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Avila B, Augusto P, Zimmer M, Serafino M, Makse HA. Fibration symmetries and cluster synchronization in the Caenorhabditis elegans connectome. ARXIV 2024:arXiv:2305.19367v2. [PMID: 37396607 PMCID: PMC10312817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Capturing how the Caenorhabditis elegans connectome structure gives rise to its neuron functionality remains unclear. It is through fiber symmetries found in its neuronal connectivity that synchronization of a group of neurons can be determined. To understand these we investigate graph symmetries and search for such in the symmetrized versions of the forward and backward locomotive sub-networks of the Caenorhabditi elegans worm neuron network. The use of ordinarily differential equations simulations admissible to these graphs are used to validate the predictions of these fiber symmetries and are compared to the more restrictive orbit symmetries. Additionally fibration symmetries are used to decompose these graphs into their fundamental building blocks which reveal units formed by nested loops or multilayered fibers. It is found that fiber symmetries of the connectome can accurately predict neuronal synchronization even under not idealized connectivity as long as the dynamics are within stable regimes of simulations.
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Affiliation(s)
- Bryant Avila
- Levich Institute, Physics Department, City College of New York, New York, NY, USA
| | - Pedro Augusto
- Vienna Biocenter PhD Program, Doctoral School of the University of Vienna and Medical University of Vienna, Vienna, Austria
- Department of Neuroscience and Developmental Biology, Vienna Biocenter (VBC), University of Vienna, Vienna, Austria
| | - Manuel Zimmer
- Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), University of Vienna, Vienna, Austria
- Department of Neuroscience and Developmental Biology, Vienna Biocenter (VBC), University of Vienna, Vienna, Austria
| | - Matteo Serafino
- Levich Institute, Physics Department, City College of New York, New York, NY, USA
| | - Hernán A. Makse
- Levich Institute, Physics Department, City College of New York, New York, NY, USA
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2
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Avila B, Serafino M, Augusto P, Zimmer M, Makse HA. Fibration symmetries and cluster synchronization in the Caenorhabditis elegans connectome. PLoS One 2024; 19:e0297669. [PMID: 38598455 PMCID: PMC11006206 DOI: 10.1371/journal.pone.0297669] [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: 05/19/2023] [Accepted: 01/11/2024] [Indexed: 04/12/2024] Open
Abstract
Capturing how the Caenorhabditis elegans connectome structure gives rise to its neuron functionality remains unclear. It is through fiber symmetries found in its neuronal connectivity that synchronization of a group of neurons can be determined. To understand these we investigate graph symmetries and search for such in the symmetrized versions of the forward and backward locomotive sub-networks of the Caenorhabditi elegans worm neuron network. The use of ordinarily differential equations simulations admissible to these graphs are used to validate the predictions of these fiber symmetries and are compared to the more restrictive orbit symmetries. Additionally fibration symmetries are used to decompose these graphs into their fundamental building blocks which reveal units formed by nested loops or multilayered fibers. It is found that fiber symmetries of the connectome can accurately predict neuronal synchronization even under not idealized connectivity as long as the dynamics are within stable regimes of simulations.
