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Towards a virtual C. Elegans: A framework for simulation and visualization of the neuromuscular system in a 3D physical environment

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Abstract

The nematode C. elegans is the only animal with a known neuronal wiring diagram, or “connectome”. During the last three decades, extensive studies of the C. elegans have provided wide-ranging data about it, but few systematic ways of integrating these data into a dynamic model have been put forward. Here we present a detailed demonstration of a virtual C. elegans aimed at integrating these data in the form of a 3D dynamic model operating in a simulated physical environment. Our current demonstration includes a realistic flexible worm body model, muscular system and a partially implemented ventral neural cord. Our virtual C. elegans demonstrates successful forward and backward locomotion when sending sinusoidal patterns of neuronal activity to groups of motor neurons. To account for the relatively slow propagation velocity and the attenuation of neuronal signals, we introduced “pseudo neurons” into our model to simulate simplified neuronal dynamics. The pseudo neurons also provide a good way of visualizing the nervous system’s structure and activity dynamics.

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... ;https://doi.org/10.1101https://doi.org/10. /2021 Since similar network activity may arise from very different sets of neuronal and network parameters (Prinz et al., 2004), and since the biophysical parameters of neurons in this circuit have not been measured simultaneously and in large populations -we explored a wide range of biologically plausible values for the circuit's biophysical parameters (Goodman et al., 1998;Lindsay et al., 2011;Palyanov et al., 2012;Rakowski and Karbowski, 2017;Rakowski et al., 2013;Roehrig, 1998;Varshney et al., 2011;Wicks et al., 1996) (Figure 3A-B). For each of our 7 parameters, we used 7 different values; overall, we simulated 7 7 = 823,543 different parameter combinations and compared the activity of the simulated motor neurons to the worms' actual behavior in response to nociceptive stimuli. ...
... As the values of different biophysical parameters of the neurons in this circuit are unknown, we used 7 different parameters for the simulations. For each parameter, we used 7 different values that were equally spaced on a logarithmic scale from a biologically reasonable range (Goodman et al., 1998;Lindsay et al., 2011;Palyanov et al., 2012;Rakowski and Karbowski, 2017;Rakowski et al., 2013;Roehrig, 1998;Varshney et al., 2011;Wicks et al., 1996). Rm values were within 15kΩ-1500kΩ, capacitance values were 0.1uF-10uF, values of conductance of gap junctions and chemical synapses were 1pS-1nS, and the basal input values of the interneurons triggered a voltage change of 1mV-75mV. ...
... The specific parameters of all the neurons in the circuit were assumed to be the same. Rin was calculated separately for each set of parameters, using the changing value of Rm and a fixed value of the surface area of a cell, which was taken to be 15 ·10 !6 2 (Goodman et al., 1998;Palyanov et al., 2012;Rakowski et al., 2013;Varshney et al., 2011;Wicks et al., 1996). To induce a voltagechange of a desired magnitude, we calculated the currents to the sensory neurons and to the interneurons, using the values of each parameter set. ...
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How sexually dimorphic behavior is encoded in the nervous system is poorly understood. Here, we characterize the dimorphic nociceptive behavior in C. elegans and study the underlying circuits, which are composed of the same neurons but are wired differently. We show that while sensory transduction is similar in the two sexes, the downstream network topology markedly shapes behavior. We fit a network model that replicates the observed dimorphic behavior in response to external stimuli, and use it to predict simple network rewirings that would switch the behavior between the sexes. We then show experimentally that these subtle synaptic rewirings indeed flip behavior. Strikingly, when presented with aversive cues, rewired males were compromised in finding mating partners, suggesting that network topologies that enable efficient avoidance of noxious cues have a reproductive "cost". Our results present a deconstruction of the design of a neural circuit that controls sexual behavior, and how to reprogram it.
... In addition to various experimental approaches, computational modeling is becoming an increasingly important technique because it facilitates the validation of hypotheses and theories regarding neural circuit operation through the integration of existing observations into computer models (Sporns, 2013;Chaudhuri and Fiete, 2016;Churchland and Abbott, 2016;Denève and Machens, 2016). Indeed, extensive studies on neural network models covering Caenorhabditis elegans (Palyanov et al., 2011;Szigeti et al., 2014;Izquierdo and Beer, 2016;Sarma et al., 2018), insects (Wessnitzer and Webb, 2006), rodents, and primates (Markram, 2006;Izhikevich and Edelman, 2008;Eliasmith et al., 2012) have greatly contributed to our understanding of neural circuit functions at the systems level. However, computer modeling also faces two major challenges: (1) a large number of neural network models were built to simulate specific functions in one or few brain regions (Izhikevich and Edelman, 2008;Eliasmith et al., 2012). ...
... The activity displayed in panels (B-G) corresponds to the data points labeled by the roman numerals I-VI in the panel (A), respectively. of the Drosophila. This model is the first of its kind for any species, except for C. elegans (Palyanov et al., 2011;Szigeti et al., 2014;Izquierdo and Beer, 2016;Sarma et al., 2018), which, however, is not considered to possess a brain. The proposed fly brain model, although still in its early stage of development, already exhibits several intriguing dynamical properties when compared to a randomized brain network. ...
