A mixed-signal implementation of a polychronous spiking neural network with delay adaption

Runchun M. Wang, Tara J. Hamilton, Jonathan Tapson, André van Schaik

    Research output: Contribution to journalArticlepeer-review

    25 Citations (Scopus)

    Abstract

    We present a mixed-signal implementation of a re-configurable polychronous spiking neural network capable of storing and recalling spatio-temporal patterns. The proposed neural network contains one neuron array and one axon array. Spike Timing Dependent Delay Plasticity is used to fine-tune delays and add dynamics to the network. In our mixed-signal implementation, the neurons and axons have been implemented as both analog and digital circuits. The system thus consists of one FPGA, containing the digital neuron array and the digital axon array, and one analog IC containing the analog neuron array and the analog axon array. The system can be easily configured to use different combinations of each. We present and discuss the experimental results of all combinations of the analog and digital axon arrays and the analog and digital neuron arrays. The test results show that the proposed neural network is capable of successfully recalling more than 85% of stored patterns using both analog and digital circuits.
    Original languageEnglish
    Number of pages16
    JournalFrontiers in Neuroscience
    Volume8
    Issue number51
    DOIs
    Publication statusPublished - 2014

    Open Access - Access Right Statement

    Copyright © 2014 Wang, Hamilton, Tapson and van Schaik. 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.

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