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Affiliation(s)
- Bryant Avila
- Physics Department, Levich Institute, City College of New York, New York, NY, United Stated of America
| | - Matteo Serafino
- Physics Department, Levich Institute, City College of New York, New York, NY, United Stated of America
| | - Pedro Augusto
- Vienna Biocenter PhD Program, Doctoral School of the University of Vienna and Medical University of Vienna, Vienna, Austria
- Department of Neuroscience and Developmental Biology, Vienna Biocenter (VBC), University of Vienna, Vienna, Austria
| | - Manuel Zimmer
- Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), University of Vienna, Vienna, Austria
- Department of Neuroscience and Developmental Biology, Vienna Biocenter (VBC), University of Vienna, Vienna, Austria
| | - Hernán A. Makse
- Physics Department, Levich Institute, City College of New York, New York, NY, United Stated of America
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3
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Ecker A, Egas Santander D, Bolaños-Puchet S, Isbister JB, Reimann MW. Cortical cell assemblies and their underlying connectivity: An in silico study. PLoS Comput Biol 2024; 20:e1011891. [PMID: 38466752 PMCID: PMC10927091 DOI: 10.1371/journal.pcbi.1011891] [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: 11/09/2023] [Accepted: 02/05/2024] [Indexed: 03/13/2024] Open
Abstract
Recent developments in experimental techniques have enabled simultaneous recordings from thousands of neurons, enabling the study of functional cell assemblies. However, determining the patterns of synaptic connectivity giving rise to these assemblies remains challenging. To address this, we developed a complementary, simulation-based approach, using a detailed, large-scale cortical network model. Using a combination of established methods we detected functional cell assemblies from the stimulus-evoked spiking activity of 186,665 neurons. We studied how the structure of synaptic connectivity underlies assembly composition, quantifying the effects of thalamic innervation, recurrent connectivity, and the spatial arrangement of synapses on dendrites. We determined that these features reduce up to 30%, 22%, and 10% of the uncertainty of a neuron belonging to an assembly. The detected assemblies were activated in a stimulus-specific sequence and were grouped based on their position in the sequence. We found that the different groups were affected to different degrees by the structural features we considered. Additionally, connectivity was more predictive of assembly membership if its direction aligned with the temporal order of assembly activation, if it originated from strongly interconnected populations, and if synapses clustered on dendritic branches. In summary, reversing Hebb's postulate, we showed how cells that are wired together, fire together, quantifying how connectivity patterns interact to shape the emergence of assemblies. This includes a qualitative aspect of connectivity: not just the amount, but also the local structure matters; from the subcellular level in the form of dendritic clustering to the presence of specific network motifs.
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Affiliation(s)
- András Ecker
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Daniela Egas Santander
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Sirio Bolaños-Puchet
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - James B. Isbister
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Michael W. Reimann
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
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Crosser JT, Brinkman BAW. Applications of information geometry to spiking neural network activity. Phys Rev E 2024; 109:024302. [PMID: 38491696 DOI: 10.1103/physreve.109.024302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 01/10/2024] [Indexed: 03/18/2024]
Abstract
The space of possible behaviors that complex biological systems may exhibit is unimaginably vast, and these systems often appear to be stochastic, whether due to variable noisy environmental inputs or intrinsically generated chaos. The brain is a prominent example of a biological system with complex behaviors. The number of possible patterns of spikes emitted by a local brain circuit is combinatorially large, although the brain may not make use of all of them. Understanding which of these possible patterns are actually used by the brain, and how those sets of patterns change as properties of neural circuitry change is a major goal in neuroscience. Recently, tools from information geometry have been used to study embeddings of probabilistic models onto a hierarchy of model manifolds that encode how model outputs change as a function of their parameters, giving a quantitative notion of "distances" between outputs. We apply this method to a network model of excitatory and inhibitory neural populations to understand how the competition between membrane and synaptic response timescales shapes the network's information geometry. The hyperbolic embedding allows us to identify the statistical parameters to which the model behavior is most sensitive, and demonstrate how the ranking of these coordinates changes with the balance of excitation and inhibition in the network.
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Affiliation(s)
- Jacob T Crosser
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York 11794, USA and Department of Neurobiology and Behavior, Stony Brook University, Stony Brook, New York 11794, USA
| | - Braden A W Brinkman
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York 11794, USA and Department of Neurobiology and Behavior, Stony Brook University, Stony Brook, New York 11794, USA
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Esparza J, Sebastián ER, de la Prida LM. From cell types to population dynamics: Making hippocampal manifolds physiologically interpretable. Curr Opin Neurobiol 2023; 83:102800. [PMID: 37898015 DOI: 10.1016/j.conb.2023.102800] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 09/27/2023] [Accepted: 09/28/2023] [Indexed: 10/30/2023]
Abstract
The study of the hippocampal code is gaining momentum. While the physiological approach targets the contribution of individual cells as determined by genetic, biophysical and circuit factors, the field pushes for a population dynamic approach that considers the representation of behavioural variables by a large number of neurons. In this alternative framework, neuronal activity is projected into low-dimensional manifolds. These manifolds can reveal the structure of population representations, but their physiological interpretation is challenging. Here, we review the recent literature and propose that integrating information regarding behavioral traits, local field potential oscillations and cell-type-specificity into neural manifolds offers strategies to make them interpretable at the physiological level.