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... It is not very different from reported 2D approaches since the cylinders are completely rigid. Another 3D system is presented in Palyanov et al. (2011) and has been used for chemotaxis experiment visualizations (Demin and Vityaev, 2014). An OpenGL application shows a plate with a worm moving on it. ...
... The 3D system presented in Palyanov et al. (2011) is the starting point of the OpenWorm project regarding the modeling and visualization of the locomotion of C. elegans. (Vella et al., 2013) uses a physics engine that works with Smoothed Particle Hydrodynamics. ...
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The Si elegans platform targets the complete virtualization of the nematode Caenorhabditis elegans, and its environment. This paper presents a suite of unified web-based Graphical User Interfaces (GUIs) as the main user interaction point, and discusses their underlying technologies and methods. The user-friendly features of this tool suite enable users to graphically create neuron and network models, and behavioral experiments, without requiring knowledge of domain-specific computer-science tools. The framework furthermore allows the graphical visualization of all simulation results using a worm locomotion and neural activity viewer. Models, experiment definitions and results can be exported in a machine-readable format, thereby facilitating reproducible and cross-platform execution of in silico C. elegans experiments in other simulation environments. This is made possible by a novel XML-based behavioral experiment definition encoding format, a NeuroML XML-based model generation and network configuration description language, and their associated GUIs. User survey data confirms the platform usability and functionality, and provides insights into future directions for web-based simulation GUIs of C. elegans and other living organisms. The tool suite is available online to the scientific community and its source code has been made available.
... Computational models of the nervous system are developed at multiple scales to answer questions about how low level interactions between biological entities lead to higher level functions [1 -3]. Models based on the nematode Caenorhabditis elegans have been created at many levels including individual neurons [4] and muscles [5], subcircuits responsible for generating specific behaviours [6][7][8][9], body-wide processes including locomotion [10 -12], and detailed nervous system/musculature models [13]. Each of these models selects a subset of anatomical and physiological properties known to exist in the worm and can address a specific set of questions relevant to that level of detail. ...
... Additional data from PyOpenWorm on cell classes (sensory, motor neuron, interneuron, etc.), receptors and neurotransmitters are added to the cell models on generation. 13 Muscle cells can also be generated and these are organized into four quadrants, each containing 24 muscles for convenience. 14 These muscles are positioned in four rows separated from the worm body to facilitate visualization of connections/activity (as shown in figure 2c). ...
Article
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The OpenWorm project has the ambitious goal of producing a highly detailed in silico model of the nematode Caenorhabditis elegans. A crucial part of this work will be a model of the nervous system encompassing all known cell types and connections. The appropriate level of biophysical detail required in the neuronal model to reproduce observed high-level behaviours in the worm has yet to be determined. For this reason, we have developed a framework, c302, that allows different instances of neuronal networks to be generated incorporating varying levels of anatomical and physiological detail, which can be investigated and refined independently or linked to other tools developed in the OpenWorm modelling toolchain. This article is part of a discussion meeting issue ‘Connectome to behaviour: modelling C. elegans at cellular resolution’.
... In addition to various experimental approaches, computational modeling is becoming an increasingly important technique because it facilitates the validation of hypotheses and theories regarding neural circuit operation through the integration of existing observations into computer models (Churchland and Abbott 2016;Chaudhuri and Fiete 2016;Denève and Machens 2016;Sporns 2013). Indeed, extensive studies on neural network models covering Caenorhabditis elegans (Szigeti et al. 2014;Izquierdo and Beer 2016;Palyanov et al. 2011), insects (Wessnitzer and Webb 2006), rodents, and primates (Markram 2006;Izhikevich and Edelman 2008;Eliasmith et al. 2012) have greatly contributed to our understanding of neural circuit functions at the systems level. However, computer modeling also faces two major challenges: 1) a large number of neural network models were built to simulate specific functions in one or few brain regions (Izhikevich and Edelman 2008;Eliasmith et al. 2012). ...
... In the present study, we constructed the first brain-wide computational model based on the cellular-level connectome of the Drosophila. This model is the first of its kind for any species, except for C. elegans (Szigeti et al. 2014;Izquierdo and Beer 2016;Palyanov et al. 2011), which, however, is not considered to possess a brain. The proposed fly brain model, although still in its early stage of development, already exhibits several intriguing dynamical properties when compared to a randomized brain network. ...
Preprint
Computer simulations play an important role in testing hypotheses, integrating knowledge, and providing predictions of neural circuit functions. While considerable effort has been dedicated into simulating primate or rodent brains, the fruit fly (Drosophila melanogaster) is becoming a promising model animal in computational neuroscience for its small brain size, complex cognitive behavior, and abundancy of data available from genes to circuits. Moreover, several Drosophila connectome projects have generated a large number of neuronal images that account for a significant portion of the brain, making a systematic investigation of the whole brain circuit possible. Supported by FlyCircuit (http://www.flycircuit.tw), one of the largest Drosophila neuron image databases, we began a long-term project with the goal to construct a whole-brain spiking network model of the Drosophila brain. In this paper, we report the outcome of the first phase of the project. We developed the Flysim platform, which 1) identifies the polarity of each neuron arbor, 2) predicts connections between neurons, 3) translates morphology data from the database into physiology parameters for computational modeling, 4) reconstructs a brain-wide network model, which consists of 20,089 neurons and 1,044,020 synapses, and 5) performs computer simulations of the resting state. We compared the reconstructed brain network with a randomized brain network by shuffling the connections of each neuron. We found that the reconstructed brain can be easily stabilized by implementing synaptic short-term depression, while the randomized one exhibited seizure-like firing activity under the same treatment. Furthermore, the reconstructed Drosophila brain was structurally and dynamically more diverse than the randomized one and exhibited both Poisson-like and patterned firing activities. Despite being at its early stage of development, this single-cell level brain model allows us to study some of the fundamental properties of neural networks including network balance, critical behavior, long-term stability, and plasticity.
