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Evolution of biologically plausible neural networks performing a visually guided reaching task

Published: 12 July 2014 Publication History

Abstract

An evolutionary strategy (ES) algorithm was utilized to evolve a simulated neural network based on the known anatomy of the posterior parietal cortex (PPC), to perform a visually guided reaching task. In this task, a target remained visible for the duration of a trial, and an agent's goal was to move its hand to the target as rapidly as possible and remain for the duration of that trial. The ES was used to tune the strength of 15609 connections between neural areas and 4 parameters governing the neural dynamics. The model had sensory latencies replicating those found in recording studies with monkeys. The ES ran 100 times and generated very diverse networks that could all perform the task well. The evolved networks 1) showed velocity profiles consistent with biological movements, and 2) found solutions that reflect short-range excitation and long-range, contralateral inhibition similar to neurobiological networks. These results provide theoretical evidence for the important parameters and projections governing sensorimotor transformations in neural systems.

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Cited By

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  • (2022)Exploration of Ontological Representations for Evolutionary Computation2022 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC55065.2022.9870376(1-8)Online publication date: 18-Jul-2022
  • (2017)Evolutionary algorithm optimization of biological learning parameters in a biomimetic neuroprosthesisIBM Journal of Research and Development10.1147/JRD.2017.265675861:2-3(6:1-6:14)Online publication date: 1-Mar-2017
  • (2016)An Evolutionary Framework for Replicating Neurophysiological Data with Spiking Neural NetworksParallel Problem Solving from Nature – PPSN XIV10.1007/978-3-319-45823-6_50(537-547)Online publication date: 31-Aug-2016

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    cover image ACM Conferences
    GECCO '14: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation
    July 2014
    1478 pages
    ISBN:9781450326629
    DOI:10.1145/2576768
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 12 July 2014

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    Author Tags

    1. evolutionary strategy
    2. neural networks
    3. sensorimotor transformation
    4. visually guided reaching

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    GECCO '14: Genetic and Evolutionary Computation Conference
    July 12 - 16, 2014
    BC, Vancouver, Canada

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    GECCO '14 Paper Acceptance Rate 180 of 544 submissions, 33%;
    Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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    View all
    • (2022)Exploration of Ontological Representations for Evolutionary Computation2022 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC55065.2022.9870376(1-8)Online publication date: 18-Jul-2022
    • (2017)Evolutionary algorithm optimization of biological learning parameters in a biomimetic neuroprosthesisIBM Journal of Research and Development10.1147/JRD.2017.265675861:2-3(6:1-6:14)Online publication date: 1-Mar-2017
    • (2016)An Evolutionary Framework for Replicating Neurophysiological Data with Spiking Neural NetworksParallel Problem Solving from Nature – PPSN XIV10.1007/978-3-319-45823-6_50(537-547)Online publication date: 31-Aug-2016

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