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Neural Circuit Policies Imposing Visual Perceptual Autonomy. Neural Process Lett 2023. [DOI: 10.1007/s11063-023-11194-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
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2
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Biswas R, Shlizerman E. Statistical perspective on functional and causal neural connectomics: The Time-Aware PC algorithm. PLoS Comput Biol 2022; 18:e1010653. [PMID: 36374908 PMCID: PMC9704761 DOI: 10.1371/journal.pcbi.1010653] [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: 05/06/2022] [Revised: 11/28/2022] [Accepted: 10/12/2022] [Indexed: 11/16/2022] Open
Abstract
The representation of the flow of information between neurons in the brain based on their activity is termed the causal functional connectome. Such representation incorporates the dynamic nature of neuronal activity and causal interactions between them. In contrast to connectome, the causal functional connectome is not directly observed and needs to be inferred from neural time series. A popular statistical framework for inferring causal connectivity from observations is the directed probabilistic graphical modeling. Its common formulation is not suitable for neural time series since it was developed for variables with independent and identically distributed static samples. In this work, we propose to model and estimate the causal functional connectivity from neural time series using a novel approach that adapts directed probabilistic graphical modeling to the time series scenario. In particular, we develop the Time-Aware PC (TPC) algorithm for estimating the causal functional connectivity, which adapts the PC algorithm-a state-of-the-art method for statistical causal inference. We show that the model outcome of TPC has the properties of reflecting causality of neural interactions such as being non-parametric, exhibits the directed Markov property in a time-series setting, and is predictive of the consequence of counterfactual interventions on the time series. We demonstrate the utility of the methodology to obtain the causal functional connectome for several datasets including simulations, benchmark datasets, and recent multi-array electro-physiological recordings from the mouse visual cortex.
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Affiliation(s)
- Rahul Biswas
- Department of Statistics, University of Washington, Seattle, Washington, United States of America
| | - Eli Shlizerman
- Department of Applied Mathematics and Department of Electrical & Computer Engineering, University of Washington, Seattle, Washington, United States of America
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Zhang Y, Guo Y, Zhang Z, Chen M, Wang S, Zhang J. Universal framework for reconstructing complex networks and node dynamics from discrete or continuous dynamics data. Phys Rev E 2022; 106:034315. [PMID: 36266816 DOI: 10.1103/physreve.106.034315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Accepted: 06/15/2022] [Indexed: 06/16/2023]
Abstract
Many dynamical processes of complex systems can be understood as the dynamics of a group of nodes interacting on a given network structure. However, finding such interaction structure and node dynamics from time series of node behaviors is tough. Conventional methods focus on either network structure inference task or dynamics reconstruction problem, very few of them can work well on both. This paper proposes a universal framework for reconstructing network structure and node dynamics at the same time from observed time-series data of nodes. We use a differentiable Bernoulli sampling process to generate a candidate network structure, and we use neural networks to simulate the node dynamics based on the candidate network. We then adjust all the parameters with a stochastic gradient descent algorithm to maximize the likelihood function defined on the data. The experiments show that our model can recover various network structures and node dynamics at the same time with high accuracy. It can also work well on binary, discrete, and continuous time-series data, and the reconstruction results are robust against noise and missing information.