... Additionally, the nervous system has an interdependency Recently, OpenWorm was started as an open-source initiative to model the complete body of small unsegmented nematodes, Caenorhabditis elegans (C. elegans) 24,25 . As part of the model, OpenWorm provides the 'connectome' for C. elegans which is basically a map of all the neurons of the nervous system and their connections. ...
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The nervous system holds a central position among the major in-body networks. It comprises of cells known as neurons that are responsible to carry messages between different parts of the body and make decisions based on those messages. In this work, further to the extensive theoretical studies, we demonstrate the first controlled information transfer through an in vivo nervous system by modulating digital data from macro-scale devices onto the nervous system of common earthworms and conducting successful transmissions. The results and analysis of our experiments provide a method to model networks of neurons, calculate the channel propagation delay, create their simulation models, indicate optimum parameters such as frequency, amplitude and modulation schemes for such networks, and identify average nerve spikes per input pulse as the nervous information coding scheme. Future studies on neuron characterization and artificial neurons may benefit from the results of our work.
... To conduct experiments with the proposed model we have used an interactive 3D-simulator of nematode with a graphical interface developed and grant to us by Palyanov and Dibert (2009) and Palyanov, Khayrulin, Larson, and Dibert (2012). This 3D-simulator designed for combining the existing and future data on the worm systems (sensory, neurological, muscular, etc.). ...
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In this paper, we propose a learning control system, which models the neural circuits controlling locomotion and chemotaxis of the Caenorhabditis elegans nematode. Using the realistic 3D-simulator of the nematode, we have conducted a series of successful experiments in teaching the proposed model. It is shown that the control system can stably learn an effective way of movement forward in 100 working cycles on the average, and identify an optimal chemotaxis strategy in 1000 cycles on the average. At the same time, we observe a considerable visual likeness between the behavior of the model and the behavior of a real nematode and noted the coincidence of the detected chemotaxis strategy with the strategy used by the biological prototype. The results of experiments have shown that the movement function and associated orientation mechanisms of a nematode can be obtained by way of teaching only in interaction with the environment, and the proposed model of control system is quite effective and can be successfully used to control complex objects with many degrees of freedom.
... Also in 2011, a project was launched of a rather different nature to those discussed above. OpenWorm is an "open science" project to develop a detailed 3D dynamic simulation of the nematode C. elegans (Palyanov et al., 2012). Although the simulation itself is not web-based, the core team are distributed across the world and have regular team meetings using web-based collaboration tools. ...
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A brief survey is presented of the first 18 years of web-based Artificial Life ("WebAL") research and applications, covering the period 1995-2013. The survey is followed by a short discussion of common methodologies employed and current technologies relevant to WebAL research. The paper concludes with a quick look at what the future may hold for work in this exciting area.
... The main active project in the field of simulation of C. elegans is the OpenWorm Project (OpenWorm, 2014). In this case, the physical model is based on the work of Palyanov et al. (Palyanov et al., 2012), where the virtual worm is composed of point masses and springs to model skin and muscles. The neuronal system of this model uses the 23 neurons that handle forward locomotion (Chalfie et al., 1985). ...
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This paper presents the work that has been done in Si elegans project in order to visualize the locomotion and the behaviour of a virtual reproduction of the nematode Caenorhabditis elegans, one of the most studied animal in neuroscience. The project aims to develop the first hardware-based computing framework that will accurately mimic this worm. It will enable complex and realistic behaviour to emerge through interaction with a rich and dynamic simulation of a natural or laboratory environment. In order to visualize the physical behaviours that emerge from the neuronal system that has been constructed in the project, a web environment has been designed where the user will be able to define an assay and to run it in a WebGL-based 3D virtual arena. For that a relation has been defined from the physics based simulation (run on the server side) and the simplified web rendering of it.
... Currently Geppetto includes two major modules: one that simulates the electrical activity of the nervous system and a soft body physics module which will be used to calculate the interaction of the worm with its environment (Figures 1A,B). The integration of these two modules will make it possible to simulate the contraction of muscle tissue in response to the electrical stimuli generated by the nervous system (Palyanov et al., 2012). The Sibernetic physics engine (Palyanov et al., 2013) was developed by the OpenWorm team in parallel to Geppetto to simulate the biomechanics of soft tissues and the environment of the worm ( Figure 1C). ...
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OpenWorm is an international collaboration with the aim of understanding how the behavior of Caenorhabditis elegans (C. elegans) emerges from its underlying physiological processes. The project has developed a modular simulation engine to create computational models of the worm. The modularity of the engine makes it possible to easily modify the model, incorporate new experimental data and test hypotheses. The modeling framework incorporates both biophysical neuronal simulations and a novel fluid-dynamics-based soft-tissue simulation for physical environment-body interactions. The project's open-science approach is aimed at overcoming the difficulties of integrative modeling within a traditional academic environment. In this article the rationale is presented for creating the OpenWorm collaboration, the tools and resources developed thus far are outlined and the unique challenges associated with the project are discussed.
