timescales arise from recurrent interactions shaped by spatially arranged connectivity, and attentional modulation of timescales results from an increase in the efficacy of recurrent interactions. Our results suggest that multiple timescales may arise from the spatial connectivity in the visual cortex and flexibly change with the cognitive state due to dynamic effective interactions between neurons.
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Affiliation(s)
- Roxana Zeraati
- International Max Planck Research School for the Mechanisms of Mental Function and Dysfunction, University of Tübingen, Tübingen, Germany
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Yan-Liang Shi
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | | | - Marc A Gieselmann
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Alexander Thiele
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Tirin Moore
- Department of Neurobiology and Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA
| | - Anna Levina
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany.
- Department of Computer Science, University of Tübingen, Tübingen, Germany.
- Bernstein Center for Computational Neuroscience Tübingen, Tübingen, Germany.
| | - Tatiana A Engel
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
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16
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Westlin C, Theriault JE, Katsumi Y, Nieto-Castanon A, Kucyi A, Ruf SF, Brown SM, Pavel M, Erdogmus D, Brooks DH, Quigley KS, Whitfield-Gabrieli S, Barrett LF. Improving the study of brain-behavior relationships by revisiting basic assumptions. Trends Cogn Sci 2023; 27:246-257. [PMID: 36739181 PMCID: PMC10012342 DOI: 10.1016/j.tics.2022.12.015] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 12/23/2022] [Accepted: 12/29/2022] [Indexed: 02/05/2023]
Abstract
Neuroimaging research has been at the forefront of concerns regarding the failure of experimental findings to replicate. In the study of brain-behavior relationships, past failures to find replicable and robust effects have been attributed to methodological shortcomings. Methodological rigor is important, but there are other overlooked possibilities: most published studies share three foundational assumptions, often implicitly, that may be faulty. In this paper, we consider the empirical evidence from human brain imaging and the study of non-human animals that calls each foundational assumption into question. We then consider the opportunities for a robust science of brain-behavior relationships that await if scientists ground their research efforts in revised assumptions supported by current empirical evidence.
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Affiliation(s)
| | - Jordan E Theriault
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Yuta Katsumi
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Alfonso Nieto-Castanon
- Department of Speech, Language, and Hearing Sciences, Boston University, Boston, MA, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Aaron Kucyi
- Department of Psychological and Brain Sciences, Drexel University, Philadelphia, PA, USA
| | - Sebastian F Ruf
- Department of Civil and Environmental Engineering, Northeastern University, Boston, MA, USA
| | - Sarah M Brown
- Department of Computer Science and Statistics, University of Rhode Island, Kingston, RI, USA
| | - Misha Pavel
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA; Bouvé College of Health Sciences, Northeastern University, Boston, MA, USA
| | - Deniz Erdogmus
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA
| | - Dana H Brooks
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA
| | - Karen S Quigley
- Department of Psychology, Northeastern University, Boston, MA, USA
| | | | - Lisa Feldman Barrett
- Department of Psychology, Northeastern University, Boston, MA, USA; A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
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17
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Hou B, Ma J, Yang F. Energy-guided synapse coupling between neurons under noise. J Biol Phys 2023; 49:49-76. [PMID: 36640246 PMCID: PMC9958228 DOI: 10.1007/s10867-022-09622-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 12/26/2022] [Indexed: 01/15/2023] Open
Abstract
From a physical viewpoint, any external stimuli including noise disturbance can inject energy into the media, and the electric response is regulated by the equivalent electric stimulus. For example, mode transition in electric activities in neurons occurs and kinds of spatial patterns are formed during the wave propagation. In this paper, a feasible criterion is suggested to explain and control the growth of electric synapse and memristive synapse between Hindmarsh-Rose neurons in the presence of noise. It is claimed that synaptic coupling can be enhanced adaptively due to energy diversity, and the coupling intensity is increased to a saturation value until two neurons reach certain energy balance. Two identical neurons can reach perfect synchronization when electric synapse coupling is further increased. This scheme is also considered in a chain neural network and uniform noise is applied on all neurons. However, reaching synchronization becomes difficult for neurons in presenting spiking, bursting, and chaotic and periodic patterns, even when the local energy balance is corrupted to continue further growth of the coupling intensity. In the presence of noise, energy diversity becomes uncertain because of spatial diversity in excitability, and development of regular patterns is blocked. The similar scheme is used to control the growth of memristive synapse for neurons, and the synchronization stability and pattern formation are controlled by the energy diversity among neurons effectively. These results provide possible guidance for knowing the biophysical mechanism for synapse growth and energy flow can be applied to control the synchronous patterns between neurons.
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Affiliation(s)
- Bo Hou
- Department of Physics, Lanzhou University of Technology, Lanzhou, 730050, China
| | - Jun Ma
- Department of Physics, Lanzhou University of Technology, Lanzhou, 730050, China.
- School of Science, Chongqing University of Posts and Telecommunications, Chongqing, 430065, China.
- College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, 730050, China.
| | - Feifei Yang
- College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, 730050, China
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18
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Haggard M, Chacron MJ. Coding of object location by heterogeneous neural populations with spatially dependent correlations in weakly electric fish. PLoS Comput Biol 2023; 19:e1010938. [PMID: 36867650 PMCID: PMC10016687 DOI: 10.1371/journal.pcbi.1010938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 03/15/2023] [Accepted: 02/09/2023] [Indexed: 03/04/2023] Open
Abstract
Understanding how neural populations encode sensory stimuli remains a central problem in neuroscience. Here we performed multi-unit recordings from sensory neural populations in the electrosensory system of the weakly electric fish Apteronotus leptorhynchus in response to stimuli located at different positions along the rostro-caudal axis. Our results reveal that the spatial dependence of correlated activity along receptive fields can help mitigate the deleterious effects that these correlations would otherwise have if they were spatially independent. Moreover, using mathematical modeling, we show that experimentally observed heterogeneities in the receptive fields of neurons help optimize information transmission as to object location. Taken together, our results have important implications for understanding how sensory neurons whose receptive fields display antagonistic center-surround organization encode location. Important similarities between the electrosensory system and other sensory systems suggest that our results will be applicable elsewhere.
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Affiliation(s)
- Myriah Haggard
- Quantitative Life Sciences, McGill University, Montreal, Canada
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19
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Schmitt FJ, Rostami V, Nawrot MP. Efficient parameter calibration and real-time simulation of large-scale spiking neural networks with GeNN and NEST. Front Neuroinform 2023; 17:941696. [PMID: 36844916 PMCID: PMC9950635 DOI: 10.3389/fninf.2023.941696] [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: 05/23/2022] [Accepted: 01/16/2023] [Indexed: 02/12/2023] Open
Abstract
Spiking neural networks (SNNs) represent the state-of-the-art approach to the biologically realistic modeling of nervous system function. The systematic calibration for multiple free model parameters is necessary to achieve robust network function and demands high computing power and large memory resources. Special requirements arise from closed-loop model simulation in virtual environments and from real-time simulation in robotic application. Here, we compare two complementary approaches to efficient large-scale and real-time SNN simulation. The widely used NEural Simulation Tool (NEST) parallelizes simulation across multiple CPU cores. The GPU-enhanced Neural Network (GeNN) simulator uses the highly parallel GPU-based architecture to gain simulation speed. We quantify fixed and variable simulation costs on single machines with different hardware configurations. As a benchmark model, we use a spiking cortical attractor network with a topology of densely connected excitatory and inhibitory neuron clusters with homogeneous or distributed synaptic time constants and in comparison to the random balanced network. We show that simulation time scales linearly with the simulated biological model time and, for large networks, approximately linearly with the model size as dominated by the number of synaptic connections. Additional fixed costs with GeNN are almost independent of model size, while fixed costs with NEST increase linearly with model size. We demonstrate how GeNN can be used for simulating networks with up to 3.5 · 106 neurons (> 3 · 1012synapses) on a high-end GPU, and up to 250, 000 neurons (25 · 109 synapses) on a low-cost GPU. Real-time simulation was achieved for networks with 100, 000 neurons. Network calibration and parameter grid search can be efficiently achieved using batch processing. We discuss the advantages and disadvantages of both approaches for different use cases.