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Affiliation(s)
- Yan Zhang
- School of Systems Science, Beijing Normal University, Beijing 100875, China
| | - Yu Guo
- Software Institute, Nanjing University, Nanjing 210093, China
| | - Zhang Zhang
- School of Systems Science, Beijing Normal University, Beijing 100875, China
| | - Mengyuan Chen
- China TravelSky Mobile Technology Co., Ltd, Beijing 100029, China
| | - Shuo Wang
- School of Systems Science, Beijing Normal University, Beijing 100875, China
| | - Jiang Zhang
- School of Systems Science, Beijing Normal University, Beijing 100875, China; Swarma Research, Beijing 102308, China
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Biswas R, Shlizerman E. Statistical Perspective on Functional and Causal Neural Connectomics: A Comparative Study. Front Syst Neurosci 2022; 16:817962. [PMID: 35308566 PMCID: PMC8924489 DOI: 10.3389/fnsys.2022.817962] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 01/19/2022] [Indexed: 11/13/2022] Open
Abstract
Representation of brain network interactions is fundamental to the translation of neural structure to brain function. As such, methodologies for mapping neural interactions into structural models, i.e., inference of functional connectome from neural recordings, are key for the study of brain networks. While multiple approaches have been proposed for functional connectomics based on statistical associations between neural activity, association does not necessarily incorporate causation. Additional approaches have been proposed to incorporate aspects of causality to turn functional connectomes into causal functional connectomes, however, these methodologies typically focus on specific aspects of causality. This warrants a systematic statistical framework for causal functional connectomics that defines the foundations of common aspects of causality. Such a framework can assist in contrasting existing approaches and to guide development of further causal methodologies. In this work, we develop such a statistical guide. In particular, we consolidate the notions of associations and representations of neural interaction, i.e., types of neural connectomics, and then describe causal modeling in the statistics literature. We particularly focus on the introduction of directed Markov graphical models as a framework through which we define the Directed Markov Property—an essential criterion for examining the causality of proposed functional connectomes. We demonstrate how based on these notions, a comparative study of several existing approaches for finding causal functional connectivity from neural activity can be conducted. We proceed by providing an outlook ahead regarding the additional properties that future approaches could include to thoroughly address causality.
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Affiliation(s)
- Rahul Biswas
- Department of Statistics, University of Washington, Seattle, WA, United States
| | - Eli Shlizerman
- Department of Applied Mathematics, Department of Electrical & Computer Engineering, University of Washington, Seattle, WA, United States
- *Correspondence: Eli Shlizerman
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Hori S, Mitani S. The transcription factor unc-130/FOXD3/4 contributes to the biphasic calcium response required to optimize avoidance behavior. Sci Rep 2022; 12:1907. [PMID: 35115609 PMCID: PMC8814005 DOI: 10.1038/s41598-022-05942-0] [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: 05/21/2021] [Accepted: 01/13/2022] [Indexed: 11/12/2022] Open
Abstract
The central neural network optimizes avoidance behavior depending on the nociceptive stimulation intensity and is essential for survival. How the property of hub neurons that enables the selection of behaviors is genetically defined is not well understood. We show that the transcription factor unc-130, a human FOXD3/4 ortholog, is required to optimize avoidance behavior depending on stimulus strength in Caenorhabditis elegans. unc-130 is necessary for both ON responses (calcium decreases) and OFF responses (calcium increases) in AIBs, central neurons of avoidance optimization. Ablation of predicted upstream inhibitory neurons reduces the frequency of turn behavior, suggesting that optimization needs both calcium responses. At the molecular level, unc-130 upregulates the expression of at least three genes: nca-2, a homolog of the vertebrate cation leak channel NALCN; glr-1, an AMPA-type glutamate receptor; and eat-4, a hypothetical L-glutamate transmembrane transporter in the central neurons of optimization. unc-130 shows more limited regulation in optimizing behavior than an atonal homolog lin-32, and unc-130 and lin-32 appear to act in parallel molecular pathways. Our findings suggest that unc-130 is required for the establishment of some AIB identities to optimize avoidance behavior.
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Affiliation(s)
- Sayaka Hori
- Department of Physiology, Tokyo Women's Medical University School of Medicine, Tokyo, 162-8666, Japan
| | - Shohei Mitani
- Department of Physiology, Tokyo Women's Medical University School of Medicine, Tokyo, 162-8666, Japan.
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Batta I, Yao Q, Sabrin KM, Dovrolis C. A weighted network analysis framework for the hourglass effect-And its application in the C. elegans connectome. PLoS One 2021; 16:e0249846. [PMID: 34705821 PMCID: PMC8550382 DOI: 10.1371/journal.pone.0249846] [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: 04/05/2021] [Accepted: 09/09/2021] [Indexed: 11/18/2022] Open
Abstract
Understanding hierarchy and modularity in natural as well as technological networks is of utmost importance. A major aspect of such analysis involves identifying the nodes that are crucial to the overall processing structure of the network. More recently, the approach of hourglass analysis has been developed for the purpose of quantitatively analyzing whether only a few intermediate nodes mediate the information processing between a large number of inputs and outputs of a network. We develop a new framework for hourglass analysis that takes network weights into account while identifying the core nodes and the extent of hourglass effect in a given weighted network. We use this framework to study the structural connectome of the C. elegans and identify intermediate neurons that form the core of sensori-motor pathways in the organism. Our results show that the neurons forming the core of the connectome show significant differences across the male and hermaphrodite sexes, with most core nodes in the male concentrated in sex-organs while they are located in the head for the hermaphrodite. Our work demonstrates that taking weights into account for network analysis framework leads to emergence of different network patterns in terms of identification of core nodes and hourglass structure in the network, which otherwise would be missed by unweighted approaches.