... The vibrant field of Artificial life uses computer simulation to investigate evolution of behavioural and cognitive mechanisms in virtual creatures Sims (1994), potentially leading to advances in both biology (e.g: Palyanov et al. (2012)) and robotics (e.g:Černỳ and Kubalík (2013)). We restrict our review to work related to understanding the evolution of movement strategies that might ultimately be applied to the robotic field. ...
... The central object of interest of the OpenWorm project was to study the fundamental principles underlying the functioning of the nervous system of C. elegans; however, it is not possible to sim ulate only the nervous system as part of this task. We also need a sensing system to provide the flow of incoming signals and the muscular system such that the body had the ability to move according to the com mands of the nervous system, as well as a fragment of the environment in which nematodes will move, lead ing in turn to changes in sensory signals (Palyanov et al., 2012;Cohen and Sanders, 2014). ...
Article
This paper describes the Sibernetic software package. We developed it as part of the international project OpenWorm, which is aimed at the creation of a biologically-based computer model of the nematode Caenorhabditis elegans, including its nervous system. To simulate the body of C. elegans and a fragment of an environment in which it could move under the influence of muscle contractions, we needed an algorithm capable of calculating the fluid dynamics, the dynamics of the elastic body, liquid-impermeable elastic films, and muscle fibers. We selected the PCI SPH algorithm, a modification of the well-known algorithm of smoothed-particle hydrodynamics, which makes it possible to simulate the incompressible fluid. We implemented this algorithm in a parallel form (OpenCL) and significantly supplemented it with a functional aimed at modeling problems in the biomechanics of living systems. This paper describes the capabilities of Sibernetic and is illustrated by the example of our model of the C. elegans body equipped with a muscular system, which is extremely relevant in view of the consistently high level of interest in research on and modeling of this organism among neuroscientists and experts in biomechanics of invertebrates. With this approach, this problem was solved for the first time with such a high degree of realism and detail.
... Creatures. The vibrant field of artificial life uses computer simulation to investigate evolution of behavioral and cognitive mechanisms in virtual creatures [Sims 1994], potentially leading to advances in both biology (e.g., Palyanov et al. [2012]) and robotics (e.g., Černỳ and Kubalík [2013]). We restrict our review to work related to understanding the evolution of movement strategies that might ultimately be applied to the robotic field. ...
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Distributed autonomous systems consisting of large numbers of components with no central control point need to be able to dynamically adapt their controlmechanisms to deal with an unpredictable and changing environment. Existing frameworks for engineering self-adaptive systems fail to account for the need to incorporate self-expression - that is, the capability of a system to dynamically adapt its coordination pattern during runtime. Although the benefits of incorporating self-expression are well known, currently there is no principled means of enabling this during system design.We propose a conceptual framework for principled design of systems that exhibit self-expression, based on inspiration from the natural immune system. The framework is described as a set of design principles and customizable algorithms and then is instantiated in three case studies, including two from robotics and one from artificial chemistry.We show that it enables self-expression in each case, resulting in systems that are able to adapt their choice of coordination pattern during runtime to optimize functional and nonfunctional goals, as well as to discover novel patterns and architectures.
... OpenWorm, which aims to create a whole system simulation of a living organism. Specifically, the goal is to develop a detailed 3D dynamic simulation of the nematode C. elegans [82]. Although the simulation itself is not web-based, the core team are distributed across the world and have regular team meetings using web-based collaboration tools. ...
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We present a survey of the first 21 years of web-based artificial life (WebAL) research and applications, broadly construed to include the many different ways in which artificial life and web technologies might intersect. Our survey covers the period from 1994-when the first WebAL work appeared-up to the present day, together with a brief discussion of relevant precursors. We examine recent projects, from 2010-2015, in greater detail in order to highlight the current state of the art. We follow the survey with a discussion of common themes and methodologies that can be observed in recent work and identify a number of likely directions for future work in this exciting area. THIS PAPER IS FREELY AVAILABLE OPEN ACCESS AT http://www.mitpressjournals.org/doi/abs/10.1162/ARTL_a_00211
... While this is generally accomplished using microbial life, we could also create analogues for chordates and invertebrates. In the case of C. elegans, initiatives such as the OpenWorm project [96,97] might provide the tools for booting up the adult form of this organism. Finally, the differentiation tree approach and assorted tools might help us to understand the structure and global properties of developmental cell lineages that lineage trees, fate maps, and high-resolution microscopy simply cannot do on their own. ...
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Embryonic development proceeds through a series of differentiation events. The mosaic version of this process (binary cell divisions) can be analyzed by comparing early development of Ciona intestinalis and Caenorhabditis elegans . To do this, we reorganize lineage trees into differentiation trees using the graph theory ordering of relative cell volume. Lineage and differentiation trees provide us with means to classify each cell using binary codes. Extracting data characterizing lineage tree position, cell volume, and nucleus position for each cell during early embryogenesis, we conduct several statistical analyses, both within and between taxa. We compare both cell volume distributions and cell volume across developmental time within and between single species and assess differences between lineage tree and differentiation tree orderings. This enhances our understanding of the differentiation events in a model of pure mosaic embryogenesis and its relationship to evolutionary conservation. We also contribute several new techniques for assessing both differences between lineage trees and differentiation trees, and differences between differentiation trees of different species. The results suggest that at the level of differentiation trees, there are broad similarities between distantly related mosaic embryos that might be essential to understanding evolutionary change and phylogeny reconstruction. Differentiation trees may therefore provide a basis for an Evo-Devo Postmodern Synthesis.
