Neural Interactome: Interactive Simulation of a Neuronal System

Abstract

Both connectivity and biophysical processes determine the functionality of neuronal networks. We, therefore, develop a real-time framework, called Neural Interactome 1 , 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), as typically done in experiments. 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). We implement the framework using a model of the nervous system of Caenorhabditis elegans (C. elegans) nematode, a model organism for which full connectome and neural dynamics have been resolved. 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. The development of the Neural Interactome was guided by generic concepts to be applicable to neuronal networks with different neural connectivity and dynamics. Author SummaryEmerging neuroimaging techniques and novel optical interfaces which record and control neural dynamics enable detailed computational connectivity and dynamics models for neurobiological systems. An open question stemming from these advances is how to validate, simulate and apply these models to predict network functionality. Supervised empirical exploration to identify functional stimulations is an elaborate process, and direct computational approach of sequential stimulation is also formidable since produces large amounts of data without clarity on how it can 1 Web interface available at http://neuralcode.amath.washington.edu/neuralinteractome 2 be used to steer toward meaningful functionalities. We therefore develop a platform to inspect network dynamics in real time while preserving structural connectivity properties, displaying the dynamics on a graph, with possibilities to identify functional sub circuits and review the simulated dynamics. The platform allows for real time interactions with the network such as variation of stimuli and performing connectivity changes as neural ablation. We apply the platform to Caenorhabditis elegans nematode nervous system model. We revisit experimentally known scenarios of stimulations and show how our platform helps to detect associated neural dynamic patterns within seconds through few interactions. In addition, we show how the platform could provide novel hypotheses for scenarios that were n...

Functional connectomics from neural dynamics: probabilistic graphical models for neuronal network ofCaenorhabditis elegans

“…Neural Interactome platform (see [21,27] for further details). We run the simulations for 15 s with a time step of 0.01 s and record the dynamics of all neurons in snapshot matrices with each snapshot matrix S of dimensions n  T ¼ 279  1501.…”

“…Data accessibility. To simulate C. elegans neural dynamics we use the Neural Interactome available at [21,27] and store the dynamics as Python matrices. Python software to construct the PGM for C. elegans is available at [40].…”

Computing temporal sequences associated with dynamic patterns on the C. elegans connectome

“…Using the available experimental data [67,19,33] we constructed a structural geometric connectivity network model (Methods 4.1) in order to analyze the dynamics on the C. elegans connectome using our dynamic signaling framework. This is in direct contrast to other attempts to model C. elegans using biophysical models [61,57,36,38]. Biophysical models are computationally expensive [27] and require parameter estimates and assumptions that are not typically readily measurable or known in most cases.…”

“…Although the organism's connectome has been known for decades, how neurons functionally interact in the context of the entire network and how the resultant dynamics regulates participating neurons is not fully understood [6,5,39]. Recent research efforts have moved past analyzing the network's structure [64,62], towards analyzing its dynamics in various ways [35,11,55,48,61,70,36]. Here we asked how does the concurrent activity of independent neuronal elements ultimately give rise to a rich behavioral repertoire of the C. elegans connectome?…”

Black-box model reduction of the C. Elegans nervous system

“…A second-layer containing the biophysical processes of neural responses and interactions needs to be added to the static first-layer (the connectome) to make it into a dynamical model (the interactome) [22]. The connectome in [22] is an interactive network, but the underlying neurons are compact models with no geometrical distribution whereas the neural responses are modeled with graded potentials.…”