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Affiliation(s)
- Ishaan Batta
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America
| | - Qihang Yao
- School of Computer Science, Georgia Institute of Technology, Atlanta, Georgia, United States of America
| | - Kaeser M. Sabrin
- School of Computer Science, Georgia Institute of Technology, Atlanta, Georgia, United States of America
| | - Constantine Dovrolis
- School of Computer Science, Georgia Institute of Technology, Atlanta, Georgia, United States of America
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Batta I, Yao Q, Sabrin KM, Dovrolis C. A Weighted Network Analysis Framework for the Hourglass Effect — and its Application in the C. Elegans Connectome.. [DOI: 10.1101/2021.03.19.436224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/27/2023]
Abstract
ABSTRACTUnderstanding hierarchy and modularity in natural as well as technological networks is of utmost importance. A major aspect of such analysis involves identifying the nodes that are crucial to the overall processing structure of the network. More recently, the approach of hourglass analysis has been developed for the purpose of quantitatively analyzing whether only a few intermediate nodes mediate the information processing between a large number of inputs and outputs of a network. We develop a new framework for hourglass analysis that takes network weights into account while identifying the core nodes and the extent of hourglass effect in a given weighted network. We use this framework to study the structural connectome of theC. elegansand identify intermediate neurons that form the core of sensori-motor pathways in the organism. Our results show that the neurons forming the core of the connectome show significant differences across the male and hermaphrodite sexes, with most core nodes in the male concentrated in sex-organs while they are located in the head for the hermaphrodite. Our work demonstrates that taking weights into account for network analysis framework leads to emergence of different network patterns in terms of identification of core nodes and hourglass structure in the network, which otherwise would be missed by unweighted approaches.
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George VK, Puppo F, Silva GA. Computing Temporal Sequences Associated With Dynamic Patterns on the C. elegans Connectome. Front Syst Neurosci 2021; 15:564124. [PMID: 33767613 PMCID: PMC7985353 DOI: 10.3389/fnsys.2021.564124] [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: 05/20/2020] [Accepted: 02/04/2021] [Indexed: 12/03/2022] Open
Abstract
Understanding how the structural connectivity and spatial geometry of a network constrains the dynamics it is able to support is an active and open area of research. We simulated the plausible dynamics resulting from the known C. elegans connectome using a recent model and theoretical analysis that computes the dynamics of neurobiological networks by focusing on how local interactions among connected neurons give rise to the global dynamics in an emergent way. We studied the dynamics which resulted from stimulating a chemosensory neuron (ASEL) in a known feeding circuit, both in isolation and embedded in the full connectome. We show that contralateral motorneuron activations in ventral (VB) and dorsal (DB) classes of motorneurons emerged from the simulations, which are qualitatively similar to rhythmic motorneuron firing pattern associated with locomotion of the worm. One interpretation of these results is that there is an inherent-and we propose-purposeful structural wiring to the C. elegans connectome that has evolved to serve specific behavioral functions. To study network signaling pathways responsible for the dynamics we developed an analytic framework that constructs Temporal Sequences (TSeq), time-ordered walks of signals on graphs. We found that only 5% of TSeq are preserved between the isolated feeding network relative to its embedded counterpart. The remaining 95% of signaling pathways computed in the isolated network are not present in the embedded network. This suggests a cautionary note for computational studies of isolated neurobiological circuits and networks.