... The first target is a description of the worm's locomotion by simulating the 302 neurons and 95 body wall muscle cells (Szigeti et al 2014). Among the currently available modules is a realistic flexible worm body model including the muscular system and a partially implemented ventral neural cord (Palyanov et al 2011, Openworm Browser 2014. It is based on a merged and extended connectome dataset , which is similar to a system of spherical particles of different sizes that was reported to model both the nematode and its environment during movement and feeding behavior (Rönkkö and Wong 2008). ...
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Objective: In light of recent progress in mapping neural function to behavior, we briefly and selectively review past and present endeavors to reveal and reconstruct nervous system function in Caenorhabditis elegans through simulation. Approach: Rather than presenting an all-encompassing review on the mathematical modeling of C. elegans, this contribution collects snapshots of pathfinding key works and emerging technologies that recent single- and multi-center simulation initiatives are building on. We thereby point out a few general limitations and problems that these undertakings are faced with and discuss how these may be addressed and overcome. Main results: Lessons learned from past and current computational approaches to deciphering and reconstructing information flow in the C. elegans nervous system corroborate the need of refining neural response models and linking them to intra- and extra-environmental interactions to better reflect and understand the actual biological, biochemical and biophysical events that lead to behavior. Together with single-center research efforts, the Si elegans and OpenWorm projects aim at providing the required, in some cases complementary tools for different hardware architectures to support advancement into this direction. Significance: Despite its seeming simplicity, the nervous system of the hermaphroditic nematode C. elegans with just 302 neurons gives rise to a rich behavioral repertoire. Besides controlling vital functions (feeding, defecation, reproduction), it encodes different stimuli-induced as well as autonomous locomotion modalities (crawling, swimming and jumping). For this dichotomy between system simplicity and behavioral complexity, C. elegans has challenged neurobiologists and computational scientists alike. Understanding the underlying mechanisms that lead to a context-modulated functionality of individual neurons would not only advance our knowledge on nervous system function and its failure in pathological states, but have directly exploitable benefits for robotics and the engineering of brain-mimetic computational architectures that are orthogonal to current von-Neumann-type machines.
... Raichel called this phenomenon "the brain dark energy," and his discovery changes every previous concept about brain functioning. This energy-burning atitude seems to be the common way of living brains, and signs of constant burning energy have been reported in bees [5] and submillimeter worms [6]. ...
... The McCulloch-Pitts model [36] is a computational model that is widely used to describe dynamics of artificial networks and to reproduce the dynamics of biological neural systems. There are already a few examples of its application to the C. elegans neural network, with relatively simple descriptions [15,[37][38][39], and more detailed ones [40]. In this approach the status of the network is described by a vector rðtÞ, whose elements are binary variables ( rðtÞ fr 1 ðtÞ; r 2 ðtÞ; . . . ...
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Although the synaptic connections of the C. elegans connectome have been precisely mapped with detailed electron microscopy studies of nematode slices, their excitatory or inhibitory character is mostly unknown. This makes the C. elegans neural dynamics unpredictable, and limits our understanding of how specific sub-circuits work. The present study proposes a recurrent neural network model that reproduces known escape behaviours of C. elegans. To do this, our model uses the known information about the connectome, and makes guesses on the excitatory or inhibitory character of the synapses, which are iteratively updated until the model is able to reproduce the known behaviours. Specifically, we apply this model-based approach to study the neuronal sub-circuits involved in the association of aversive stimuli with escape responses. To form an escape response dataset that allows us to adjust our model parameters, we performed a meta-study on the C. elegans stimulus-response behaviours reported in literature, focusing on robust escape reactions triggered by aversive stimuli. Given a wide sample of all possible parameter sets that satisfy the behavioural constraints of the dataset, we find that more than 75% of the synaptic connections reach a univocal optimal assignment of the inhibitory or excitatory character. To validate our model, we show that in the few cases where the excitatory/inhibitory character is already known, our retrieved optimum in synaptic characters matches the results reported in literature. Finally, we assess the accuracy of this approach by applying it on recurrent neural networks that have the same connectivity structure of C. elegans but random inhibitory or excitatory character. These findings confirm the predictive power of the proposed method.
... Previous work has investigated the use of rigid-body simulations of the C. elegans body (electronic supplementary material, table S1). Our own previous investigation took the form of the CyberElegans simulator [30]. Muscles were represented only as a spring between two point masses. ...
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To better understand how a nervous system controls the movements of an organism, we have created a three-dimensional computational biomechanical model of the Caenorhabditis elegans body based on real anatomical structure. The body model is created with a particle system–based simulation engine known as Sibernetic, which implements the smoothed particle–hydrodynamics algorithm. The model includes an elastic body-wall cuticle subject to hydrostatic pressure. This cuticle is then driven by body-wall muscle cells that contract and relax, whose positions and shape are mapped from C. elegans anatomy, and determined from light microscopy and electron micrograph data. We show that by using different muscle activation patterns, this model is capable of producing C. elegans -like behaviours, including crawling and swimming locomotion in environments with different viscosities, while fitting multiple additional known biomechanical properties of the animal. This article is part of a discussion meeting issue ‘Connectome to behaviour: modelling C. elegans at cellular resolution’.
