Search

An open challenge on the road to unraveling the brain's multilevel organization is establishing techniques to research connectivity and dynamics at different scales in time and space, as well as the links between them. This work focuses on the design of a framework that facilitates the generation of multiscale connectivity in large neural networks using a symbolic visual language capable of representing the model at different structural levels-ConGen. This symbolic language allows researchers to create and visually analyze the generated networks independently of the simulator to be used, since the visual model is translated into a simulator-independent language. The simplicity of the front end visual representation, together with the simulator independence provided by the back end translation, combine into a framework to enhance collaboration among scientists with expertise at different scales of abstraction and from different fields. On the basis of two use cases, we introduce the features and possibilities of our proposed visual language and associated workflow. We demonstrate that ConGen enables the creation, editing, and visualization of multiscale biological neural networks and provides a whole workflow to produce simulation scripts from the visual representation of the model., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Herbers, Calvo, Diaz-Pier, Robles, Mata, Toharia, Pastor, Peyser, Morrison and Klijn.)

This work presents the first simulation of a large-scale, bio-physically constrained cerebellum model performed on neuromorphic hardware. A model containing 97,000 neurons and 4.2 million synapses is simulated on the SpiNNaker neuromorphic system. Results are validated against a baseline simulation of the same model executed with NEST, a popular spiking neural network simulator using generic computational resources and double precision floating point arithmetic. Individual cell and network-level spiking activity is validated in terms of average spike rates, relative lead or lag of spike times, and membrane potential dynamics of individual neurons, and SpiNNaker is shown to produce results in agreement with NEST. Once validated, the model is used to investigate how to accelerate the simulation speed of the network on the SpiNNaker system, with the future goal of creating a real-time neuromorphic cerebellum. Through detailed communication profiling, peak network activity is identified as one of the main challenges for simulation speed-up. Propagation of spiking activity through the network is measured, and will inform the future development of accelerated execution strategies for cerebellum models on neuromorphic hardware. The large ratio of granule cells to other cell types in the model results in high levels of activity converging onto few cells, with those cells having relatively larger time costs associated with the processing of communication. Organizing cells on SpiNNaker in accordance with their spatial position is shown to reduce the peak communication load by 41%. It is hoped that these insights, together with alternative parallelization strategies, will pave the way for real-time execution of large-scale, bio-physically constrained cerebellum models on SpiNNaker. This in turn will enable exploration of cerebellum-inspired controllers for neurorobotic applications, and execution of extended duration simulations over timescales that would currently be prohibitive using conventional computational platforms., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Bogdan, Marcinnò, Casellato, Casali, Rowley, Hopkins, Leporati, D'Angelo and Rhodes.)

ReaxFF MD (Reactive Force Field Molecular Dynamics) is a promising method for investigating complex chemical reactions in relatively larger scale molecular systems. The existing analysis tools for ReaxFF MD lack the capability of capturing chemical reactions directly by analyzing the simulation trajectory, which is critical in exploring reaction mechanisms. This paper presents the algorithms, implementation strategies, features, and applications of VARxMD, a tool for Visualization and Analysis of Reactive Molecular Dynamics. VARxMD is dedicated to detailed chemical reaction analysis and visualization from the trajectories obtained in ReaxFF MD simulations. The interrelationships among the atoms, bonds, fragments, species and reactions are analyzed directly from the three-dimensional (3D) coordinates and bond orders of the atoms in a trajectory, which are accomplished by determination of atomic connectivity for recognizing connected molecular fragments, perception of bond types in the connected fragments for molecules or radicals, indexing of all these molecules or radicals (chemical species) based on their 3D coordinates and recognition of bond breaking or forming in the chemical species for reactions. Consequently, detailed chemical reactions taking place between two sampled frames can be generated automatically. VARxMD is the first tool specialized for reaction analysis and visualization in ReaxFF MD simulations. Applications of VARxMD in ReaxFF MD simulations of coal and HDPE (high-density polyethylene) pyrolysis show that VARxMD provides the capabilities in exploring the reaction mechanism in large systems with complex chemical reactions involved that are difficult to access manually., (Copyright © 2014 Elsevier Inc. All rights reserved.)