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Affiliation(s)
- Felix Johannes Schmitt
- Computational Systems Neuroscience, Institute of Zoology, University of Cologne, Cologne, Germany
| | - Vahid Rostami
- Computational Systems Neuroscience, Institute of Zoology, University of Cologne, Cologne, Germany
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20
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Ashhad S, Slepukhin VM, Feldman JL, Levine AJ. Microcircuit Synchronization and Heavy-Tailed Synaptic Weight Distribution Augment preBötzinger Complex Bursting Dynamics. J Neurosci 2023; 43:240-260. [PMID: 36400528 PMCID: PMC9838711 DOI: 10.1523/jneurosci.1195-22.2022] [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: 06/17/2022] [Revised: 11/05/2022] [Accepted: 11/10/2022] [Indexed: 11/19/2022] Open
Abstract
The preBötzinger Complex (preBötC) encodes inspiratory time as rhythmic bursts of activity underlying each breath. Spike synchronization throughout a sparsely connected preBötC microcircuit initiates bursts that ultimately drive the inspiratory motor patterns. Using minimal microcircuit models to explore burst initiation dynamics, we examined the variability in probability and latency to burst following exogenous stimulation of a small subset of neurons, mimicking experiments. Among various physiologically plausible graphs of 1000 excitatory neurons constructed using experimentally determined synaptic and connectivity parameters, directed Erdős-Rényi graphs with a broad (lognormal) distribution of synaptic weights best captured the experimentally observed dynamics. preBötC synchronization leading to bursts was regulated by the efferent connectivity of spiking neurons that are optimally tuned to amplify modest preinspiratory activity through input convergence. Using graph-theoretic and machine learning-based analyses, we found that input convergence of efferent connectivity at the next-nearest neighbor order was a strong predictor of incipient synchronization. Our analyses revealed a crucial role of synaptic heterogeneity in imparting exceptionally robust yet flexible preBötC attractor dynamics. Given the pervasiveness of lognormally distributed synaptic strengths throughout the nervous system, we postulate that these mechanisms represent a ubiquitous template for temporal processing and decision-making computational motifs.SIGNIFICANCE STATEMENT Mammalian breathing is robust, virtually continuous throughout life, yet is inherently labile: to adapt to rapid metabolic shifts (e.g., fleeing a predator or chasing prey); for airway reflexes; and to enable nonventilatory behaviors (e.g., vocalization, breathholding, laughing). Canonical theoretical frameworks-based on pacemakers and intrinsic bursting-cannot account for the observed robustness and flexibility of the preBötzinger Complex rhythm. Experiments reveal that network synchronization is the key to initiate inspiratory bursts in each breathing cycle. We investigated preBötC synchronization dynamics using network models constructed with experimentally determined neuronal and synaptic parameters. We discovered that a fat-tailed (non-Gaussian) synaptic weight distribution-a manifestation of synaptic heterogeneity-augments neuronal synchronization and attractor dynamics in this vital rhythmogenic network, contributing to its extraordinary reliability and responsiveness.
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Affiliation(s)
- Sufyan Ashhad
- Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California 90095-1763
| | - Valentin M Slepukhin
- Department of Physics & Astronomy, University of California, Los Angeles, Los Angeles, California 90095-1596
| | - Jack L Feldman
- Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California 90095-1763
| | - Alex J Levine
- Department of Physics & Astronomy, University of California, Los Angeles, Los Angeles, California 90095-1596
- Department of Chemistry & Biochemistry, University of California, Los Angeles, Los Angeles, California 90095-1596
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21
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Miehl C, Gjorgjieva J. Stability and learning in excitatory synapses by nonlinear inhibitory plasticity. PLoS Comput Biol 2022; 18:e1010682. [PMID: 36459503 PMCID: PMC9718420 DOI: 10.1371/journal.pcbi.1010682] [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: 04/05/2022] [Accepted: 10/25/2022] [Indexed: 12/03/2022] Open
Abstract
Synaptic changes are hypothesized to underlie learning and memory formation in the brain. But Hebbian synaptic plasticity of excitatory synapses on its own is unstable, leading to either unlimited growth of synaptic strengths or silencing of neuronal activity without additional homeostatic mechanisms. To control excitatory synaptic strengths, we propose a novel form of synaptic plasticity at inhibitory synapses. Using computational modeling, we suggest two key features of inhibitory plasticity, dominance of inhibition over excitation and a nonlinear dependence on the firing rate of postsynaptic excitatory neurons whereby inhibitory synaptic strengths change with the same sign (potentiate or depress) as excitatory synaptic strengths. We demonstrate that the stable synaptic strengths realized by this novel inhibitory plasticity model affects excitatory/inhibitory weight ratios in agreement with experimental results. Applying a disinhibitory signal can gate plasticity and lead to the generation of receptive fields and strong bidirectional connectivity in a recurrent network. Hence, a novel form of nonlinear inhibitory plasticity can simultaneously stabilize excitatory synaptic strengths and enable learning upon disinhibition.
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Affiliation(s)
- Christoph Miehl
- Max Planck Institute for Brain Research, Frankfurt am Main, Germany
- School of Life Sciences, Technical University of Munich, Freising, Germany
- * E-mail: (CM); (JG)
| | - Julijana Gjorgjieva
- Max Planck Institute for Brain Research, Frankfurt am Main, Germany
- School of Life Sciences, Technical University of Munich, Freising, Germany
- * E-mail: (CM); (JG)
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22
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Chockanathan U, Padmanabhan K. From synapses to circuits and back: Bridging levels of understanding in animal models of Alzheimer's disease. Eur J Neurosci 2022; 56:5564-5586. [PMID: 35244297 PMCID: PMC10926359 DOI: 10.1111/ejn.15636] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 02/04/2022] [Accepted: 02/23/2022] [Indexed: 12/14/2022]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder characterized by behavioural changes that include memory loss and cognitive decline and is associated with the appearance of amyloid-β plaques and neurofibrillary tangles throughout the brain. Although aspects of the disease percolate across multiple levels of neuronal organization, from the cellular to the behavioural, it is increasingly clear that circuits are a critical junction between the cellular pathology and the behavioural phenotypes that bookend these levels of analyses. In this review, we discuss critical aspects of neural circuit research, beginning with synapses and progressing to network activity and how they influence our understanding of disease processed in AD.
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Affiliation(s)
- Udaysankar Chockanathan
- Department of Neuroscience, University of Rochester School of Medicine and Dentistry, Rochester, New York, USA
- Neuroscience Graduate Program, University of Rochester School of Medicine and Dentistry, Rochester, New York, USA
- Medical Scientist Training Program, University of Rochester School of Medicine and Dentistry, Rochester, New York, USA
- Ernest J. Del Monte Institute for Neuroscience, University of Rochester School of Medicine and Dentistry, Rochester, New York, USA
| | - Krishnan Padmanabhan
- Department of Neuroscience, University of Rochester School of Medicine and Dentistry, Rochester, New York, USA
- Neuroscience Graduate Program, University of Rochester School of Medicine and Dentistry, Rochester, New York, USA
- Medical Scientist Training Program, University of Rochester School of Medicine and Dentistry, Rochester, New York, USA
- Ernest J. Del Monte Institute for Neuroscience, University of Rochester School of Medicine and Dentistry, Rochester, New York, USA
- Center for Visual Science, University of Rochester School of Medicine and Dentistry, Rochester, New York, USA
- Intellectual and Developmental Disabilities Research Center, University of Rochester School of Medicine and Dentistry, Rochester, New York, USA
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23
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Ash RT, Palagina G, Fernandez-Leon JA, Park J, Seilheimer R, Lee S, Sabharwal J, Reyes F, Wang J, Lu D, Sarfraz M, Froudarakis E, Tolias AS, Wu SM, Smirnakis SM. Increased Reliability of Visually-Evoked Activity in Area V1 of the MECP2-Duplication Mouse Model of Autism. J Neurosci 2022; 42:6469-6482. [PMID: 35831173 PMCID: PMC9398540 DOI: 10.1523/jneurosci.0654-22.2022] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 05/15/2022] [Accepted: 06/02/2022] [Indexed: 11/21/2022] Open
Abstract
Atypical sensory processing is now thought to be a core feature of the autism spectrum. Influential theories have proposed that both increased and decreased neural response reliability within sensory systems could underlie altered sensory processing in autism. Here, we report evidence for abnormally increased reliability of visual-evoked responses in layer 2/3 neurons of adult male and female primary visual cortex in the MECP2-duplication syndrome animal model of autism. Increased response reliability was due in part to decreased response amplitude, decreased fluctuations in endogenous activity, and an abnormal decoupling of visual-evoked activity from endogenous activity. Similar to what was observed neuronally, the optokinetic reflex occurred more reliably at low contrasts in mutant mice compared with controls. Retinal responses did not explain our observations. These data suggest that the circuit mechanisms for combining sensory-evoked and endogenous signal and noise processes may be altered in this form of syndromic autism.SIGNIFICANCE STATEMENT Atypical sensory processing is now thought to be a core feature of the autism spectrum. Influential theories have proposed that both increased and decreased neural response reliability within sensory systems could underlie altered sensory processing in autism. Here, we report evidence for abnormally increased reliability of visual-evoked responses in primary visual cortex of the animal model for MECP2-duplication syndrome, a high-penetrance single-gene cause of autism. Visual-evoked activity was abnormally decoupled from endogenous activity in mutant mice, suggesting in line with the influential "hypo-priors" theory of autism that sensory priors embedded in endogenous activity may have less influence on perception in autism.
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Affiliation(s)
- Ryan T Ash
- Department of Psychiatry, Stanford University School of Medicine, Stanford, California 94305
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas 77030
| | - Ganna Palagina
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115
| | - Jose A Fernandez-Leon
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas 77030
- Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires and Instituto de Investigación en Tecnología Informática Avanzada, Exact Sciences Faculty-Universidad Nacional del Centro de la Provincia de Buenos Aires, Tandil, Argentina
| | - Jiyoung Park
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115
- Program in Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine, Houston, Texas 77030
| | - Rob Seilheimer
- Program in Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine, Houston, Texas 77030
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Sangkyun Lee
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115
| | - Jasdeep Sabharwal
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas 77030
- Wilmer Eye Institute, Johns Hopkins University, Baltimore, Maryland 21205
| | - Fredy Reyes
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas 77030
| | - Jing Wang
- Department of Ophthalmology, Baylor College of Medicine, Houston, Texas 77030
| | - Dylan Lu
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115
| | - Muhammad Sarfraz
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115
| | - Emmanouil Froudarakis
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas 77030
- FORTH, Heraklion, Crete, Greece 70013
| | - Andreas S Tolias
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas 77030
| | - Samuel M Wu
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas 77030
- Department of Ophthalmology, Baylor College of Medicine, Houston, Texas 77030
| | - Stelios M Smirnakis
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115
- Department of Neuroscience, Baylor College of Medicine, Houston, Texas 77030
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24
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Mazzucato L. Neural mechanisms underlying the temporal organization of naturalistic animal behavior. eLife 2022; 11:76577. [PMID: 35792884 PMCID: PMC9259028 DOI: 10.7554/elife.76577] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 06/07/2022] [Indexed: 12/17/2022] Open
Abstract
Naturalistic animal behavior exhibits a strikingly complex organization in the temporal domain, with variability arising from at least three sources: hierarchical, contextual, and stochastic. What neural mechanisms and computational principles underlie such intricate temporal features? In this review, we provide a critical assessment of the existing behavioral and neurophysiological evidence for these sources of temporal variability in naturalistic behavior. Recent research converges on an emergent mechanistic theory of temporal variability based on attractor neural networks and metastable dynamics, arising via coordinated interactions between mesoscopic neural circuits. We highlight the crucial role played by structural heterogeneities as well as noise from mesoscopic feedback loops in regulating flexible behavior. We assess the shortcomings and missing links in the current theoretical and experimental literature and propose new directions of investigation to fill these gaps.
