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      Brian2Loihi: An emulator for the neuromorphic chip Loihi using the spiking neural network simulator Brian

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          Abstract

          Developing intelligent neuromorphic solutions remains a challenging endeavor. It requires a solid conceptual understanding of the hardware's fundamental building blocks. Beyond this, accessible and user-friendly prototyping is crucial to speed up the design pipeline. We developed an open source Loihi emulator based on the neural network simulator Brian that can easily be incorporated into existing simulation workflows. We demonstrate errorless Loihi emulation in software for a single neuron and for a recurrently connected spiking neural network. On-chip learning is also reviewed and implemented, with reasonable discrepancy due to stochastic rounding. This work provides a coherent presentation of Loihi's computational unit and introduces a new, easy-to-use Loihi prototyping package with the aim to help streamline conceptualization and deployment of new algorithms.

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          Most cited references38

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          Loihi: A Neuromorphic Manycore Processor with On-Chip Learning

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            Chaos in neuronal networks with balanced excitatory and inhibitory activity.

            Neurons in the cortex of behaving animals show temporally irregular spiking patterns. The origin of this irregularity and its implications for neural processing are unknown. The hypothesis that the temporal variability in the firing of a neuron results from an approximate balance between its excitatory and inhibitory inputs was investigated theoretically. Such a balance emerges naturally in large networks of excitatory and inhibitory neuronal populations that are sparsely connected by relatively strong synapses. The resulting state is characterized by strongly chaotic dynamics, even when the external inputs to the network are constant in time. Such a network exhibits a linear response, despite the highly nonlinear dynamics of single neurons, and reacts to changing external stimuli on time scales much smaller than the integration time constant of a single neuron.
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              Chaos in Random Neural Networks

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                Author and article information

                Contributors
                Journal
                Front Neuroinform
                Front Neuroinform
                Front. Neuroinform.
                Frontiers in Neuroinformatics
                Frontiers Media S.A.
                1662-5196
                09 November 2022
                2022
                : 16
                : 1015624
                Affiliations
                [1] 1Department of Computational Neuroscience, University of Göttingen , Göttingen, Germany
                [2] 2Bernstein Center for Computational Neuroscience, University of Göttingen , Göttingen, Germany
                Author notes

                Edited by: Andrew P. Davison, UMR9197 Institut des Neurosciences Paris Saclay (Neuro-PSI), France

                Reviewed by: Yao-Feng Chang, Intel, United States; Terrence C. Stewart, National Research Council Canada (NRC), Canada

                *Correspondence: Carlo Michaelis carlo.michaelis@ 123456phys.uni-goettingen.de

                †These authors have contributed equally to this work

                Article
                10.3389/fninf.2022.1015624
                9682266
                7c7af213-845f-45fe-948e-22c688777620
                Copyright © 2022 Michaelis, Lehr, Oed and Tetzlaff.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 09 August 2022
                : 12 October 2022
                Page count
                Figures: 3, Tables: 0, Equations: 26, References: 38, Pages: 13, Words: 8215
                Categories
                Neuroscience
                Technology and Code

                Neurosciences
                neuromorphic computing,loihi,brian2,emulator,spiking neural network,open source
                Neurosciences
                neuromorphic computing, loihi, brian2, emulator, spiking neural network, open source

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