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Affiliation(s)
- Vivek Kurien George
- Department of Bioengineering, University of California, San Diego, San Diego, CA, United States
- Center for Engineered Natural Intelligence, University of California, San Diego, San Diego, CA, United States
| | - Francesca Puppo
- Department of Bioengineering, University of California, San Diego, San Diego, CA, United States
- Center for Engineered Natural Intelligence, University of California, San Diego, San Diego, CA, United States
- BioCircuits Institute, University of California, San Diego, San Diego, CA, United States
| | - Gabriel A. Silva
- Department of Bioengineering, University of California, San Diego, San Diego, CA, United States
- Center for Engineered Natural Intelligence, University of California, San Diego, San Diego, CA, United States
- BioCircuits Institute, University of California, San Diego, San Diego, CA, United States
- Department of Neurosciences, University of California, San Diego, San Diego, CA, United States
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Conte D, Borisyuk R, Hull M, Roberts A. A simple method defines 3D morphology and axon projections of filled neurons in a small CNS volume: Steps toward understanding functional network circuitry. J Neurosci Methods 2020; 351:109062. [PMID: 33383055 DOI: 10.1016/j.jneumeth.2020.109062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 12/11/2020] [Accepted: 12/22/2020] [Indexed: 10/22/2022]
Abstract
BACKGROUND Fundamental to understanding neuronal network function is defining neuron morphology, location, properties, and synaptic connectivity in the nervous system. A significant challenge is to reconstruct individual neuron morphology and connections at a whole CNS scale and bring together functional and anatomical data to understand the whole network. NEW METHOD We used a PC controlled micropositioner to hold a fixed whole mount of Xenopus tadpole CNS and replace the stage on a standard microscope. This allowed direct recording in 3D coordinates of features and axon projections of one or two neurons dye-filled during whole-cell recording to study synaptic connections. Neuron reconstructions were normalised relative to the ventral longitudinal axis of the nervous system. Coordinate data were stored as simple text files. RESULTS Reconstructions were at 1 μm resolution, capturing axon lengths in mm. The output files were converted to SWC format and visualised in 3D reconstruction software NeuRomantic. Coordinate data are tractable, allowing correction for histological artefacts. Through normalisation across multiple specimens we could infer features of network connectivity of mapped neurons of different types. COMPARISON WITH EXISTING METHODS Unlike other methods using fluorescent markers and utilising large-scale imaging, our method allows direct acquisition of 3D data on neurons whose properties and synaptic connections have been studied using whole-cell recording. CONCLUSIONS This method can be used to reconstruct neuron 3D morphology and follow axon projections in the CNS. After normalisation to a common CNS framework, inferences on network connectivity at a whole nervous system scale contribute to network modelling to understand CNS function.
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Affiliation(s)
- Deborah Conte
- School of Biological Sciences, University of Bristol, 24 Tyndall Avenue, Bristol, BS8 1TQ, United Kingdom.
| | - Roman Borisyuk
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Harrison Building, North Park Road, Exeter, EX4 4QF, United Kingdom; Institute of Mathematical Problems of Biology, the Branch of Keldysh Institute of Applied Mathematics, Russian Academy of Sciences, Pushchino, 142290, Russia; School of Computing, Engineering and Mathematics, University of Plymouth, PL4 8AA, United Kingdom.
| | - Mike Hull
- School of Biological Sciences, University of Bristol, 24 Tyndall Avenue, Bristol, BS8 1TQ, United Kingdom; Institute for Adaptive and Neural Computation, University of Edinburgh, Edinburgh, EH8 9AB, United Kingdom.
| | - Alan Roberts
- School of Biological Sciences, University of Bristol, 24 Tyndall Avenue, Bristol, BS8 1TQ, United Kingdom.
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Driving the connectome by-wire: Comment on "What would a synthetic connectome look like?" by Ithai Rabinowitch. Phys Life Rev 2019; 33:25-27. [PMID: 31735640 DOI: 10.1016/j.plrev.2019.11.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Accepted: 11/07/2019] [Indexed: 02/07/2023]
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DiLoreto EM, Chute CD, Bryce S, Srinivasan J. Novel Technological Advances in Functional Connectomics in C. elegans. J Dev Biol 2019; 7:E8. [PMID: 31018525 PMCID: PMC6630759 DOI: 10.3390/jdb7020008] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Revised: 02/08/2019] [Accepted: 02/13/2019] [Indexed: 12/11/2022] Open
Abstract
The complete structure and connectivity of the Caenorhabditis elegans nervous system ("mind of a worm") was first published in 1986, representing a critical milestone in the field of connectomics. The reconstruction of the nervous system (connectome) at the level of synapses provided a unique perspective of understanding how behavior can be coded within the nervous system. The following decades have seen the development of technologies that help understand how neural activity patterns are connected to behavior and modulated by sensory input. Investigations on the developmental origins of the connectome highlight the importance of role of neuronal cell lineages in the final connectivity matrix of the nervous system. Computational modeling of neuronal dynamics not only helps reconstruct the biophysical properties of individual neurons but also allows for subsequent reconstruction of whole-organism neuronal network models. Hence, combining experimental datasets with theoretical modeling of neurons generates a better understanding of organismal behavior. This review discusses some recent technological advances used to analyze and perturb whole-organism neuronal function along with developments in computational modeling, which allows for interrogation of both local and global neural circuits, leading to different behaviors. Combining these approaches will shed light into how neural networks process sensory information to generate the appropriate behavioral output, providing a complete understanding of the worm nervous system.