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Affiliation(s)
- Luca Mazzucato
- Institute of Neuroscience, Departments of Biology, Mathematics and Physics, University of Oregon
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25
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Smith SJ, von Zastrow M. A Molecular Landscape of Mouse Hippocampal Neuromodulation. Front Neural Circuits 2022; 16:836930. [PMID: 35601530 PMCID: PMC9120848 DOI: 10.3389/fncir.2022.836930] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 03/30/2022] [Indexed: 12/23/2022] Open
Abstract
Adaptive neuronal circuit function requires a continual adjustment of synaptic network parameters known as “neuromodulation.” This process is now understood to be based primarily on the binding of myriad secreted “modulatory” ligands such as dopamine, serotonin and the neuropeptides to G protein-coupled receptors (GPCRs) that, in turn, regulate the function of the ion channels that establish synaptic weights and membrane excitability. Many of the basic molecular mechanisms of neuromodulation are now known, but the organization of neuromodulation at a network level is still an enigma. New single-cell RNA sequencing data and transcriptomic neurotaxonomies now offer bright new lights to shine on this critical “dark matter” of neuroscience. Here we leverage these advances to explore the cell-type-specific expression of genes encoding GPCRs, modulatory ligands, ion channels and intervening signal transduction molecules in mouse hippocampus area CA1, with the goal of revealing broad outlines of this well-studied brain structure’s neuromodulatory network architecture.
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Affiliation(s)
- Stephen J Smith
- Allen Institute for Brain Science, Seattle, WA, United States
- *Correspondence: Stephen J Smith,
| | - Mark von Zastrow
- Departments of Psychiatry and Pharmacology, University of California, San Francisco, San Francisco, CA, United States
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26
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Abstract
The design of robots that interact autonomously with the environment and exhibit complex behaviours is an open challenge that can benefit from understanding what makes living beings fit to act in the world. Neuromorphic engineering studies neural computational principles to develop technologies that can provide a computing substrate for building compact and low-power processing systems. We discuss why endowing robots with neuromorphic technologies - from perception to motor control - represents a promising approach for the creation of robots which can seamlessly integrate in society. We present initial attempts in this direction, highlight open challenges, and propose actions required to overcome current limitations.
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Affiliation(s)
- Chiara Bartolozzi
- Event-Driven Perception for Robotics, Istituto Italiano di Tecnologia, via San Quirico 19D, 16163, Genova, Italy.
| | - Giacomo Indiveri
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Winterthurerstr. 190, 8057, Zurich, Switzerland
| | - Elisa Donati
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Winterthurerstr. 190, 8057, Zurich, Switzerland
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27
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Piñeiro M, Mena W, Ewer J, Orio P. Extracting temporal relationships between weakly coupled peptidergic and motoneuronal signaling: Application to Drosophila ecdysis behavior. PLoS Comput Biol 2021; 17:e1008933. [PMID: 34910730 PMCID: PMC8716061 DOI: 10.1371/journal.pcbi.1008933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 12/29/2021] [Accepted: 11/14/2021] [Indexed: 11/18/2022] Open
Abstract
Neuromodulators, such as neuropeptides, can regulate and reconfigure neural circuits to alter their output, affecting in this way animal physiology and behavior. The interplay between the activity of neuronal circuits, their modulation by neuropeptides, and the resulting behavior, is still poorly understood. Here, we present a quantitative framework to study the relationships between the temporal pattern of activity of peptidergic neurons and of motoneurons during Drosophila ecdysis behavior, a highly stereotyped motor sequence that is critical for insect growth. We analyzed, in the time and frequency domains, simultaneous intracellular calcium recordings of peptidergic CCAP (crustacean cardioactive peptide) neurons and motoneurons obtained from isolated central nervous systems throughout fictive ecdysis behavior induced ex vivo by Ecdysis triggering hormone. We found that the activity of both neuronal populations is tightly coupled in a cross-frequency manner, suggesting that CCAP neurons modulate the frequency of motoneuron firing. To explore this idea further, we used a probabilistic logistic model to show that calcium dynamics in CCAP neurons can predict the oscillation of motoneurons, both in a simple model and in a conductance-based model capable of simulating many features of the observed neural dynamics. Finally, we developed an algorithm to quantify the motor behavior observed in videos of pupal ecdysis, and compared their features to the patterns of neuronal calcium activity recorded ex vivo. We found that the motor activity of the intact animal is more regular than the motoneuronal activity recorded from ex vivo preparations during fictive ecdysis behavior; the analysis of the patterns of movement also allowed us to identify a new post-ecdysis phase.
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Affiliation(s)
- Miguel Piñeiro
- Centro Interdisciplinario de Neurociencia de Valparaíso, Universidad de Valparaíso, Valparaíso, Chile
| | - Wilson Mena
- Centro Interdisciplinario de Neurociencia de Valparaíso, Universidad de Valparaíso, Valparaíso, Chile
- Department of Neuroscience, Institut Pasteur, Paris, France
| | - John Ewer
- Centro Interdisciplinario de Neurociencia de Valparaíso, Universidad de Valparaíso, Valparaíso, Chile
- Instituto de Neurociencias, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
- * E-mail: (JE); (PO)
| | - Patricio Orio
- Centro Interdisciplinario de Neurociencia de Valparaíso, Universidad de Valparaíso, Valparaíso, Chile
- Instituto de Neurociencias, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
- * E-mail: (JE); (PO)
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28
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Abdelrahman NY, Vasilaki E, Lin AC. Compensatory variability in network parameters enhances memory performance in the Drosophila mushroom body. Proc Natl Acad Sci U S A 2021; 118:e2102158118. [PMID: 34845010 PMCID: PMC8670477 DOI: 10.1073/pnas.2102158118] [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] [Accepted: 10/06/2021] [Indexed: 11/18/2022] Open
Abstract
Neural circuits use homeostatic compensation to achieve consistent behavior despite variability in underlying intrinsic and network parameters. However, it remains unclear how compensation regulates variability across a population of the same type of neurons within an individual and what computational benefits might result from such compensation. We address these questions in the Drosophila mushroom body, the fly's olfactory memory center. In a computational model, we show that under sparse coding conditions, memory performance is degraded when the mushroom body's principal neurons, Kenyon cells (KCs), vary realistically in key parameters governing their excitability. However, memory performance is rescued while maintaining realistic variability if parameters compensate for each other to equalize KC average activity. Such compensation can be achieved through both activity-dependent and activity-independent mechanisms. Finally, we show that correlations predicted by our model's compensatory mechanisms appear in the Drosophila hemibrain connectome. These findings reveal compensatory variability in the mushroom body and describe its computational benefits for associative memory.
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Affiliation(s)
- Nada Y Abdelrahman
- School of Biosciences, University of Sheffield, Sheffield S10 2TN, United Kingdom
- Department of Computer Science, University of Sheffield, Sheffield S1 4DP, United Kingdom
- Neuroscience Institute, University of Sheffield, Sheffield S10 2TN, United Kingdom
| | - Eleni Vasilaki
- Department of Computer Science, University of Sheffield, Sheffield S1 4DP, United Kingdom
- Neuroscience Institute, University of Sheffield, Sheffield S10 2TN, United Kingdom
| | - Andrew C Lin
- School of Biosciences, University of Sheffield, Sheffield S10 2TN, United Kingdom;
- Neuroscience Institute, University of Sheffield, Sheffield S10 2TN, United Kingdom
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29
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A general principle of dendritic constancy: A neuron's size- and shape-invariant excitability. Neuron 2021; 109:3647-3662.e7. [PMID: 34555313 DOI: 10.1016/j.neuron.2021.08.028] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Revised: 06/29/2021] [Accepted: 08/20/2021] [Indexed: 11/20/2022]
Abstract
Reducing neuronal size results in less membrane and therefore lower input conductance. Smaller neurons are thus more excitable, as seen in their responses to somatic current injections. However, the impact of a neuron's size and shape on its voltage responses to dendritic synaptic activation is much less understood. Here we use analytical cable theory to predict voltage responses to distributed synaptic inputs in unbranched cables, showing that these are entirely independent of dendritic length. For a given synaptic density, neuronal responses depend only on the average dendritic diameter and intrinsic conductivity. This remains valid for a wide range of morphologies irrespective of their arborization complexity. Spiking models indicate that morphology-invariant numbers of spikes approximate the percentage of active synapses. In contrast to spike rate, spike times do depend on dendrite morphology. In summary, neuronal excitability in response to distributed synaptic inputs is largely unaffected by dendrite length or complexity.
