Neural Interactome: Interactive Simulation of a Neuronal System

Kim, Jimin; Leahy, William; Shlizerman, Eli

doi:10.3389/fncom.2019.00008

Cited by 18 publications

(17 citation statements)

References 47 publications

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“…However, developing models that can predict network function based on simulations is still an area that requires further study. Additionally, dynamics of multitudes of neurons during a certain behavior, allow for new approaches in modeling incorporating both the structural connectome data and layering it with the neurophysiological responses and interactions [77]. The ‘Dynome’ model depicts the dynamical systems overlaying the structural connectivity [77].…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

“…A recent study has developed the “dynome” of the worm nervous system [77]. This predictive system relies on the simulation of neural dynamics, or temporal experiments on multiple neurons, to allow application of stimuli to neurons to examine network properties.…”

Section: Technologies Employed To Unravel the Functional Connectomementioning

confidence: 99%

“…Changing these parameters allows for calculation of neural response patterns associated with different stimuli. The strength of this approach lies in the ability of removing neurons from a network and studying the dynamics of the circuit [77,78]. This unique computational approach is a first step in making any nervous system’s architecture being able to predict a behavior based on network properties.…”

Section: Technologies Employed To Unravel the Functional Connectomementioning

confidence: 99%

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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: Discussion and Future Directionsmentioning

confidence: 99%

Section: Technologies Employed To Unravel the Functional Connectomementioning

confidence: 99%

Section: Technologies Employed To Unravel the Functional Connectomementioning

confidence: 99%

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Novel Technological Advances in Functional Connectomics in C. elegans

DiLoreto

Chute

Bryce

et al. 2019

JDB

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“…We utilize the recently established computational neuronal network dynamical model of C. elegans to simulate responses of the nervous system to stimuli (22,23,26). This model is based on molecular properties of neurons in C. elegans network, as determined in experiments by modeling neural responses of the full somatic nervous system as: (i) graded potentials (ii) neural gap junctions connectivity (iii) neural dynamic synaptic connectivity including glutamergic, cholinergic and GABAergic receptors.…”

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

Self Cite

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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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“…Here, we employ a lognormal recurrent neural network, load patterns, and test the resulting neural representation for information content by an output classifier. This setup is reminiscent of LSN [8] and ESN [5]; but it is also a standard setup for a computational brain model [6,11]. Mutual information (MI) analysis shows that both multi-pattern and single-pattern response neurons spontaneously form in the representations.…”

Section: Introductionmentioning

confidence: 99%

Localist neural plasticity identified by mutual information

Scheler¹,

Schumann²

2019

Preprint

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The issue of memory is difficult for standard neural network models. Ubiquitous synaptic plasticity introduces the problem of interference, which limits pattern recall and introduces conflation errors. We present a lognormal recurrent neural network, load patterns into it (MNIST), and test the resulting neural representation for information content by an output classifier. We identify neurons, which 'compress' the pattern information into their own adjacency network, and by stimulating these achieve recall. Learning is limited to intrinsic plasticity and output synapses of these pattern neurons (localist plasticity), which prevents interference.Our first experiments show that this form of storage and recall is possible, with the caveat of a 'lossy' recall similar to human memory. Comparing our results with a standard Gaussian network model, we notice that this effect breaks down for the Gaussian model.

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Neural Interactome: Interactive Simulation of a Neuronal System

Cited by 18 publications

References 47 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

Localist neural plasticity identified by mutual information

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