... The simulated C. elegans created by Palyanov et al. provides evidence that such a simplification could be reasonable (Palyanov, Khayrulin, Larson, & Dibert, 2012). Their emulation of the worm included a neuromuscular system in which the musculature was approximated using spring constructs linked to appropriate points on a wireframe body. ...
Preprint
Developing whole-brain emulation (WBE) technology would provide immense benefits across neuroscience, biomedicine, artificial intelligence, and robotics. At this time, constructing a simulated human brain lacks feasibility due to limited experimental data and limited computational resources. However, I suggest that progress towards this goal might be accelerated by working towards an intermediate objective, namely insect brain emulation (IBE). More specifically, this would entail creating biologically realistic simulations of entire insect nervous systems along with more approximate simulations of non-neuronal insect physiology to make “virtual insects.” I argue that this is realistically achievable within the next 25 years.
... Для проведения экспериментов с предложенной моделью управления локомоцией был использован интерактивный 3D-симулятор нематоды с графическим интерфейсом, предназначенный для объединения имеющихся и будущих данных о системах червя (сенсорной, нервной, мышечной и др.) [6][7]. ...
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In this paper we propose a learning model of neural circuit controlling locomotion of C. elegans nematode. Using realistic 3D simulator of the nematode, a number of successful computational experiments on proposed model’s learning were performed. It was shown that the control system was stably trained to produce effective undulatory way of forward movement within 100 cycles at average. At the same time significant visual similarity was observed between the way of movement found by a model and used by real biological nematode. Obtained results indicate that neural circuit controlling locomotion is able to learn complex undulatory form of nematode locomotion based only on experience of system’s inter-action with environment, and that proposed model of control system is quite effective and can be used for driving complex objects possessing multiple degrees of freedom.
... Используя интерактивный 3D-симулятор нематоды [18][19], мы провели серию экспериментов по обучению хемотаксису. Для обеспечения непрерывности обучения каждый раз, когда нематода приближалась к пику концентрации на достаточно близкое расстояние, пик концентрации случайным образом смещался в новую точку. ...
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The C. Elegans nematode is currently the only organism, for which the structure of its nervous system is fully known, and the connectome is obtained in the first approximation. However, we still have not understood the working principles of such a simple nervous system. In this paper, we propose a learning control system, which models the work of neural circuits controlling chemotaxis of the C.Elegans nematode. A model of learning logical neurons has been proposed to create learning neural control circuits. Using the 3D-simulator of the nematode, we have conducted a series of successful experiments in training the proposed model. The control system can stably learn an optimal chemotaxis strategy in 1000 cycles on the average. At the same time, we observe a considerable visual likeness between the behavior of the model and the behavior of a real nematode. The results of experiments have shown that the movement function and associated orientation mechanisms of a nematode can be obtained by way of learning only in interaction with the environment. Practically, the results show that the proposed model can be successfully used to control complex objects.
... Raichel called this phenomenon "the brain dark energy" and his discovery change previous concept about brain functioning. This energyburning attitude seems to be the common way of living brains and signs of it has been suggested in bees [7] and sub millimeter worms [8]. ...
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This paper deals with Brain-Inspired robot controllers, based on a special kind of artificial neural structures that burn “dark” energy to promote the self-motivated initiation of behaviors. We exploit this ambient to train a virtual multi-joint robot, with many moving parts, muscles and sensors distributed through the robot body, interacting with elements that satisfy Newtonian laws. The robot faces a logical-mechanical challenge where a heavy, slippery ball, pressed against a wall has to be pushed up by means of coordinate muscles activation, where energy, timing and balancing conditions add noticeable technical complications. As in living brains our robots contains self-motivating neural agents that consumes energy and function by themselves even without external stimulus. Networks that handle sensory and timing information are combined with agents to construct our controller. We prove that by using appropriate learning algorithms, the self-motivating capacity of agents provides the robot with powerful self-programming aptitudes, capable of solving the ball lifting problem in a quick, efficient way.
... Raichel called this phenomenon "the brain dark energy" and his discovery change previous concept about brain functioning. This energyburning attitude seems to be the common way of living brains and signs of it has been suggested in bees [7] and sub millimeter worms [8]. ...
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Full-text available
This paper deals with Brain-Inspired robot controllers, based on a special kind of artificial neural structures that burn "dark" energy to promote the self-motivated initiation of behaviors. We exploit this ambient to train a virtual multi-joint robot, with many moving parts, muscles and sensors distributed through the robot body, interacting with elements that satisfy Newtonian laws. The robot faces a logical-mechanical challenge where a heavy, slippery ball, pressed against a wall has to be pushed up by means of coordinate muscles activation , where energy, timing and balancing conditions add noticeable technical complications. As in living brains our robots contains self-motivating neu-ral agents that consumes energy and function by themselves even without external stimulus. Networks that handle sensory and timing information are combined with agents to construct our controller. We prove that by using appropriate learning algorithms, the self-motivating capacity of agents provides the robot with powerful self-programming aptitudes, capable of solving the ball lifting problem in a quick, efficient way.