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30
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Perez-Nieves N, Leung VCH, Dragotti PL, Goodman DFM. Neural heterogeneity promotes robust learning. Nat Commun 2021; 12:5791. [PMID: 34608134 PMCID: PMC8490404 DOI: 10.1038/s41467-021-26022-3] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 09/10/2021] [Indexed: 11/24/2022] Open
Abstract
The brain is a hugely diverse, heterogeneous structure. Whether or not heterogeneity at the neural level plays a functional role remains unclear, and has been relatively little explored in models which are often highly homogeneous. We compared the performance of spiking neural networks trained to carry out tasks of real-world difficulty, with varying degrees of heterogeneity, and found that heterogeneity substantially improved task performance. Learning with heterogeneity was more stable and robust, particularly for tasks with a rich temporal structure. In addition, the distribution of neuronal parameters in the trained networks is similar to those observed experimentally. We suggest that the heterogeneity observed in the brain may be more than just the byproduct of noisy processes, but rather may serve an active and important role in allowing animals to learn in changing environments.
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Affiliation(s)
- Nicolas Perez-Nieves
- Department of Electrical and Electronic Engineering, Imperial College London, London, SW7 2AZ, UK.
| | - Vincent C H Leung
- Department of Electrical and Electronic Engineering, Imperial College London, London, SW7 2AZ, UK
| | - Pier Luigi Dragotti
- Department of Electrical and Electronic Engineering, Imperial College London, London, SW7 2AZ, UK
| | - Dan F M Goodman
- Department of Electrical and Electronic Engineering, Imperial College London, London, SW7 2AZ, UK.
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31
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Mishra P, Narayanan R. Stable continual learning through structured multiscale plasticity manifolds. Curr Opin Neurobiol 2021; 70:51-63. [PMID: 34416674 PMCID: PMC7611638 DOI: 10.1016/j.conb.2021.07.009] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2021] [Revised: 07/15/2021] [Accepted: 07/19/2021] [Indexed: 11/16/2022]
Abstract
Biological plasticity is ubiquitous. How does the brain navigate this complex plasticity space, where any component can seemingly change, in adapting to an ever-changing environment? We build a systematic case that stable continuous learning is achieved by structured rules that enforce multiple, but not all, components to change together in specific directions. This rule-based low-dimensional plasticity manifold of permitted plasticity combinations emerges from cell type-specific molecular signaling and triggers cascading impacts that span multiple scales. These multiscale plasticity manifolds form the basis for behavioral learning and are dynamic entities that are altered by neuromodulation, metaplasticity, and pathology. We explore the strong links between heterogeneities, degeneracy, and plasticity manifolds and emphasize the need to incorporate plasticity manifolds into learning-theoretical frameworks and experimental designs.
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Affiliation(s)
- Poonam Mishra
- Cellular Neurophysiology Laboratory, Molecular Biophysics Unit, Indian Institute of Science, Bangalore 560012, India
| | - Rishikesh Narayanan
- Cellular Neurophysiology Laboratory, Molecular Biophysics Unit, Indian Institute of Science, Bangalore 560012, India.
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32
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Sinha M, Narayanan R. Active Dendrites and Local Field Potentials: Biophysical Mechanisms and Computational Explorations. Neuroscience 2021; 489:111-142. [PMID: 34506834 PMCID: PMC7612676 DOI: 10.1016/j.neuroscience.2021.08.035] [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: 04/27/2021] [Revised: 08/30/2021] [Accepted: 08/31/2021] [Indexed: 10/27/2022]
Abstract
Neurons and glial cells are endowed with membranes that express a rich repertoire of ion channels, transporters, and receptors. The constant flux of ions across the neuronal and glial membranes results in voltage fluctuations that can be recorded from the extracellular matrix. The high frequency components of this voltage signal contain information about the spiking activity, reflecting the output from the neurons surrounding the recording location. The low frequency components of the signal, referred to as the local field potential (LFP), have been traditionally thought to provide information about the synaptic inputs that impinge on the large dendritic trees of various neurons. In this review, we discuss recent computational and experimental studies pointing to a critical role of several active dendritic mechanisms that can influence the genesis and the location-dependent spectro-temporal dynamics of LFPs, spanning different brain regions. We strongly emphasize the need to account for the several fast and slow dendritic events and associated active mechanisms - including gradients in their expression profiles, inter- and intra-cellular spatio-temporal interactions spanning neurons and glia, heterogeneities and degeneracy across scales, neuromodulatory influences, and activitydependent plasticity - towards gaining important insights about the origins of LFP under different behavioral states in health and disease. We provide simple but essential guidelines on how to model LFPs taking into account these dendritic mechanisms, with detailed methodology on how to account for various heterogeneities and electrophysiological properties of neurons and synapses while studying LFPs.
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Affiliation(s)
- Manisha Sinha
- Cellular Neurophysiology Laboratory, Molecular Biophysics Unit, Indian Institute of Science, Bangalore, Karnataka 560012, India
| | - Rishikesh Narayanan
- Cellular Neurophysiology Laboratory, Molecular Biophysics Unit, Indian Institute of Science, Bangalore, Karnataka 560012, India.
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33
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Mishra P, Narayanan R. Ion-channel degeneracy: Multiple ion channels heterogeneously regulate intrinsic physiology of rat hippocampal granule cells. Physiol Rep 2021; 9:e14963. [PMID: 34342171 PMCID: PMC8329439 DOI: 10.14814/phy2.14963] [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: 04/11/2021] [Revised: 06/13/2021] [Accepted: 06/21/2021] [Indexed: 01/09/2023] Open
Abstract
Degeneracy, the ability of multiple structural components to elicit the same characteristic functional properties, constitutes an elegant mechanism for achieving biological robustness. In this study, we sought electrophysiological signatures for the expression of ion-channel degeneracy in the emergence of intrinsic properties of rat hippocampal granule cells. We measured the impact of four different ion-channel subtypes-hyperpolarization-activated cyclic-nucleotide-gated (HCN), barium-sensitive inward rectifier potassium (Kir ), tertiapin-Q-sensitive inward rectifier potassium, and persistent sodium (NaP) channels-on 21 functional measurements employing pharmacological agents, and report electrophysiological data on two characteristic signatures for the expression of ion-channel degeneracy in granule cells. First, the blockade of a specific ion-channel subtype altered several, but not all, functional measurements. Furthermore, any given functional measurement was altered by the blockade of many, but not all, ion-channel subtypes. Second, the impact of blocking each ion-channel subtype manifested neuron-to-neuron variability in the quantum of changes in the electrophysiological measurements. Specifically, we found that blocking HCN or Ba-sensitive Kir channels enhanced action potential firing rate, but blockade of NaP channels reduced firing rate of granule cells. Subthreshold measures of granule cell intrinsic excitability (input resistance, temporal summation, and impedance amplitude) were enhanced by blockade of HCN or Ba-sensitive Kir channels, but were not significantly altered by NaP channel blockade. We confirmed that the HCN and Ba-sensitive Kir channels independently altered sub- and suprathreshold properties of granule cells through sequential application of pharmacological agents that blocked these channels. Finally, we found that none of the sub- or suprathreshold measurements of granule cells were significantly altered upon treatment with tertiapin-Q. Together, the heterogeneous many-to-many mapping between ion channels and single-neuron intrinsic properties emphasizes the need to account for ion-channel degeneracy in cellular- and network-scale physiology.
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Affiliation(s)
- Poonam Mishra
- Cellular Neurophysiology LaboratoryMolecular Biophysics UnitIndian Institute of ScienceBangaloreIndia
| | - Rishikesh Narayanan
- Cellular Neurophysiology LaboratoryMolecular Biophysics UnitIndian Institute of ScienceBangaloreIndia
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34
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Südhof TC. The cell biology of synapse formation. J Cell Biol 2021; 220:e202103052. [PMID: 34086051 PMCID: PMC8186004 DOI: 10.1083/jcb.202103052] [Citation(s) in RCA: 121] [Impact Index Per Article: 40.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 05/13/2021] [Accepted: 05/17/2021] [Indexed: 04/25/2023] Open
Abstract
In a neural circuit, synapses transfer information rapidly between neurons and transform this information during transfer. The diverse computational properties of synapses are shaped by the interactions between pre- and postsynaptic neurons. How synapses are assembled to form a neural circuit, and how the specificity of synaptic connections is achieved, is largely unknown. Here, I posit that synaptic adhesion molecules (SAMs) organize synapse formation. Diverse SAMs collaborate to achieve the astounding specificity and plasticity of synapses, with each SAM contributing different facets. In orchestrating synapse assembly, SAMs likely act as signal transduction devices. Although many candidate SAMs are known, only a few SAMs appear to have a major impact on synapse formation. Thus, a limited set of collaborating SAMs likely suffices to account for synapse formation. Strikingly, several SAMs are genetically linked to neuropsychiatric disorders, suggesting that impairments in synapse assembly are instrumental in the pathogenesis of neuropsychiatric disorders.
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35
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Chatzikalymniou AP, Gumus M, Skinner FK. Linking minimal and detailed models of CA1 microcircuits reveals how theta rhythms emerge and their frequencies controlled. Hippocampus 2021; 31:982-1002. [PMID: 34086375 DOI: 10.1002/hipo.23364] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 05/08/2021] [Indexed: 01/18/2023]
Abstract
The wide variety of cell types and their biophysical complexities pose a challenge in our ability to understand oscillatory activities produced by cellular-based computational network models. This challenge stems from their high-dimensional and multiparametric natures. To overcome this, we implement a solution by linking minimal and detailed models of CA1 microcircuits that generate intrahippocampal (3-12 Hz) theta rhythms. We leverage insights from minimal models to guide explorations of more detailed models and obtain a cellular perspective of theta generation. Our findings distinguish the pyramidal cells as the theta rhythm initiators and reveal that their activity is regularized by the inhibitory cell populations, supporting a proposed hypothesis of an "inhibition-based tuning" mechanism. We find a strong correlation between input current to the pyramidal cells and the resulting local field potential theta frequency, indicating that intrinsic pyramidal cell properties underpin network frequency characteristics. This work provides a cellular-based foundation from which in vivo theta activities can be explored.
