PlaNeural: Spiking Neural Networks that Plan | Zendy

Ian Mitchell | Zendy; Christian Huyck | Zendy; Carl Evans | Zendy

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PlaNeural: Spiking Neural Networks that Plan

Author(s) -

Ian Mitchell,

Christian Huyck,

Carl Evans

Publication year - 2016

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2016.07.425

Subject(s) - computer science , spiking neural network , plan (archaeology) , neuromorphic engineering , spike (software development) , process (computing) , robot , component (thermodynamics) , virtual machine , artificial neural network , human–computer interaction , distributed computing , artificial intelligence , software engineering , operating system , physics , thermodynamics , archaeology , history

PlaNeural is a spike-based neural network that has the ability to plan. The network is a spreading activation network implemented with Cell Assemblies; this combination has built a dynamic network of nodes that is able to interact with an environment and respond appropriately. PlaNeural uses Cell Assemblies to make decisions and plan - there is no pre-determined code managing the decision process that leads to planning. PlaNeural is the planning component of a virtual robot in a virtual environment. This paper describes PlaNeural's behaviour in two virtual environments, programmed independently of it; actions are completed in a closed-loop. PlaNeural was programmed in PyNN, executed with Nest and on a neuromorphic platform, SpiNNaker. PlaNeural has been tested on two environments and results show a successful performance; in both cases PlaNeural takes appropriate actions to fulfil user selected goals based on environmental changes

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