Inter-spikes-intervals exponential and gamma distributions study of neuron firing rate for SVITE motor control model on FPGA

This paper presents a statistical study on a neuro-inspired spike-based implementation of the Vector-Integration-To-End-Point motor controller (SVITE) and compares its deterministic neuron-model stream of spikes with a proposed modification that converts the model, and thus the controller, in a Pois...

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Autores: Pérez-Peña, Fernando, Morgado Estévez, Arturo, Linares Barranco, Alejandro
Tipo de recurso: artículo
Estado:Versión enviada para evaluación y publicación
Fecha de publicación:2015
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/92904
Acceso en línea:https://hdl.handle.net/11441/92904
https://doi.org/10.1016/j.neucom.2014.08.024
Access Level:acceso abierto
Palabra clave:Bio-inspired
Neuro-inspired
AER
LFSR
Poisson
FPGA
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spelling Inter-spikes-intervals exponential and gamma distributions study of neuron firing rate for SVITE motor control model on FPGAPérez-Peña, FernandoMorgado Estévez, ArturoLinares Barranco, AlejandroBio-inspiredNeuro-inspiredAERLFSRPoissonFPGAThis paper presents a statistical study on a neuro-inspired spike-based implementation of the Vector-Integration-To-End-Point motor controller (SVITE) and compares its deterministic neuron-model stream of spikes with a proposed modification that converts the model, and thus the controller, in a Poisson like spike stream distribution. A set of hardware pseudo-random numbers generators, based on a Linear Feedback Shift Register (LFSR), have been introduced in the neuron-model so that they reach a closer biological neuron behavior. To validate the new neuron-model behavior a comparison between the Inter-Spikes-Interval empirical data and the Exponential and Gamma distributions has been carried out using the Kolmogorov–Smirnoff test. An in-hardware validation of the controller has been performed in a Spartan6 FPGA to drive directly with spikes DC motors from robotics to study the behavior and viability of the modified controller with random components. The results show that the original deterministic spikes distribution of the controller blocks can be swapped with Poisson distributions using 30-bit LFSRs. The comparative between the usable controlling signals such as the trajectory and the speed profile using a deterministic and the new controller show a standard deviation of 11.53 spikes/s and 3.86 spikes/s respectively. These rates do not affect our system because, within Pulse Frequency Modulation, in order to drive the motors, time length can be fixed to spread the spikes. Tuning this value, the slow rates could be filtered by the motor. Therefore, this SVITE neuro-inspired controller can be integrated within complex neuromorphic architectures with Poisson-like neurons.ElsevierArquitectura y Tecnología de ComputadoresTEP-108: Robótica y Tecnología de Computadores Aplicada a la Rehabilitación2015info:eu-repo/semantics/articleinfo:eu-repo/semantics/submittedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/92904https://doi.org/10.1016/j.neucom.2014.08.024reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésNeurocomputing, 149, part B (February 2015), 496-504.https://www.sciencedirect.com/science/article/pii/S0925231214010388info:eu-repo/semantics/openAccessoai:idus.us.es:11441/929042026-06-17T12:51:07Z
dc.title.none.fl_str_mv Inter-spikes-intervals exponential and gamma distributions study of neuron firing rate for SVITE motor control model on FPGA
title Inter-spikes-intervals exponential and gamma distributions study of neuron firing rate for SVITE motor control model on FPGA
spellingShingle Inter-spikes-intervals exponential and gamma distributions study of neuron firing rate for SVITE motor control model on FPGA
Pérez-Peña, Fernando
Bio-inspired
Neuro-inspired
AER
LFSR
Poisson
FPGA
title_short Inter-spikes-intervals exponential and gamma distributions study of neuron firing rate for SVITE motor control model on FPGA
title_full Inter-spikes-intervals exponential and gamma distributions study of neuron firing rate for SVITE motor control model on FPGA
title_fullStr Inter-spikes-intervals exponential and gamma distributions study of neuron firing rate for SVITE motor control model on FPGA
title_full_unstemmed Inter-spikes-intervals exponential and gamma distributions study of neuron firing rate for SVITE motor control model on FPGA
title_sort Inter-spikes-intervals exponential and gamma distributions study of neuron firing rate for SVITE motor control model on FPGA
dc.creator.none.fl_str_mv Pérez-Peña, Fernando
Morgado Estévez, Arturo
Linares Barranco, Alejandro
author Pérez-Peña, Fernando
author_facet Pérez-Peña, Fernando
Morgado Estévez, Arturo
Linares Barranco, Alejandro
author_role author
author2 Morgado Estévez, Arturo
Linares Barranco, Alejandro
author2_role author
author
dc.contributor.none.fl_str_mv Arquitectura y Tecnología de Computadores
TEP-108: Robótica y Tecnología de Computadores Aplicada a la Rehabilitación
dc.subject.none.fl_str_mv Bio-inspired
Neuro-inspired
AER
LFSR
Poisson
FPGA
topic Bio-inspired
Neuro-inspired
AER
LFSR
Poisson
FPGA
description This paper presents a statistical study on a neuro-inspired spike-based implementation of the Vector-Integration-To-End-Point motor controller (SVITE) and compares its deterministic neuron-model stream of spikes with a proposed modification that converts the model, and thus the controller, in a Poisson like spike stream distribution. A set of hardware pseudo-random numbers generators, based on a Linear Feedback Shift Register (LFSR), have been introduced in the neuron-model so that they reach a closer biological neuron behavior. To validate the new neuron-model behavior a comparison between the Inter-Spikes-Interval empirical data and the Exponential and Gamma distributions has been carried out using the Kolmogorov–Smirnoff test. An in-hardware validation of the controller has been performed in a Spartan6 FPGA to drive directly with spikes DC motors from robotics to study the behavior and viability of the modified controller with random components. The results show that the original deterministic spikes distribution of the controller blocks can be swapped with Poisson distributions using 30-bit LFSRs. The comparative between the usable controlling signals such as the trajectory and the speed profile using a deterministic and the new controller show a standard deviation of 11.53 spikes/s and 3.86 spikes/s respectively. These rates do not affect our system because, within Pulse Frequency Modulation, in order to drive the motors, time length can be fixed to spread the spikes. Tuning this value, the slow rates could be filtered by the motor. Therefore, this SVITE neuro-inspired controller can be integrated within complex neuromorphic architectures with Poisson-like neurons.
publishDate 2015
dc.date.none.fl_str_mv 2015
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/submittedVersion
format article
status_str submittedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/92904
https://doi.org/10.1016/j.neucom.2014.08.024
url https://hdl.handle.net/11441/92904
https://doi.org/10.1016/j.neucom.2014.08.024
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Neurocomputing, 149, part B (February 2015), 496-504.
https://www.sciencedirect.com/science/article/pii/S0925231214010388
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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