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Affiliation(s)
- Alexandra Pierri Chatzikalymniou
- Krembil Brain Institute, University Health Network, Toronto, Canada.,Department of Physiology, University of Toronto, Toronto, Canada
| | - Melisa Gumus
- Krembil Brain Institute, University Health Network, Toronto, Canada
| | - Frances K Skinner
- Krembil Brain Institute, University Health Network, Toronto, Canada.,Departments of Medicine (Neurology) and Physiology, University of Toronto, Toronto, Canada
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36
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Skinner FK, Rich S, Lunyov AR, Lefebvre J, Chatzikalymniou AP. A Hypothesis for Theta Rhythm Frequency Control in CA1 Microcircuits. Front Neural Circuits 2021; 15:643360. [PMID: 33967702 PMCID: PMC8097141 DOI: 10.3389/fncir.2021.643360] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 03/24/2021] [Indexed: 12/16/2022] Open
Abstract
Computational models of neural circuits with varying levels of biophysical detail have been generated in pursuit of an underlying mechanism explaining the ubiquitous hippocampal theta rhythm. However, within the theta rhythm are at least two types with distinct frequencies associated with different behavioral states, an aspect that must be considered in pursuit of these mechanistic explanations. Here, using our previously developed excitatory-inhibitory network models that generate theta rhythms, we investigate the robustness of theta generation to intrinsic neuronal variability by building a database of heterogeneous excitatory cells and implementing them in our microcircuit model. We specifically investigate the impact of three key "building block" features of the excitatory cell model that underlie our model design: these cells' rheobase, their capacity for post-inhibitory rebound, and their spike-frequency adaptation. We show that theta rhythms at various frequencies can arise dependent upon the combination of these building block features, and we find that the speed of these oscillations are dependent upon the excitatory cells' response to inhibitory drive, as encapsulated by their phase response curves. Taken together, these findings support a hypothesis for theta frequency control that includes two aspects: (i) an internal mechanism that stems from the building block features of excitatory cell dynamics; (ii) an external mechanism that we describe as "inhibition-based tuning" of excitatory cell firing. We propose that these mechanisms control theta rhythm frequencies and underlie their robustness.
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Affiliation(s)
- Frances K. Skinner
- Division of Clinical and Computational Neuroscience, Krembil Brain Institute, Krembil Research Institute, University Health Network, Toronto, ON, Canada
- Department of Medicine (Neurology), University of Toronto, Toronto, ON, Canada
- Department of Physiology, University of Toronto, Toronto, ON, Canada
| | - Scott Rich
- Division of Clinical and Computational Neuroscience, Krembil Brain Institute, Krembil Research Institute, University Health Network, Toronto, ON, Canada
| | - Anton R. Lunyov
- Division of Clinical and Computational Neuroscience, Krembil Brain Institute, Krembil Research Institute, University Health Network, Toronto, ON, Canada
| | - Jeremie Lefebvre
- Division of Clinical and Computational Neuroscience, Krembil Brain Institute, Krembil Research Institute, University Health Network, Toronto, ON, Canada
| | - Alexandra P. Chatzikalymniou
- Division of Clinical and Computational Neuroscience, Krembil Brain Institute, Krembil Research Institute, University Health Network, Toronto, ON, Canada
- Department of Physiology, University of Toronto, Toronto, ON, Canada
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37
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Zeldenrust F, Gutkin B, Denéve S. Efficient and robust coding in heterogeneous recurrent networks. PLoS Comput Biol 2021; 17:e1008673. [PMID: 33930016 PMCID: PMC8115785 DOI: 10.1371/journal.pcbi.1008673] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 05/12/2021] [Accepted: 04/07/2021] [Indexed: 11/19/2022] Open
Abstract
Cortical networks show a large heterogeneity of neuronal properties. However, traditional coding models have focused on homogeneous populations of excitatory and inhibitory neurons. Here, we analytically derive a class of recurrent networks of spiking neurons that close to optimally track a continuously varying input online, based on two assumptions: 1) every spike is decoded linearly and 2) the network aims to reduce the mean-squared error between the input and the estimate. From this we derive a class of predictive coding networks, that unifies encoding and decoding and in which we can investigate the difference between homogeneous networks and heterogeneous networks, in which each neurons represents different features and has different spike-generating properties. We find that in this framework, 'type 1' and 'type 2' neurons arise naturally and networks consisting of a heterogeneous population of different neuron types are both more efficient and more robust against correlated noise. We make two experimental predictions: 1) we predict that integrators show strong correlations with other integrators and resonators are correlated with resonators, whereas the correlations are much weaker between neurons with different coding properties and 2) that 'type 2' neurons are more coherent with the overall network activity than 'type 1' neurons.
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Affiliation(s)
- Fleur Zeldenrust
- Department of Neurophysiology, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Boris Gutkin
- Group for Neural Theory, INSERM U960, Département d’Études Cognitives, École Normal Supérieure PSL University, Paris, France
- Center for Cognition and Decision Making, National Research University Higher School of Economics, Moscow, Russia
| | - Sophie Denéve
- Group for Neural Theory, INSERM U960, Département d’Études Cognitives, École Normal Supérieure PSL University, Paris, France
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38
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Pooryasin A, Maglione M, Schubert M, Matkovic-Rachid T, Hasheminasab SM, Pech U, Fiala A, Mielke T, Sigrist SJ. Unc13A and Unc13B contribute to the decoding of distinct sensory information in Drosophila. Nat Commun 2021; 12:1932. [PMID: 33771998 PMCID: PMC7997984 DOI: 10.1038/s41467-021-22180-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 02/26/2021] [Indexed: 12/11/2022] Open
Abstract
The physical distance between presynaptic Ca2+ channels and the Ca2+ sensors triggering the release of neurotransmitter-containing vesicles regulates short-term plasticity (STP). While STP is highly diversified across synapse types, the computational and behavioral relevance of this diversity remains unclear. In the Drosophila brain, at nanoscale level, we can distinguish distinct coupling distances between Ca2+ channels and the (m)unc13 family priming factors, Unc13A and Unc13B. Importantly, coupling distance defines release components with distinct STP characteristics. Here, we show that while Unc13A and Unc13B both contribute to synaptic signalling, they play distinct roles in neural decoding of olfactory information at excitatory projection neuron (ePN) output synapses. Unc13A clusters closer to Ca2+ channels than Unc13B, specifically promoting fast phasic signal transfer. Reduction of Unc13A in ePNs attenuates responses to both aversive and appetitive stimuli, while reduction of Unc13B provokes a general shift towards appetitive values. Collectively, we provide direct genetic evidence that release components of distinct nanoscopic coupling distances differentially control STP to play distinct roles in neural decoding of sensory information. The physical distance between synaptic Ca2+ channels and sensors modulates short-term plasticity (STP). Here, the authors show that synaptic release factors Unc13A and Unc13B distinctly couple with Ca2+ channels and contribute to the neural decoding of distinct sensory information in Drosophila.
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Affiliation(s)
- Atefeh Pooryasin
- Institute for Biology/Genetics, Freie Universität Berlin, Berlin, Germany
| | - Marta Maglione
- Institute for Biology/Genetics, Freie Universität Berlin, Berlin, Germany.,NeuroCure Cluster of Excellence, Charité Universitätsmedizin, Berlin, Germany
| | - Marco Schubert
- Institute for Biology/Genetics, Freie Universität Berlin, Berlin, Germany
| | | | - Sayed-Mohammad Hasheminasab
- Department of Dermatology, Venereology and Allergology, Charité Universitätsmedizin, Berlin, Germany.,CCU Translational Radiation Oncology, DKTK, National Center for Tumor Diseases (NCT), Heidelberg University Hospital (UKHD) and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Ulrike Pech
- Department of Molecular Neurobiology of Behavior, Johann-Friedrich-Blumenbach-Institute for Zoology and Anthropology, University of Göttingen, Göttingen, Germany.,Laboratory of Neuronal Communication, VIB Center for the Biology of Disease, K.U.Leuven, Leuven, Belgium
| | - André Fiala
- Department of Molecular Neurobiology of Behavior, Johann-Friedrich-Blumenbach-Institute for Zoology and Anthropology, University of Göttingen, Göttingen, Germany
| | - Thorsten Mielke
- Max Planck Institute for Molecular Genetics, Berlin, Microscopy and Cryo-Electron Microscopy Group, Berlin, Germany
| | - Stephan J Sigrist
- Institute for Biology/Genetics, Freie Universität Berlin, Berlin, Germany. .,NeuroCure Cluster of Excellence, Charité Universitätsmedizin, Berlin, Germany.