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Shomali SR, Rasuli SN, Ahmadabadi MN, Shimazaki H. Uncovering hidden network architecture from spiking activities using an exact statistical input-output relation of neurons. Commun Biol 2023; 6:169. [PMID: 36792689 PMCID: PMC9932086 DOI: 10.1038/s42003-023-04511-z] [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: 02/03/2022] [Accepted: 01/20/2023] [Indexed: 02/17/2023] Open
Abstract
Identifying network architecture from observed neural activities is crucial in neuroscience studies. A key requirement is knowledge of the statistical input-output relation of single neurons in vivo. By utilizing an exact analytical solution of the spike-timing for leaky integrate-and-fire neurons under noisy inputs balanced near the threshold, we construct a framework that links synaptic type, strength, and spiking nonlinearity with the statistics of neuronal population activity. The framework explains structured pairwise and higher-order interactions of neurons receiving common inputs under different architectures. We compared the theoretical predictions with the activity of monkey and mouse V1 neurons and found that excitatory inputs given to pairs explained the observed sparse activity characterized by strong negative triple-wise interactions, thereby ruling out the alternative explanation by shared inhibition. Moreover, we showed that the strong interactions are a signature of excitatory rather than inhibitory inputs whenever the spontaneous rate is low. We present a guide map of neural interactions that help researchers to specify the hidden neuronal motifs underlying observed interactions found in empirical data.
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Affiliation(s)
- Safura Rashid Shomali
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, 19395-5746, Iran.
| | - Seyyed Nader Rasuli
- grid.418744.a0000 0000 8841 7951School of Physics, Institute for Research in Fundamental Sciences (IPM), Tehran, 19395-5531 Iran ,grid.411872.90000 0001 2087 2250Department of Physics, University of Guilan, Rasht, 41335-1914 Iran
| | - Majid Nili Ahmadabadi
- grid.46072.370000 0004 0612 7950Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, 14395-515 Iran
| | - Hideaki Shimazaki
- Graduate School of Informatics, Kyoto University, Kyoto, 606-8501, Japan. .,Center for Human Nature, Artificial Intelligence, and Neuroscience (CHAIN), Hokkaido University, Hokkaido, 060-0812, Japan.
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Evaluating the statistical similarity of neural network activity and connectivity via eigenvector angles. Biosystems 2023; 223:104813. [PMID: 36460172 DOI: 10.1016/j.biosystems.2022.104813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 11/15/2022] [Accepted: 11/15/2022] [Indexed: 12/02/2022]
Abstract
Neural systems are networks, and strategic comparisons between multiple networks are a prevalent task in many research scenarios. In this study, we construct a statistical test for the comparison of matrices representing pairwise aspects of neural networks, in particular, the correlation between spiking activity and connectivity. The "eigenangle test" quantifies the similarity of two matrices by the angles between their ranked eigenvectors. We calibrate the behavior of the test for use with correlation matrices using stochastic models of correlated spiking activity and demonstrate how it compares to classical two-sample tests, such as the Kolmogorov-Smirnov distance, in the sense that it is able to evaluate also structural aspects of pairwise measures. Furthermore, the principle of the eigenangle test can be applied to compare the similarity of adjacency matrices of certain types of networks. Thus, the approach can be used to quantitatively explore the relationship between connectivity and activity with the same metric. By applying the eigenangle test to the comparison of connectivity matrices and correlation matrices of a random balanced network model before and after a specific synaptic rewiring intervention, we gauge the influence of connectivity features on the correlated activity. Potential applications of the eigenangle test include simulation experiments, model validation, and data analysis.
