Functional connectomics from neural dynamics: probabilistic graphical models for neuronal network of<i>Caenorhabditis elegans</i>

Liu, Hexuan; Kim, Jimin; Shlizerman, Eli

doi:10.1098/rstb.2017.0377

Cited by 20 publications

(17 citation statements)

References 34 publications

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“…Modeling the dynamics of the neural network is possible by combining the known structural connectome data of C. elegans with a physiologically model of a neuron [74]. Models such as probabilistic graphical models (PGMs), use known circuits of repetitive behavior, (Figure 2D), to predict responses to novel situations [75,76].…”

Section: Technologies Employed To Unravel the Functional Connectomementioning

confidence: 99%

Novel Technological Advances in Functional Connectomics in C. elegans

DiLoreto

Chute

Bryce

et al. 2019

JDB

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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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Section: Technologies Employed To Unravel the Functional Connectomementioning

confidence: 99%

Novel Technological Advances in Functional Connectomics in C. elegans

DiLoreto

Chute

Bryce

et al. 2019

JDB

View full text Add to dashboard Cite

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“…In addition, the model allows for computational clamping of neurons by external current injection. We previously observed that injection of constant current into sensory neurons, e.g., the posterior PLM mechanosensory neurons, evoke oscillatory neural responses in some motor neurons producing low dimensional attractor-like dynamics and transient dynamics with longer timescales than the intrinsic neural dynamics (24,31).…”

Section: Resultsmentioning

confidence: 99%

Whole integration of neural connectomics, dynamics and bio-mechanics for identification of behavioral sensorimotor pathways in Caenorhabditis elegans

Kim

Santos

Alkema

et al. 2019

Preprint

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The ability to fully discern how the brain orchestrates behavior requires the development of successful computational approaches to integrate and inform in-vivo investigations of the nervous system. To effectively assist with such investigations, computational approaches must be generic, scalable and unbiased. We propose such a comprehensive framework to investigate the interaction between the nervous system and the body for the nematode Caenorhabditis elegans (C. elegans). Specifically, we introduce a model that computationally emulates the activity of the complete somatic nervous system and its response to stimuli. The model builds upon the full anatomical wiring diagram, the connectome, and integrates it with additional layers including intra-cellular and extra-cellular bio-physically relevant neural dynamics, layers translating neural activity to muscle forces and muscle impulses to body postures. In addition, it implements inverse integration which modulates neural dynamics according to external forces on the body. We validate the model by in-silico injection of currents into sensory-and interneurons known to play a role in locomotion behaviors (e.g. posterior/anterior touch) and by applying external forces on the body. We are able to generate characteristic baseline locomotion behaviors (forward and backward movements). Inclusion of proprioceptive feedback, implemented through inverse integration, shows that feedback can entrain and sustain movements initiated by neural or mechanical triggers. We further apply neural stimuli, experimentally known to modulate locomotion, and show that our model supports natural behavioral responses such as turns, reversals and avoidance. The proposed model can be utilized to infer neural circuits involved in sensorimotor behavior. For this purpose, we develop large-scale computational ablation approaches such as (i) ablation survey and (ii) conditional ablation. Our results show how an ablation survey can identify neurons required for a ventral turning behavior. We also show how conditional ablation can identify alternative novel neural pathways, e.g. propose neurons which facilitate steering behavior towards olfactory attractants.The outcomes of our study show that the framework can be utilized to identify neural circuits, which control, mediate and generate natural behavior.

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“…Being closer to the realistic neuronal system, dynome studies have more potential to reveal neural pathways and functionalities of the network (Bargmann and Marder, 2013 ; Sporns and Bullmore, 2014 ; Liu et al, 2018 ). However, they also introduce challenges in finding appropriate methods for efficient studies of network capabilities (Mucha et al, 2010 ).…”

Section: Introductionmentioning

confidence: 99%

Neural Interactome: Interactive Simulation of a Neuronal System

Kim

Leahy

Shlizerman

2019

Front. Comput. Neurosci.

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Connectivity and biophysical processes determine the functionality of neuronal networks. We, therefore, developed a real-time framework, called Neural Interactome1,2, 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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Functional connectomics from neural dynamics: probabilistic graphical models for neuronal network ofCaenorhabditis elegans

Cited by 20 publications

References 34 publications

Novel Technological Advances in Functional Connectomics in C. elegans

Novel Technological Advances in Functional Connectomics in C. elegans

Whole integration of neural connectomics, dynamics and bio-mechanics for identification of behavioral sensorimotor pathways in Caenorhabditis elegans

Neural Interactome: Interactive Simulation of a Neuronal System

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