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39
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Gao R, van den Brink RL, Pfeffer T, Voytek B. Neuronal timescales are functionally dynamic and shaped by cortical microarchitecture. eLife 2020; 9:e61277. [PMID: 33226336 PMCID: PMC7755395 DOI: 10.7554/elife.61277] [Citation(s) in RCA: 103] [Impact Index Per Article: 25.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 11/22/2020] [Indexed: 12/21/2022] Open
Abstract
Complex cognitive functions such as working memory and decision-making require information maintenance over seconds to years, from transient sensory stimuli to long-term contextual cues. While theoretical accounts predict the emergence of a corresponding hierarchy of neuronal timescales, direct electrophysiological evidence across the human cortex is lacking. Here, we infer neuronal timescales from invasive intracranial recordings. Timescales increase along the principal sensorimotor-to-association axis across the entire human cortex, and scale with single-unit timescales within macaques. Cortex-wide transcriptomic analysis shows direct alignment between timescales and expression of excitation- and inhibition-related genes, as well as genes specific to voltage-gated transmembrane ion transporters. Finally, neuronal timescales are functionally dynamic: prefrontal cortex timescales expand during working memory maintenance and predict individual performance, while cortex-wide timescales compress with aging. Thus, neuronal timescales follow cytoarchitectonic gradients across the human cortex and are relevant for cognition in both short and long terms, bridging microcircuit physiology with macroscale dynamics and behavior.
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Affiliation(s)
- Richard Gao
- Department of Cognitive Science, University of California, San DiegoLa JollaUnited States
| | - Ruud L van den Brink
- Section Computational Cognitive Neuroscience, Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-EppendorfHamburgGermany
| | - Thomas Pfeffer
- Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu FabraBarcelonaSpain
| | - Bradley Voytek
- Department of Cognitive Science, University of California, San DiegoLa JollaUnited States
- Halıcıoğlu Data Science Institute, University of California, San DiegoLa JollaUnited States
- Neurosciences Graduate Program, University of California, San DiegoLa JollaUnited States
- Kavli Institute for Brain and Mind, University of California, San DiegoLa JollaUnited States
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Seenivasan P, Narayanan R. Efficient phase coding in hippocampal place cells. PHYSICAL REVIEW RESEARCH 2020; 2:033393. [PMID: 32984841 PMCID: PMC7116119 DOI: 10.1103/physrevresearch.2.033393] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Neural codes have been postulated to build efficient representations of the external world. The hippocampus, an encoding system, employs neuronal firing rates and spike phases to encode external space. Although the biophysical origin of such codes is at a single neuronal level, the role of neural components in efficient coding is not understood. The complexity of this problem lies in the dimensionality of the parametric space encompassing neural components, and is amplified by the enormous biological heterogeneity observed in each parameter. A central question that spans encoding systems therefore is how neurons arrive at efficient codes in the face of widespread biological heterogeneities. To answer this, we developed a conductance-based spiking model for phase precession, a phase code of external space exhibited by hippocampal place cells. Our model accounted for several experimental observations on place cell firing and electrophysiology: the emergence of phase precession from exact spike timings of conductance-based models with neuron-specific ion channels and receptors; biological heterogeneities in neural components and excitability; the emergence of subthreshold voltage ramp, increased firing rate, enhanced theta power within the place field; a signature reduction in extracellular theta frequency compared to its intracellular counterpart; and experience-dependent asymmetry in firing-rate profile. We formulated phase-coding efficiency, using Shannon's information theory, as an information maximization problem with spike phase as the response and external space within a single place field as the stimulus. We employed an unbiased stochastic search spanning an 11-dimensional neural space, involving thousands of iterations that accounted for the biophysical richness and neuron-to-neuron heterogeneities. We found a small subset of models that exhibited efficient spatial information transfer through the phase code, and investigated the distinguishing features of this subpopulation at the parametric and functional scales. At the parametric scale, which spans the molecular components that defined the neuron, several nonunique parametric combinations with weak pairwise correlations yielded models with similar high phase-coding efficiency. Importantly, placing additional constraints on these models in terms of matching other aspects of hippocampal neural responses did not hamper parametric degeneracy. We provide quantitative evidence demonstrating this parametric degeneracy to be a consequence of a many-to-one relationship between the different parameters and phase-coding efficiency. At the functional scale, involving the cellular-scale neural properties, our analyses revealed an important higher-order constraint that was exclusive to models exhibiting efficient phase coding. Specifically, we found a counterbalancing negative correlation between neuronal gain and the strength of external synaptic inputs as a critical functional constraint for the emergence of efficient phase coding. These observations implicate intrinsic neural properties as important contributors in effectuating such counterbalance, which can be achieved by recruiting nonunique parametric combinations. Finally, we show that a change in afferent statistics, manifesting as input asymmetry onto these neuronal models, induced an adaptive shift in the phase code that preserved its efficiency. Together, our analyses unveil parametric degeneracy as a mechanism to harness widespread neuron-to-neuron heterogeneity towards accomplishing stable and efficient encoding, provided specific higher-order functional constraints on the relationship of neural gain to external inputs are satisfied.
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Montangie L, Miehl C, Gjorgjieva J. Autonomous emergence of connectivity assemblies via spike triplet interactions. PLoS Comput Biol 2020; 16:e1007835. [PMID: 32384081 PMCID: PMC7239496 DOI: 10.1371/journal.pcbi.1007835] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Revised: 05/20/2020] [Accepted: 03/31/2020] [Indexed: 01/08/2023] Open
Abstract
Non-random connectivity can emerge without structured external input driven by activity-dependent mechanisms of synaptic plasticity based on precise spiking patterns. Here we analyze the emergence of global structures in recurrent networks based on a triplet model of spike timing dependent plasticity (STDP), which depends on the interactions of three precisely-timed spikes, and can describe plasticity experiments with varying spike frequency better than the classical pair-based STDP rule. We derive synaptic changes arising from correlations up to third-order and describe them as the sum of structural motifs, which determine how any spike in the network influences a given synaptic connection through possible connectivity paths. This motif expansion framework reveals novel structural motifs under the triplet STDP rule, which support the formation of bidirectional connections and ultimately the spontaneous emergence of global network structure in the form of self-connected groups of neurons, or assemblies. We propose that under triplet STDP assembly structure can emerge without the need for externally patterned inputs or assuming a symmetric pair-based STDP rule common in previous studies. The emergence of non-random network structure under triplet STDP occurs through internally-generated higher-order correlations, which are ubiquitous in natural stimuli and neuronal spiking activity, and important for coding. We further demonstrate how neuromodulatory mechanisms that modulate the shape of the triplet STDP rule or the synaptic transmission function differentially promote structural motifs underlying the emergence of assemblies, and quantify the differences using graph theoretic measures. Emergent non-random connectivity structures in different brain regions are tightly related to specific patterns of neural activity and support diverse brain functions. For instance, self-connected groups of neurons, known as assemblies, have been proposed to represent functional units in brain circuits and can emerge even without patterned external instruction. Here we investigate the emergence of non-random connectivity in recurrent networks using a particular plasticity rule, triplet STDP, which relies on the interaction of spike triplets and can capture higher-order statistical dependencies in neural activity. We derive the evolution of the synaptic strengths in the network and explore the conditions for the self-organization of connectivity into assemblies. We demonstrate key differences of the triplet STDP rule compared to the classical pair-based rule in terms of how assemblies are formed, including the realistic asymmetric shape and influence of novel connectivity motifs on network plasticity driven by higher-order correlations. Assembly formation depends on the specific shape of the STDP window and synaptic transmission function, pointing towards an important role of neuromodulatory signals on formation of intrinsically generated assemblies.
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Affiliation(s)
- Lisandro Montangie
- Computation in Neural Circuits Group, Max Planck Institute for Brain Research, Frankfurt, Germany
| | - Christoph Miehl
- Computation in Neural Circuits Group, Max Planck Institute for Brain Research, Frankfurt, Germany
- Technical University of Munich, School of Life Sciences, Freising, Germany
| | - Julijana Gjorgjieva
- Computation in Neural Circuits Group, Max Planck Institute for Brain Research, Frankfurt, Germany
- Technical University of Munich, School of Life Sciences, Freising, Germany
- * E-mail:
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42
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Masoli S, Tognolina M, Laforenza U, Moccia F, D'Angelo E. Parameter tuning differentiates granule cell subtypes enriching transmission properties at the cerebellum input stage. Commun Biol 2020; 3:222. [PMID: 32385389 PMCID: PMC7210112 DOI: 10.1038/s42003-020-0953-x] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Accepted: 04/13/2020] [Indexed: 02/06/2023] Open
Abstract
The cerebellar granule cells (GrCs) are classically described as a homogeneous neuronal population discharging regularly without adaptation. We show that GrCs in fact generate diverse response patterns to current injection and synaptic activation, ranging from adaptation to acceleration of firing. Adaptation was predicted by parameter optimization in detailed computational models based on available knowledge on GrC ionic channels. The models also predicted that acceleration required additional mechanisms. We found that yet unrecognized TRPM4 currents specifically accounted for firing acceleration and that adapting GrCs outperformed accelerating GrCs in transmitting high-frequency mossy fiber (MF) bursts over a background discharge. This implied that GrC subtypes identified by their electroresponsiveness corresponded to specific neurotransmitter release probability values. Simulations showed that fine-tuning of pre- and post-synaptic parameters generated effective MF-GrC transmission channels, which could enrich the processing of input spike patterns and enhance spatio-temporal recoding at the cerebellar input stage.
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Affiliation(s)
- Stefano Masoli
- Department of Brain and Behavioral Sciences, University of Pavia, Via Forlanini 6, 27100, Pavia, Italy
| | - Marialuisa Tognolina
- Department of Brain and Behavioral Sciences, University of Pavia, Via Forlanini 6, 27100, Pavia, Italy
| | - Umberto Laforenza
- Department of Molecular Medicine, University of Pavia, Via Forlanini 6, 27100, Pavia, Italy
| | - Francesco Moccia
- Department of Biology and Biotechnology, University of Pavia, Via Forlanini 6, 27100, Pavia, Italy
| | - Egidio D'Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Via Forlanini 6, 27100, Pavia, Italy. .,Brain Connectivity Center, IRCCS Mondino Foundation, Via Mondino 2, 27100, Pavia, Italy.