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Hu B, Guan ZH, Chen G, Chen CLP. Neuroscience and Network Dynamics Toward Brain-Inspired Intelligence. IEEE TRANSACTIONS ON CYBERNETICS 2022; 52:10214-10227. [PMID: 33909581 DOI: 10.1109/tcyb.2021.3071110] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
This article surveys the interdisciplinary research of neuroscience, network science, and dynamic systems, with emphasis on the emergence of brain-inspired intelligence. To replicate brain intelligence, a practical way is to reconstruct cortical networks with dynamic activities that nourish the brain functions, instead of using only artificial computing networks. The survey provides a complex network and spatiotemporal dynamics (abbr. network dynamics) perspective for understanding the brain and cortical networks and, furthermore, develops integrated approaches of neuroscience and network dynamics toward building brain-inspired intelligence with learning and resilience functions. Presented are fundamental concepts and principles of complex networks, neuroscience, and hybrid dynamic systems, as well as relevant studies about the brain and intelligence. Other promising research directions, such as brain science, data science, quantum information science, and machine behavior are also briefly discussed toward future applications.
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Srivastava P, Fotiadis P, Parkes L, Bassett DS. The expanding horizons of network neuroscience: From description to prediction and control. Neuroimage 2022; 258:119250. [PMID: 35659996 PMCID: PMC11164099 DOI: 10.1016/j.neuroimage.2022.119250] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 04/15/2022] [Accepted: 04/25/2022] [Indexed: 01/11/2023] Open
Abstract
The field of network neuroscience has emerged as a natural framework for the study of the brain and has been increasingly applied across divergent problems in neuroscience. From a disciplinary perspective, network neuroscience originally emerged as a formal integration of graph theory (from mathematics) and neuroscience (from biology). This early integration afforded marked utility in describing the interconnected nature of neural units, both structurally and functionally, and underscored the relevance of that interconnection for cognition and behavior. But since its inception, the field has not remained static in its methodological composition. Instead, it has grown to use increasingly advanced graph-theoretic tools and to bring in several other disciplinary perspectives-including machine learning and systems engineering-that have proven complementary. In doing so, the problem space amenable to the discipline has expanded markedly. In this review, we discuss three distinct flavors of investigation in state-of-the-art network neuroscience: (i) descriptive network neuroscience, (ii) predictive network neuroscience, and (iii) a perturbative network neuroscience that draws on recent advances in network control theory. In considering each area, we provide a brief summary of the approaches, discuss the nature of the insights obtained, and highlight future directions.
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Affiliation(s)
- Pragya Srivastava
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Panagiotis Fotiadis
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Neuroscience, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Linden Parkes
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Physics & Astronomy, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Neurology, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia PA 19104, USA; Santa Fe Institute, Santa Fe NM 87501, USA.
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A set of hub neurons and non-local connectivity features support global brain dynamics in C. elegans. Curr Biol 2022; 32:3443-3459.e8. [PMID: 35809568 DOI: 10.1016/j.cub.2022.06.039] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 05/17/2022] [Accepted: 06/13/2022] [Indexed: 11/20/2022]
Abstract
The wiring architecture of neuronal networks is assumed to be a strong determinant of their dynamical computations. An ongoing effort in neuroscience is therefore to generate comprehensive synapse-resolution connectomes alongside brain-wide activity maps. However, the structure-function relationship, i.e., how the anatomical connectome and neuronal dynamics relate to each other on a global scale, remains unsolved. Systematically, comparing graph features in the C. elegans connectome with correlations in nervous system-wide neuronal dynamics, we found that few local connectivity motifs and mostly other non-local features such as triplet motifs and input similarities can predict functional relationships between neurons. Surprisingly, quantities such as connection strength and amount of common inputs do not improve these predictions, suggesting that the network's topology is sufficient. We demonstrate that hub neurons in the connectome are key to these relevant graph features. Consistently, inhibition of multiple hub neurons specifically disrupts brain-wide correlations. Thus, we propose that a set of hub neurons and non-local connectivity features provide an anatomical substrate for global brain dynamics.