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Affiliation(s)
- Elizabeth M DiLoreto
- Biology and Biotechnology Department, Worcester Polytechnic Institute, Worcester, MA 01605, USA.
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Kim J, Leahy W, Shlizerman E. Neural Interactome: Interactive Simulation of a Neuronal System. Front Comput Neurosci 2019; 13:8. [PMID: 30930759 PMCID: PMC6425397 DOI: 10.3389/fncom.2019.00008] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2018] [Accepted: 01/30/2019] [Indexed: 01/14/2023] Open
Abstract
Connectivity and biophysical processes determine the functionality of neuronal networks. We, therefore, developed a real-time framework, called Neural Interactome,, to simultaneously visualize and interact with the structure and dynamics of such networks. Neural Interactome is a cross-platform framework, which combines graph visualization with the simulation of neural dynamics, or experimentally recorded multi neural time series, to allow application of stimuli to neurons to examine network responses. In addition, Neural Interactome supports structural changes, such as disconnection of neurons from the network (ablation feature). Neural dynamics can be explored on a single neuron level (using a zoom feature), back in time (using a review feature), and recorded (using presets feature). The development of the Neural Interactome was guided by generic concepts to be applicable to neuronal networks with different neural connectivity and dynamics. We implement the framework using a model of the nervous system of Caenorhabditis elegans (C. elegans) nematode, a model organism with resolved connectome and neural dynamics. We show that Neural Interactome assists in studying neural response patterns associated with locomotion and other stimuli. In particular, we demonstrate how stimulation and ablation help in identifying neurons that shape particular dynamics. We examine scenarios that were experimentally studied, such as touch response circuit, and explore new scenarios that did not undergo elaborate experimental studies.
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Affiliation(s)
- Jimin Kim
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States
| | - William Leahy
- Department of Applied Mathematics, University of Washington, Seattle, WA, United States
| | - Eli Shlizerman
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States.,Department of Applied Mathematics, University of Washington, Seattle, WA, United States
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Larson SD, Gleeson P, Brown AEX. Connectome to behaviour: modelling Caenorhabditis elegans at cellular resolution. Philos Trans R Soc Lond B Biol Sci 2018; 373:20170366. [PMID: 30201832 PMCID: PMC6158229 DOI: 10.1098/rstb.2017.0366] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/17/2018] [Indexed: 12/20/2022] Open
Abstract
It has been 30 years since the 'mind of the worm' was published in Philosophical Transactions B (White et al 1986 Phil. Trans. R. Soc. Lond. B314, 1-340). Predicting Caenorhabditis elegans' behaviour from its wiring diagram has been an enduring challenge since then. This special theme issue of Philosophical Transactions B combines research from neuroscientists, physicists, mathematicians and engineers to discuss advances in neural activity imaging, behaviour quantification and multiscale simulations, and how they are bringing the goal of whole-animal modelling at cellular resolution within reach.This article is part of a discussion meeting issue 'Connectome to behaviour: modelling C. elegans at cellular resolution'.
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Affiliation(s)
| | - Padraig Gleeson
- OpenWorm Foundation, Boston, MA, USA
- Department of Neuroscience, Physiology and Pharmacology, University College London WC1E 6BT, UK
| | - André E X Brown
- MRC London Institute of Medical Sciences, London W12 0N, UK
- Institute of Clinical Sciences, Imperial College London, London SW7 2AZ, UK
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