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43
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Mishra P, Narayanan R. Heterogeneities in intrinsic excitability and frequency-dependent response properties of granule cells across the blades of the rat dentate gyrus. J Neurophysiol 2020; 123:755-772. [PMID: 31913748 PMCID: PMC7052640 DOI: 10.1152/jn.00443.2019] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Revised: 12/25/2019] [Accepted: 01/07/2020] [Indexed: 12/18/2022] Open
Abstract
The dentate gyrus (DG), the input gate to the hippocampus proper, is anatomically segregated into three different sectors, namely, the suprapyramidal blade, the crest region, and the infrapyramidal blade. Although there are well-established differences between these sectors in terms of neuronal morphology, connectivity patterns, and activity levels, differences in electrophysiological properties of granule cells within these sectors have remained unexplored. Here, employing somatic whole cell patch-clamp recordings from the rat DG, we demonstrate that granule cells in these sectors manifest considerable heterogeneities in their intrinsic excitability, temporal summation, action potential characteristics, and frequency-dependent response properties. Across sectors, these neurons showed positive temporal summation of their responses to inputs mimicking excitatory postsynaptic currents and showed little to no sag in their voltage responses to pulse currents. Consistently, the impedance amplitude profile manifested low-pass characteristics and the impedance phase profile lacked positive phase values at all measured frequencies and voltages and for all sectors. Granule cells in all sectors exhibited class I excitability, with broadly linear firing rate profiles, and granule cells in the crest region fired significantly fewer action potentials compared with those in the infrapyramidal blade. Finally, we found weak pairwise correlations across the 18 different measurements obtained individually from each of the three sectors, providing evidence that these measurements are indeed reporting distinct aspects of neuronal physiology. Together, our analyses show that granule cells act as integrators of afferent information and emphasize the need to account for the considerable physiological heterogeneities in assessing their roles in information encoding and processing.NEW & NOTEWORTHY We employed whole cell patch-clamp recordings from granule cells in the three subregions of the rat dentate gyrus to demonstrate considerable heterogeneities in their intrinsic excitability, temporal summation, action potential characteristics, and frequency-dependent response properties. Across sectors, granule cells did not express membrane potential resonance, and their impedance profiles lacked inductive phase leads at all measured frequencies. Our analyses also show that granule cells manifest class I excitability characteristics, categorizing them as integrators of afferent information.
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Affiliation(s)
- Poonam Mishra
- Cellular Neurophysiology Laboratory, Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
| | - Rishikesh Narayanan
- Cellular Neurophysiology Laboratory, Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
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44
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Woitkuhn J, Ender A, Beuschel CB, Maglione M, Matkovic-Rachid T, Huang S, Lehmann M, Geiger JRP, Sigrist SJ. The Unc13A isoform is important for phasic release and olfactory memory formation at mushroom body synapses. J Neurogenet 2020; 34:106-114. [PMID: 31980003 DOI: 10.1080/01677063.2019.1710146] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
The cellular analysis of mushroom body (MB)-dependent memory forming processes is far advanced, whereas, the molecular and physiological understanding of their synaptic basis lags behind. Recent analysis of the Drosophila olfactory system showed that Unc13A, a member of the M(Unc13) release factor family, promotes a phasic, high release probability component, while Unc13B supports a slower tonic release component, reflecting their different nanoscopic positioning within individual active zones. We here use STED super-resolution microscopy of MB lobe synapses to show that Unc13A clusters closer to the active zone centre than Unc13B. Unc13A specifically supported phasic transmission and short-term plasticity of Kenyon cell:output neuron synapses, measured by combining electrophysiological recordings of output neurons with optogenetic stimulation. Knockdown of unc13A within Kenyon cells provoked drastic deficits of olfactory aversive short-term and anaesthesia-sensitive middle-term memory. Knockdown of unc13B provoked milder memory deficits. Thus, a low frequency domain transmission component is probably crucial for the proper representation of memory-associated activity patterns, consistent with sparse Kenyon cell activation during memory acquisition and retrieval. Notably, Unc13A/B ratios appeared highly diversified across MB lobes, leaving room for an interplay of activity components in memory encoding and retrieval.
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Affiliation(s)
- Jennifer Woitkuhn
- Institute for Biology/Genetics, Freie Universitaet Berlin, Berlin, Germany
| | - Anatoli Ender
- German Center for Neurodegenerative Disorders, Charité Universitätsmedizin Berlin, Berlin, Germany
| | | | - Marta Maglione
- Institute for Biology/Genetics, Freie Universitaet Berlin, Berlin, Germany.,NeuroCure Cluster of Excellence, Charité Universitätsmedizin, Berlin, Germany.,Cellular Imaging, Light Microscopy, Leibniz-Forschungsinstitut fur Molekulare Pharmakologie (FMP), Berlin, Germany
| | | | - Sheng Huang
- Institute for Biology/Genetics, Freie Universitaet Berlin, Berlin, Germany
| | - Martin Lehmann
- Cellular Imaging, Light Microscopy, Leibniz-Forschungsinstitut fur Molekulare Pharmakologie (FMP), Berlin, Germany
| | - Joerg R P Geiger
- NeuroCure Cluster of Excellence, Charité Universitätsmedizin, Berlin, Germany.,Institut fur Neurophysiologie, Charité Universitätsmedizin, Berlin, Germany
| | - Stephan J Sigrist
- Institute for Biology/Genetics, Freie Universitaet Berlin, Berlin, Germany.,German Center for Neurodegenerative Disorders, Charité Universitätsmedizin Berlin, Berlin, Germany.,NeuroCure Cluster of Excellence, Charité Universitätsmedizin, Berlin, Germany
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45
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Jain A, Narayanan R. Degeneracy in the emergence of spike-triggered average of hippocampal pyramidal neurons. Sci Rep 2020; 10:374. [PMID: 31941985 PMCID: PMC6962224 DOI: 10.1038/s41598-019-57243-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Accepted: 12/26/2019] [Indexed: 12/15/2022] Open
Abstract
Hippocampal pyramidal neurons are endowed with signature excitability characteristics, exhibit theta-frequency selectivity - manifesting as impedance resonance and as a band-pass structure in the spike-triggered average (STA) - and coincidence detection tuned for gamma-frequency inputs. Are there specific constraints on molecular-scale (ion channel) properties in the concomitant emergence of cellular-scale encoding (feature detection and selectivity) and excitability characteristics? Here, we employed a biophysically-constrained unbiased stochastic search strategy involving thousands of conductance-based models, spanning 11 active ion channels, to assess the concomitant emergence of 14 different electrophysiological measurements. Despite the strong biophysical and physiological constraints, we found models that were similar in terms of their spectral selectivity, operating mode along the integrator-coincidence detection continuum and intrinsic excitability characteristics. The parametric combinations that resulted in these functionally similar models were non-unique with weak pair-wise correlations. Employing virtual knockout of individual ion channels in these functionally similar models, we found a many-to-many relationship between channels and physiological characteristics to mediate this degeneracy, and predicted a dominant role for HCN and transient potassium channels in regulating hippocampal neuronal STA. Our analyses reveals the expression of degeneracy, that results from synergistic interactions among disparate channel components, in the concomitant emergence of neuronal excitability and encoding characteristics.
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Affiliation(s)
- Abha Jain
- Cellular Neurophysiology Laboratory, Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India.,Undergraduate program, Indian Institute of Science, Bangalore, India
| | - Rishikesh Narayanan
- Cellular Neurophysiology Laboratory, Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India.
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46
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Kastanenka KV, Moreno-Bote R, De Pittà M, Perea G, Eraso-Pichot A, Masgrau R, Poskanzer KE, Galea E. A roadmap to integrate astrocytes into Systems Neuroscience. Glia 2020; 68:5-26. [PMID: 31058383 PMCID: PMC6832773 DOI: 10.1002/glia.23632] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Revised: 04/08/2019] [Accepted: 04/09/2019] [Indexed: 12/14/2022]
Abstract
Systems neuroscience is still mainly a neuronal field, despite the plethora of evidence supporting the fact that astrocytes modulate local neural circuits, networks, and complex behaviors. In this article, we sought to identify which types of studies are necessary to establish whether astrocytes, beyond their well-documented homeostatic and metabolic functions, perform computations implementing mathematical algorithms that sub-serve coding and higher-brain functions. First, we reviewed Systems-like studies that include astrocytes in order to identify computational operations that these cells may perform, using Ca2+ transients as their encoding language. The analysis suggests that astrocytes may carry out canonical computations in a time scale of subseconds to seconds in sensory processing, neuromodulation, brain state, memory formation, fear, and complex homeostatic reflexes. Next, we propose a list of actions to gain insight into the outstanding question of which variables are encoded by such computations. The application of statistical analyses based on machine learning, such as dimensionality reduction and decoding in the context of complex behaviors, combined with connectomics of astrocyte-neuronal circuits, is, in our view, fundamental undertakings. We also discuss technical and analytical approaches to study neuronal and astrocytic populations simultaneously, and the inclusion of astrocytes in advanced modeling of neural circuits, as well as in theories currently under exploration such as predictive coding and energy-efficient coding. Clarifying the relationship between astrocytic Ca2+ and brain coding may represent a leap forward toward novel approaches in the study of astrocytes in health and disease.
