Shaping embodied neural networks for adaptive goal-directed behavior.

The acts of learning and memory are thought to emerge from the modifications of synaptic connections between neurons, as guided by sensory feedback during behavior. However, much is unknown about how such synaptic processes can sculpt and are sculpted by neuronal population dynamics and an interacti...

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Autores principales:	Zenas C Chao, Douglas J Bakkum, Steve M Potter
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2008
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/af3070d59d304ebdb5e9091b6b0a6603
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spelling	oai:doaj.org-article:af3070d59d304ebdb5e9091b6b0a66032021-11-25T05:41:21ZShaping embodied neural networks for adaptive goal-directed behavior.1553-734X1553-735810.1371/journal.pcbi.1000042https://doaj.org/article/af3070d59d304ebdb5e9091b6b0a66032008-03-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/18369432/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358The acts of learning and memory are thought to emerge from the modifications of synaptic connections between neurons, as guided by sensory feedback during behavior. However, much is unknown about how such synaptic processes can sculpt and are sculpted by neuronal population dynamics and an interaction with the environment. Here, we embodied a simulated network, inspired by dissociated cortical neuronal cultures, with an artificial animal (an animat) through a sensory-motor loop consisting of structured stimuli, detailed activity metrics incorporating spatial information, and an adaptive training algorithm that takes advantage of spike timing dependent plasticity. By using our design, we demonstrated that the network was capable of learning associations between multiple sensory inputs and motor outputs, and the animat was able to adapt to a new sensory mapping to restore its goal behavior: move toward and stay within a user-defined area. We further showed that successful learning required proper selections of stimuli to encode sensory inputs and a variety of training stimuli with adaptive selection contingent on the animat's behavior. We also found that an individual network had the flexibility to achieve different multi-task goals, and the same goal behavior could be exhibited with different sets of network synaptic strengths. While lacking the characteristic layered structure of in vivo cortical tissue, the biologically inspired simulated networks could tune their activity in behaviorally relevant manners, demonstrating that leaky integrate-and-fire neural networks have an innate ability to process information. This closed-loop hybrid system is a useful tool to study the network properties intermediating synaptic plasticity and behavioral adaptation. The training algorithm provides a stepping stone towards designing future control systems, whether with artificial neural networks or biological animats themselves.Zenas C ChaoDouglas J BakkumSteve M PotterPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 4, Iss 3, p e1000042 (2008)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Zenas C Chao Douglas J Bakkum Steve M Potter Shaping embodied neural networks for adaptive goal-directed behavior.
description	The acts of learning and memory are thought to emerge from the modifications of synaptic connections between neurons, as guided by sensory feedback during behavior. However, much is unknown about how such synaptic processes can sculpt and are sculpted by neuronal population dynamics and an interaction with the environment. Here, we embodied a simulated network, inspired by dissociated cortical neuronal cultures, with an artificial animal (an animat) through a sensory-motor loop consisting of structured stimuli, detailed activity metrics incorporating spatial information, and an adaptive training algorithm that takes advantage of spike timing dependent plasticity. By using our design, we demonstrated that the network was capable of learning associations between multiple sensory inputs and motor outputs, and the animat was able to adapt to a new sensory mapping to restore its goal behavior: move toward and stay within a user-defined area. We further showed that successful learning required proper selections of stimuli to encode sensory inputs and a variety of training stimuli with adaptive selection contingent on the animat's behavior. We also found that an individual network had the flexibility to achieve different multi-task goals, and the same goal behavior could be exhibited with different sets of network synaptic strengths. While lacking the characteristic layered structure of in vivo cortical tissue, the biologically inspired simulated networks could tune their activity in behaviorally relevant manners, demonstrating that leaky integrate-and-fire neural networks have an innate ability to process information. This closed-loop hybrid system is a useful tool to study the network properties intermediating synaptic plasticity and behavioral adaptation. The training algorithm provides a stepping stone towards designing future control systems, whether with artificial neural networks or biological animats themselves.
format	article
author	Zenas C Chao Douglas J Bakkum Steve M Potter
author_facet	Zenas C Chao Douglas J Bakkum Steve M Potter
author_sort	Zenas C Chao
title	Shaping embodied neural networks for adaptive goal-directed behavior.
title_short	Shaping embodied neural networks for adaptive goal-directed behavior.
title_full	Shaping embodied neural networks for adaptive goal-directed behavior.
title_fullStr	Shaping embodied neural networks for adaptive goal-directed behavior.
title_full_unstemmed	Shaping embodied neural networks for adaptive goal-directed behavior.
title_sort	shaping embodied neural networks for adaptive goal-directed behavior.
publisher	Public Library of Science (PLoS)
publishDate	2008
url	https://doaj.org/article/af3070d59d304ebdb5e9091b6b0a6603
work_keys_str_mv	AT zenascchao shapingembodiedneuralnetworksforadaptivegoaldirectedbehavior AT douglasjbakkum shapingembodiedneuralnetworksforadaptivegoaldirectedbehavior AT stevempotter shapingembodiedneuralnetworksforadaptivegoaldirectedbehavior
_version_	1718414536625618944

Shaping embodied neural networks for adaptive goal-directed behavior.

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