... Early examples of brain robots is the worm brain robot, where scientists were able to create a robotic brain simulating a digitization of the neural system of the worm Caenorhabditis elegans. The OpenWorm Project digitized all 306 neurons of the worm in a goal to simulate its brain [19]. To test the reactivity of such a robot. ...
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Recent advances in the field of neurorobotics are surveyed with emphasis on the areas of brain-computer interface systems, brain-based robots and the human brain project. A simple proof-of-concept experiment, inspired by the mapping of the human brain into a robot, is described and constructed. The capturing of brain electroencephalogram signals was performed through an Emotiv Epoc headset. The resulting brain-computer interface was setup to control a surrogate NAO humanoid robot, while feeding sensory data back to the subject. The various applications of this emerging technology is discussed while emphasising its research and pedagogical value.
... Early examples of brain robots is the worm brain robot, where scientists were able to create a robotic brain simulating a digitization of the neural system of the worm Caenorhabditis elegans. The OpenWorm Project digitized all 306 neurons of the worm in a goal to simulate its brain [19]. To test the reactivity of such a robot. ...
Conference Paper
Full-text available
Recent advances in the field of neurorobotics are surveyed with emphasis on the areas of brain-computer interface systems, brain-based robots and the human brain project. A simple proof-of-concept experiment, inspired by the mapping of the human brain into a robot, is described and constructed. The capturing of brain electroencephalogram signals was performed through an Emotiv Epoc headset. The resulting brain-computer interface was setup to control a surrogate NAO humanoid robot, while feeding sensory data back to the subject. The various applications of this emerging technology is discussed while emphasising its research and pedagogical value.
... Fortunately, these processes may necessitate less detailed modeling to achieve biological realism. The simulated Caenorhabditis elegans created by Palyanov et al. provides evidence that such a simplification could be reasonable (Palyanov et al. 2012). Their emulation of the worm included a neuromuscular system in which the musculature was approximated using spring constructs linked to appropriate points on a wireframe body. ...
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Developing whole-brain emulation (WBE) technology would provide immense benefits across neuroscience, biomedicine, artificial intelligence, and robotics. At this time, constructing a simulated human brain lacks feasibility due to limited experimental data and limited computational resources. However, I suggest that progress toward this goal might be accelerated by working toward an intermediate objective, namely insect brain emulation (IBE). More specifically, this would entail creating biologically realistic simulations of entire insect nervous systems along with more approximate simulations of non-neuronal insect physiology to make “virtual insects.” I argue that this could be realistically achievable within the next 20 years. I propose that developing emulations of insect brains will galvanize the global community of scientists, businesspeople, and policymakers toward pursuing the loftier goal of emulating the human brain. By demonstrating that WBE is possible via IBE, simulating mammalian brains and eventually the human brain may no longer be viewed as too radically ambitious to deserve substantial funding and resources. Furthermore, IBE will facilitate dramatic advances in cognitive neuroscience, artificial intelligence, and robotics through studies performed using virtual insects.
... Это позволило наблюдать ее у модели: мышечные сокращения привели к реалистичному поступательному движению вперед. Подробное изложение научных принципов и технических деталей программной реализации данной модели описано в работе [10]. Описанный симулятор разработан с использованием C++, STL и OpenGL и способен работать на обычном персональном компьютере. ...
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Nowadays a significant amount of neurobiological studies, including human neurobiology, is being performed using modern methods, technologies and equipment, but scientists are still unison in opinions that we are still far from understanding of fundamental mechanisms of brain and consciousness functioning. Many researchers also suppose that we are, moreover, still far from understanding of a single neuron. Until this challenging puzzle remains unsolved we can only expect the real amount of knowledge and technology level intercepting the humanity from the success. In this paper the analysis of actual situation in computational neuroscience will be peformed, particularly the brain reverse-engineering problem – study of mechanisms underlying principles of living organisms’ nervous systems functioning and reproduction of them in the form of computer simulations. Also we’ll try to identify the most principal problems and discuss the ways of solving them, as well as further perspectives. A part of the paper is devoted to authors’ work on development of computer simulation of C. elegans nematode including its neuromuscular model. (the paper is written in Russian)
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In this work we will work on analogue signal processing in the neural circuit of C. elegans which is able to detect the analogue signals from the environment and produce locomotive behaviours which are in accordance with experiments. The signals in C. elegans are processed in a purely analogue procedure, since no action potential has been recorded in its neural activity. We aim to show how signal processing can be executed in analogue domain in a living creature. In order to do that we will model two different behaviours of C. elegans which are generated in the same network of neurons, klinotaxis behaviour and isothermal tracking. We will implement a Genetic Algorithm to find appropriate sets of parameters of the model. Our contribution is to show how relatively straight forward differential equations can lead to relatively complex and different behaviours.
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Standard mechanical components, such as adapters or mounts, are ubiquitous in research laboratories, C. elegans labs included. Recently, in-house prototyping and fabricating both standard and custom mechanical parts has become simple and cost effective. Here we describe the basic steps, equipment, and considerations required for rapid prototyping of a handful of simple yet useful designs. These examples were chosen for their simplicity, as well as for demonstrating specific practicalities. They are thus appropriate as training exercises.
