Cargando…

A Spiking Neural Network Model of the Lateral Geniculate Nucleus on the SpiNNaker Machine

We present a spiking neural network model of the thalamic Lateral Geniculate Nucleus (LGN) developed on SpiNNaker, which is a state-of-the-art digital neuromorphic hardware built with very-low-power ARM processors. The parallel, event-based data processing in SpiNNaker makes it viable for building m...

Descripción completa

Detalles Bibliográficos
Autores principales: Sen-Bhattacharya, Basabdatta, Serrano-Gotarredona, Teresa, Balassa, Lorinc, Bhattacharya, Akash, Stokes, Alan B., Rowley, Andrew, Sugiarto, Indar, Furber, Steve
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5552764/
https://www.ncbi.nlm.nih.gov/pubmed/28848380
http://dx.doi.org/10.3389/fnins.2017.00454
_version_ 1783256512536248320
author Sen-Bhattacharya, Basabdatta
Serrano-Gotarredona, Teresa
Balassa, Lorinc
Bhattacharya, Akash
Stokes, Alan B.
Rowley, Andrew
Sugiarto, Indar
Furber, Steve
author_facet Sen-Bhattacharya, Basabdatta
Serrano-Gotarredona, Teresa
Balassa, Lorinc
Bhattacharya, Akash
Stokes, Alan B.
Rowley, Andrew
Sugiarto, Indar
Furber, Steve
author_sort Sen-Bhattacharya, Basabdatta
collection PubMed
description We present a spiking neural network model of the thalamic Lateral Geniculate Nucleus (LGN) developed on SpiNNaker, which is a state-of-the-art digital neuromorphic hardware built with very-low-power ARM processors. The parallel, event-based data processing in SpiNNaker makes it viable for building massively parallel neuro-computational frameworks. The LGN model has 140 neurons representing a “basic building block” for larger modular architectures. The motivation of this work is to simulate biologically plausible LGN dynamics on SpiNNaker. Synaptic layout of the model is consistent with biology. The model response is validated with existing literature reporting entrainment in steady state visually evoked potentials (SSVEP)—brain oscillations corresponding to periodic visual stimuli recorded via electroencephalography (EEG). Periodic stimulus to the model is provided by: a synthetic spike-train with inter-spike-intervals in the range 10–50 Hz at a resolution of 1 Hz; and spike-train output from a state-of-the-art electronic retina subjected to a light emitting diode flashing at 10, 20, and 40 Hz, simulating real-world visual stimulus to the model. The resolution of simulation is 0.1 ms to ensure solution accuracy for the underlying differential equations defining Izhikevichs neuron model. Under this constraint, 1 s of model simulation time is executed in 10 s real time on SpiNNaker; this is because simulations on SpiNNaker work in real time for time-steps dt ⩾ 1 ms. The model output shows entrainment with both sets of input and contains harmonic components of the fundamental frequency. However, suppressing the feed-forward inhibition in the circuit produces subharmonics within the gamma band (>30 Hz) implying a reduced information transmission fidelity. These model predictions agree with recent lumped-parameter computational model-based predictions, using conventional computers. Scalability of the framework is demonstrated by a multi-node architecture consisting of three “nodes,” where each node is the “basic building block” LGN model. This 420 neuron model is tested with synthetic periodic stimulus at 10 Hz to all the nodes. The model output is the average of the outputs from all nodes, and conforms to the above-mentioned predictions of each node. Power consumption for model simulation on SpiNNaker is ≪1 W.
format Online
Article
Text
id pubmed-5552764
institution National Center for Biotechnology Information
language English
publishDate 2017
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-55527642017-08-28 A Spiking Neural Network Model of the Lateral Geniculate Nucleus on the SpiNNaker Machine Sen-Bhattacharya, Basabdatta Serrano-Gotarredona, Teresa Balassa, Lorinc Bhattacharya, Akash Stokes, Alan B. Rowley, Andrew Sugiarto, Indar Furber, Steve Front Neurosci Neuroscience We present a spiking neural network model of the thalamic Lateral Geniculate Nucleus (LGN) developed on SpiNNaker, which is a state-of-the-art digital neuromorphic hardware built with very-low-power ARM processors. The parallel, event-based data processing in SpiNNaker makes it viable for building massively parallel neuro-computational frameworks. The LGN model has 140 neurons representing a “basic building block” for larger modular architectures. The motivation of this work is to simulate biologically plausible LGN dynamics on SpiNNaker. Synaptic layout of the model is consistent with biology. The model response is validated with existing