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Biswas T, Fitzgerald JE. Geometric framework to predict structure from function in neural networks. PHYSICAL REVIEW RESEARCH 2022; 4:023255. [PMID: 37635906 PMCID: PMC10456994 DOI: 10.1103/physrevresearch.4.023255] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 08/29/2023]
Abstract
Neural computation in biological and artificial networks relies on the nonlinear summation of many inputs. The structural connectivity matrix of synaptic weights between neurons is a critical determinant of overall network function, but quantitative links between neural network structure and function are complex and subtle. For example, many networks can give rise to similar functional responses, and the same network can function differently depending on context. Whether certain patterns of synaptic connectivity are required to generate specific network-level computations is largely unknown. Here we introduce a geometric framework for identifying synaptic connections required by steady-state responses in recurrent networks of threshold-linear neurons. Assuming that the number of specified response patterns does not exceed the number of input synapses, we analytically calculate the solution space of all feedforward and recurrent connectivity matrices that can generate the specified responses from the network inputs. A generalization accounting for noise further reveals that the solution space geometry can undergo topological transitions as the allowed error increases, which could provide insight into both neuroscience and machine learning. We ultimately use this geometric characterization to derive certainty conditions guaranteeing a nonzero synapse between neurons. Our theoretical framework could thus be applied to neural activity data to make rigorous anatomical predictions that follow generally from the model architecture.
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Affiliation(s)
- Tirthabir Biswas
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia 20147, USA
- Department of Physics, Loyola University, New Orleans, Louisiana 70118, USA
| | - James E. Fitzgerald
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia 20147, USA
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Liu J, Lu W, Yuan Y, Xin K, Zhao P, Gu X, Raza A, Huo H, Li Z, Fang T. Fixed Point Attractor Theory Bridges Structure and Function in C. elegans Neuronal Network. Front Neurosci 2022; 16:808824. [PMID: 35546893 PMCID: PMC9085386 DOI: 10.3389/fnins.2022.808824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 03/30/2022] [Indexed: 11/27/2022] Open
Abstract
Understanding the structure–function relationship in a neuronal network is one of the major challenges in neuroscience research. Despite increasing researches at circuit connectivity and neural network structure, their structure-based biological interpretability remains unclear. Based on the attractor theory, here we develop an analytical framework that links neural circuit structures and their functions together through fixed point attractor in Caenorhabditis elegans. In this framework, we successfully established the structural condition for the emergence of multiple fixed points in C. elegans connectome. Then we construct a finite state machine to explain how functions related to bistable phenomena at the neural activity and behavioral levels are encoded. By applying the proposed framework to the command circuit in C. elegans, we provide a circuit level interpretation for the forward-reverse switching behaviors. Interestingly, network properties of the command circuit and first layer amphid interneuron circuit can also be inferred from their functions in this framework. Our research indicates the reliability of the fixed point attractor bridging circuit structure and functions, suggesting its potential applicability to more complex neuronal circuits in other species.
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Affiliation(s)
- Jian Liu
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China
- Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, China
| | - Wenbo Lu
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Ye Yuan
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China
- Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, China
| | - Kuankuan Xin
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Peng Zhao
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China
- Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, China
| | - Xiao Gu
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China
- Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, China
| | - Asif Raza
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China
- Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, China
| | - Hong Huo
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China
- Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, China
- *Correspondence: Hong Huo,
| | - Zhaoyu Li
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
- Zhaoyu Li,
| | - Tao Fang
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China
- Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, China
- Tao Fang,
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13
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Abstract
The full functionality of the brain is determined by its molecular, cellular and circuit structure. Modern neuroscience now prioritizes the mapping of whole brain connectomes by detecting all direct neuron to neuron synaptic connections, a feat first accomplished for C. elegans, a full reconstruction of a 302-neuron nervous system. Efforts at Janelia Research Campus will soon reconstruct the whole brain connectomes of a larval and an adult Drosophila. These connectomes will provide a framework for incorporating detailed neural circuit information that Drosophila neuroscientists have gathered over decades. But when viewed in the context of a whole brain, it becomes difficult to isolate the contributions of distinct circuits, whether sensory systems or higher brain regions. The complete wiring diagram tells us that sensory information is not only processed in separate channels, but that even the earliest sensory layers are strongly synaptically interconnected. In the higher brain, long-range projections densely interconnect major brain regions and convergence centers that integrate input from different sensory systems. Furthermore, we also need to understand the impact of neuronal communication beyond direct synaptic modulation. Nevertheless, all of this can be pursued with Drosophila, combining connectomics with a diverse array of genetic tools and behavioral paradigms that provide effective approaches to entire brain function.
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Affiliation(s)
- Katrin Vogt
- Department of Physics, Harvard University, Cambridge, MA, USA.,Center for Brain Science, Harvard University, Cambridge, MA, USA
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