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We pursue the hypothesis that neuronal placement in animals minimizes wiring costs for given functional constraints, as specified by synaptic connectivity. Using a newly compiled version of the Caenorhabditis elegans wiring diagram, we solve for the optimal layout of 279 nonpharyngeal neurons. In the optimal layout, most neurons are located close to their actual positions, suggesting that wiring minimization is an important factor. Yet some neurons exhibit strong deviations from “optimal” position. We propose that biological factors relating to axonal guidance and command neuron functions contribute to these deviations. We capture these factors by proposing a modified wiring cost function. • Caenorhabditis elegans • optimal placement
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I propose to consider the question, “Can machines think?”♣ This should begin with definitions of the meaning of the terms “machine” and “think”. The definitions might be framed so as to reflect so far as possible the normal use of the words, but this attitude is dangerous. If the meaning of the words “machine” and “think” are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning and the answer to the question, “Can machines think?” is to be sought in a statistical survey such as a Gallup poll.
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1. Introduction Part I. Single Neuron Models: 2. Detailed neuron models 3. Two-dimensional neuron models 4. Formal spiking neuron models 5. Noise in spiking neuron models Part II. Population Models: 6. Population equations 7. Signal transmission and neuronal coding 8. Oscillations and synchrony 9. Spatially structured networks Part III. Models of Synaptic Plasticity: 10. Hebbian models 11. Learning equations 12. Plasticity and coding Bibliography Index.
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The eye of Aplysia californica was studied by electrophysiological and histological methods. It has a central spheroidal lens which is surrounded by a retina composed of several thousand receptor cells which are replete with clear vesicles, pigmented support cells, neurons which contain secretory granules, and glial cells. The thin optic nerve that connects the eye to the cerebral ganglion gives a simple "on" response of synchronized action potentials. Tonic activity occurs in the optic nerve in the dark and is dependent on previous dark adaptation. Micropipette recordings indicate that the ERG is positive (relative to a bathelectrode) on the outer surface of the eye and negative in the region of the distal segments of the receptors. Intracellular recordings show that receptor cells have resting potentials of 40-50 mv and respond to illumination with graded potentials of up to 55 mv. Dark-adapted receptors exhibit discrete bumps on the graded response to brief light flashes. Other elements in the retina that do not give large graded responses fall into two classes. One class responds to illumination with action potentials that are in synchrony with the extracellularly recorded compound optic nerve potentials. The other class is tonically active and is depolarized or hyperpolarized and inhibited upon illumination. It is apparent that complex excitatory and lateral inhibitory interactions occur among the elements of the retina.
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Little is known about the physiology of neurons in Caenorhabditis elegans. Using new techniques for in situ patch-clamp recording in C. elegans, we analyzed the electrical properties of an identified sensory neuron (ASER) across four developmental stages and 42 unidentified neurons at one stage. We find that ASER is nearly isopotential and fails to generate classical Na+ action potentials. Rather, ASER displays a high sensitivity to input currents coupled to a depolarization-dependent reduction in sensitivity that may endow ASER with a wide dynamic range. Voltage clamp revealed depolarization-activated K+ and Ca2+ currents that contribute to high sensitivity near the zero-current potential. The depolarization-dependent reduction in sensitivity can be attributed to activation of K+ current at voltages where it dominates the net membrane current. The voltage dependence of membrane current was similar in all neurons examined, suggesting that C. elegans neurons share a common mechanism of sensitivity and dynamic range.
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Neurotransmitter receptors, neurotransmitter synthesis and release pathways, and heterotrimeric GTP–binding protein (G protein)–coupled second messenger pathways are highly conserved between Caenorhabditis elegans and mammals, but gap junctions and chemosensory receptors have independent origins in vertebrates and nematodes. Most ion channels are similar to vertebrate channels but there are no predicted voltage-activated sodium channels. The C. elegans genome encodes at least 80 potassium channels, 90 neurotransmitter-gated ion channels, 50 peptide receptors, and up to 1000 orphan receptors that may be chemoreceptors. For many gene families, C. elegans has both conventional members and divergent outliers with weak homology to known genes; these outliers may provide insights into previously unknown functions of conserved protein families.
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The nematode worm Caenorhabditis elegans can learn and remember the stimuli it encounters, the environment it is in, and its own physiological state. Analyses of mutations in C. elegans that affect different aspects of experience are beginning to address the nature of learning.
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: We present a learning technique that automatically synthesizes realistic locomotion for the animation of physics-based models of animals. The method is especially suitable for animals with highly flexible, many-degree-of-freedom bodies and a considerable number of internal muscle actuators, such as snakes and fish. The multilevel learning process first performs repeated locomotion trials in search of actuator control functions that produce efficient locomotion, presuming virtually nothing about the form of these functions. Applying a short-time Fourier analysis, the learning process then abstracts control functions that produce effective locomotion into a compact representation which makes explicit the natural quasi-periodicities and coordination of the muscle actions. The artificial animals can finally put into practice the compact, efficient controllers that they have learned. Their locomotion learning abilities enable them to accomplish higher-level tasks specified by the animator while guided by sensory perception of their virtual world; e.g., locomotion to a visible target. We demonstrate physics-based animation of learned locomotion in dynamic models of land snakes, fishes, and even marine mammals that have trained themselves to perform "SeaWorld" stunts. 1
Forward locomotion of the nematode C. elegans is achieved [31] E. Singer. A Wiring Diagram of the Brain://www.technologyreview.com/biotech/ 19731/?a=f
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