literature reporting entrainment in steady state visually evoked potentials (SSVEP)—brain oscillations corresponding to periodic visual stimuli recorded via electroencephalography (EEG). Periodic stimulus to the model is provided by: a synthetic spike-train with inter-spike-intervals in the range 10–50 Hz at a resolution of 1 Hz; and spike-train output from a state-of-the-art electronic retina subjected to a light emitting diode flashing at 10, 20, and 40 Hz, simulating real-world visual stimulus to the model. The resolution of simulation is 0.1 ms to ensure solution accuracy for the underlying differential equations defining Izhikevichs neuron model. Under this constraint, 1 s of model simulation time is executed in 10 s real time on SpiNNaker; this is because simulations on SpiNNaker work in real time for time-steps dt ⩾ 1 ms. The model output shows entrainment with both sets of input and contains harmonic components of the fundamental frequency. However, suppressing the feed-forward inhibition in the circuit produces subharmonics within the gamma band (>30 Hz) implying a reduced information transmission fidelity. These model predictions agree with recent lumped-parameter computational model-based predictions, using conventional computers. Scalability of the framework is demonstrated by a multi-node architecture consisting of three “nodes,” where each node is the “basic building block” LGN model. This 420 neuron model is tested with synthetic periodic stimulus at 10 Hz to all the nodes. The model output is the average of the outputs from all nodes, and conforms to the above-mentioned predictions of each node. Power consumption for model simulation on SpiNNaker is ≪1 W. Frontiers Media S.A. 2017-08-09 /pmc/articles/PMC5552764/ /pubmed/28848380 http://dx.doi.org/10.3389/fnins.2017.00454 Text en Copyright © 2017 Sen-Bhattacharya, Serrano-Gotarredona, Balassa, Bhattacharya, Stokes, Rowley, Sugiarto and Furber. http://creativecommons.org/licenses/by/4.0/ 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) or licensor 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.
spellingShingle Neuroscience
Sen-Bhattacharya, Basabdatta
Serrano-Gotarredona, Teresa
Balassa, Lorinc
Bhattacharya, Akash
Stokes, Alan B.
Rowley, Andrew
Sugiarto, Indar
Furber, Steve
A Spiking Neural Network Model of the Lateral Geniculate Nucleus on the SpiNNaker Machine
title A Spiking Neural Network Model of the Lateral Geniculate Nucleus on the SpiNNaker Machine
title_full A Spiking Neural Network Model of the Lateral Geniculate Nucleus on the SpiNNaker Machine
title_fullStr A Spiking Neural Network Model of the Lateral Geniculate Nucleus on the SpiNNaker Machine
title_full_unstemmed A Spiking Neural Network Model of the Lateral Geniculate Nucleus on the SpiNNaker Machine
title_short A Spiking Neural Network Model of the Lateral Geniculate Nucleus on the SpiNNaker Machine
title_sort spiking neural network model of the lateral geniculate nucleus on the spinnaker machine
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5552764/
https://www.ncbi.nlm.nih.gov/pubmed/28848380
http://dx.doi.org/10.3389/fnins.2017.00454
work_keys_str_mv AT senbhattacharyabasabdatta aspikingneuralnetworkmodelofthelateralgeniculatenucleusonthespinnakermachine
AT serranogotarredonateresa aspikingneuralnetworkmodelofthelateralgeniculatenucleusonthespinnakermachine
AT balassalorinc aspikingneuralnetworkmodelofthelateralgeniculatenucleusonthespinnakermachine
AT bhattacharyaakash aspikingneuralnetworkmodelofthelateralgeniculatenucleusonthespinnakermachine
AT stokesalanb aspikingneuralnetworkmodelofthelateralgeniculatenucleusonthespinnakermachine
AT rowleyandrew aspikingneuralnetworkmodelofthelateralgeniculatenucleusonthespinnakermachine
AT sugiartoindar aspikingneuralnetworkmodelofthelateralgeniculatenucleusonthespinnakermachine
AT furbersteve aspikingneuralnetworkmodelofthelateralgeniculatenucleusonthespinnakermachine
AT senbhattacharyabasabdatta spikingneuralnetworkmodelofthelateralgeniculatenucleusonthespinnakermachine
AT serranogotarredonateresa spikingneuralnetworkmodelofthelateralgeniculatenucleusonthespinnakermachine
AT balassalorinc spikingneuralnetworkmodelofthelateralgeniculatenucleusonthespinnakermachine
AT bhattacharyaakash spikingneuralnetworkmodelofthelateralgeniculatenucleusonthespinnakermachine
AT stokesalanb spikingneuralnetworkmodelofthelateralgeniculatenucleusonthespinnakermachine
AT rowleyandrew spikingneuralnetworkmodelofthelateralgeniculatenucleusonthespinnakermachine
AT sugiartoindar spikingneuralnetworkmodelofthelateralgeniculatenucleusonthespinnakermachine
AT furbersteve spikingneuralnetworkmodelofthelateralgeniculatenucleusonthespinnakermachine