Population Coding for Neuromorphic Hardware

Saad Qasim Khan, Arfan Ghani, Muhammed Khurram

    Research output: Contribution to journalArticlepeer-review

    4 Citations (Scopus)
    59 Downloads (Pure)

    Abstract

    Population coding has been established as the key mechanism for decoding sensory information received from periphery organs such as retinas and cochlear. In this paper a novel architecture is presented to embed population coding in neuromorphic hardware. A resonance based mechanism between two layers of neuron is utilised in the presented work. The mechanism discussed in this paper facilitates selective triggering of higher layer neurons which serves as target ensemble for evaluation of a particular input of interest. It has been shown that presented model can be used to detect any physical quantity (light intensity, temperature, etc) feed to the sensor on the basis of population coding.
    Original languageEnglish
    Pages (from-to)153-164
    Number of pages12
    JournalNeurocomputing
    Volume239
    Early online date10 Feb 2017
    DOIs
    Publication statusPublished - 24 May 2017

    Bibliographical note

    NOTICE: this is the author’s version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neurocomputing, [239, (2017)] DOI: 10.1016/j.neucom.2017.02.013

    © 2017, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

    Keywords

    • Population Coding
    • Neuromorphic Hardware
    • Synaptic Plasticity
    • Interspike Interval
    • Tuning Curve

    Fingerprint

    Dive into the research topics of 'Population Coding for Neuromorphic Hardware'. Together they form a unique fingerprint.

    Cite this