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Affiliation(s)
- Ksenia V. Kastanenka
- Department of Neurology, MassGeneral Institute for Neurodegenerative Diseases, Massachusetts General Hospital and Harvard Medical School, Massachusetts 02129, USA
| | - Rubén Moreno-Bote
- Department of Information and Communications Technologies, Center for Brain and Cognition and Universitat Pompeu Fabra, 08018 Barcelona, Spain
- ICREA, 08010 Barcelona, Spain
| | | | | | - Abel Eraso-Pichot
- Departament de Bioquímica, Institut de Neurociències i Universitat Autònoma de Barcelona, Bellaterra, 08193 Barcelona, Spain
| | - Roser Masgrau
- Departament de Bioquímica, Institut de Neurociències i Universitat Autònoma de Barcelona, Bellaterra, 08193 Barcelona, Spain
| | - Kira E. Poskanzer
- Department of Biochemistry & Biophysics, Neuroscience Graduate Program, and Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, California 94143, USA
- Equally contributing authors
| | - Elena Galea
- ICREA, 08010 Barcelona, Spain
- Departament de Bioquímica, Institut de Neurociències i Universitat Autònoma de Barcelona, Bellaterra, 08193 Barcelona, Spain
- Equally contributing authors
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47
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Wang J, Cauwenberghs G, Broccard FD. Neuromorphic Dynamical Synapses With Reconfigurable Voltage-Gated Kinetics. IEEE Trans Biomed Eng 2019; 67:1831-1840. [PMID: 31647418 DOI: 10.1109/tbme.2019.2948809] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
OBJECTIVE Although biological synapses express a large variety of receptors in neuronal membranes, the current hardware implementation of neuromorphic synapses often rely on simple models ignoring the heterogeneity of synaptic transmission. Our objective is to emulate different types of synapses with distinct properties. METHODS Conductance-based chemical and electrical synapses were implemented between silicon neurons on a fully programmable and reconfigurable, biophysically realistic neuromorphic VLSI chip. Different synaptic properties were achieved by configuring on-chip digital parameters for the conductances, reversal potentials, and voltage dependence of the channel kinetics. The measured I-V characteristics of the artificial synapses were compared with biological data. RESULTS We reproduced the response properties of five different types of chemical synapses, including both excitatory ( AMPA, NMDA) and inhibitory ( GABAA, GABAC, glycine) ionotropic receptors. In addition, electrical synapses were implemented in a small network of four silicon neurons. CONCLUSION Our work extends the repertoire of synapse types between silicon neurons, providing greater flexibility for the design and implementation of biologically realistic neural networks on neuromorphic chips. SIGNIFICANCE A higher synaptic heterogeneity in neuromorphic chips is relevant for the hardware implementation of energy-efficient population codes as well as for dynamic clamp applications where neural models are implemented in neuromorphic VLSI hardware.
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48
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Fulterer A, Andlauer TFM, Ender A, Maglione M, Eyring K, Woitkuhn J, Lehmann M, Matkovic-Rachid T, Geiger JRP, Walter AM, Nagel KI, Sigrist SJ. Active Zone Scaffold Protein Ratios Tune Functional Diversity across Brain Synapses. Cell Rep 2019; 23:1259-1274. [PMID: 29719243 DOI: 10.1016/j.celrep.2018.03.126] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2017] [Revised: 02/28/2018] [Accepted: 03/27/2018] [Indexed: 01/20/2023] Open
Abstract
High-throughput electron microscopy has started to reveal synaptic connectivity maps of single circuits and whole brain regions, for example, in the Drosophila olfactory system. However, efficacy, timing, and frequency tuning of synaptic vesicle release are also highly diversified across brain synapses. These features critically depend on the nanometer-scale coupling distance between voltage-gated Ca2+ channels (VGCCs) and the synaptic vesicle release machinery. Combining light super resolution microscopy with in vivo electrophysiology, we show here that two orthogonal scaffold proteins (ELKS family Bruchpilot, BRP, and Syd-1) cluster-specific (M)Unc13 release factor isoforms either close (BRP/Unc13A) or further away (Syd-1/Unc13B) from VGCCs across synapses of the Drosophila olfactory system, resulting in different synapse-characteristic forms of short-term plasticity. Moreover, BRP/Unc13A versus Syd-1/Unc13B ratios were different between synapse types. Thus, variation in tightly versus loosely coupled scaffold protein/(M)Unc13 modules can tune synapse-type-specific release features, and "nanoscopic molecular fingerprints" might identify synapses with specific temporal features.
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Affiliation(s)
- Andreas Fulterer
- Institute for Biology/Genetics, Freie Universität Berlin, 14195 Berlin, Germany
| | - Till F M Andlauer
- Max Planck Institute of Psychiatry, 80804 Munich, Germany; Department of Neurology, Klinikum rechts der Isar, Technical University of Munich, 81675 Munich, Germany
| | - Anatoli Ender
- German Center for Neurodegenerative Disorders, Charité Universitätsmedizin Berlin, 10117 Berlin, Germany
| | - Marta Maglione
- Institute for Biology/Genetics, Freie Universität Berlin, 14195 Berlin, Germany; NeuroCure Cluster of Excellence, Charité Universitätsmedizin, 10117 Berlin, Germany; Leibniz-Forschungsinstitut für Molekulare Pharmakologie (FMP), 13125 Berlin, Germany
| | - Katherine Eyring
- Neuroscience Institute, NYU School of Medicine, New York, NY 10016, USA
| | - Jennifer Woitkuhn
- Institute for Biology/Genetics, Freie Universität Berlin, 14195 Berlin, Germany
| | - Martin Lehmann
- Leibniz-Forschungsinstitut für Molekulare Pharmakologie (FMP), 13125 Berlin, Germany
| | | | - Joerg R P Geiger
- NeuroCure Cluster of Excellence, Charité Universitätsmedizin, 10117 Berlin, Germany; Institut für Neurophysiologie, Charité Universitätsmedizin, 10117 Berlin, Germany
| | - Alexander M Walter
- Leibniz-Forschungsinstitut für Molekulare Pharmakologie (FMP), 13125 Berlin, Germany
| | - Katherine I Nagel
- Neuroscience Institute, NYU School of Medicine, New York, NY 10016, USA.
| | - Stephan J Sigrist
- Institute for Biology/Genetics, Freie Universität Berlin, 14195 Berlin, Germany; NeuroCure Cluster of Excellence, Charité Universitätsmedizin, 10117 Berlin, Germany.
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49
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Rathour RK, Narayanan R. Degeneracy in hippocampal physiology and plasticity. Hippocampus 2019; 29:980-1022. [PMID: 31301166 PMCID: PMC6771840 DOI: 10.1002/hipo.23139] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Revised: 05/27/2019] [Accepted: 06/25/2019] [Indexed: 12/17/2022]
Abstract
Degeneracy, defined as the ability of structurally disparate elements to perform analogous function, has largely been assessed from the perspective of maintaining robustness of physiology or plasticity. How does the framework of degeneracy assimilate into an encoding system where the ability to change is an essential ingredient for storing new incoming information? Could degeneracy maintain the balance between the apparently contradictory goals of the need to change for encoding and the need to resist change towards maintaining homeostasis? In this review, we explore these fundamental questions with the mammalian hippocampus as an example encoding system. We systematically catalog lines of evidence, spanning multiple scales of analysis that point to the expression of degeneracy in hippocampal physiology and plasticity. We assess the potential of degeneracy as a framework to achieve the conjoint goals of encoding and homeostasis without cross-interferences. We postulate that biological complexity, involving interactions among the numerous parameters spanning different scales of analysis, could establish disparate routes towards accomplishing these conjoint goals. These disparate routes then provide several degrees of freedom to the encoding-homeostasis system in accomplishing its tasks in an input- and state-dependent manner. Finally, the expression of degeneracy spanning multiple scales offers an ideal reconciliation to several outstanding controversies, through the recognition that the seemingly contradictory disparate observations are merely alternate routes that the system might recruit towards accomplishment of its goals.
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Affiliation(s)
- Rahul K. Rathour
- Cellular Neurophysiology LaboratoryMolecular Biophysics Unit, Indian Institute of ScienceBangaloreIndia
| | - Rishikesh Narayanan
- Cellular Neurophysiology LaboratoryMolecular Biophysics Unit, Indian Institute of ScienceBangaloreIndia
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50
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Abstract
The suprachiasmatic nucleus (SCN) of the hypothalamus is remarkable. Despite numbering only about 10,000 neurons on each side of the third ventricle, the SCN is our principal circadian clock, directing the daily cycles of behaviour and physiology that set the tempo of our lives. When this nucleus is isolated in organotypic culture, its autonomous timing mechanism can persist indefinitely, with precision and robustness. The discovery of the cell-autonomous transcriptional and post-translational feedback loops that drive circadian activity in the SCN provided a powerful exemplar of the genetic specification of complex mammalian behaviours. However, the analysis of circadian time-keeping is moving beyond single cells. Technical and conceptual advances, including intersectional genetics, multidimensional imaging and network theory, are beginning to uncover the circuit-level mechanisms and emergent properties that make the SCN a uniquely precise and robust clock. However, much remains unknown about the SCN, not least the intrinsic properties of SCN neurons, its circuit topology and the neuronal computations that these circuits support. Moreover, the convention that the SCN is a neuronal clock has been overturned by the discovery that astrocytes are an integral part of the timepiece. As a test bed for examining the relationships between genes, cells and circuits in sculpting complex behaviours, the SCN continues to offer powerful lessons and opportunities for contemporary neuroscience.
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