Bio-Inspired Hybrid Framework for Multi-view Face Detection

McCarroll, Niall; Belatreche, Ammar; Harkin, Jim; Li, Yuhua

doi:10.1007/978-3-319-26561-2_28

Cited by 1 publication

(1 citation statement)

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“…Spiking neural networks (SNNs) use models derived from the spike timing of neurons to encode and process information. Recent research has demonstrated the advantages of these computing paradigms in modelling memory [1], pattern recognition [2], fault tolerance [3] and also in many other applications [4,5]. Astrocytes have been shown to co-exist with neurons [6] where these cells communicate with synapses and neurons, and in doing so, regulate synaptic morphology and formation [6].…”

Section: Introductionmentioning

confidence: 99%

Self-repairing Learning Rule for Spiking Astrocyte-Neuron Networks

Liu

McDaid

Harkin

et al. 2017

Neural Information Processing

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In this paper a novel self-repairing learning rule is proposed which is a combination of the spike-timing-dependent plasticity (STDP) and Bienenstock, Cooper, and Munro (BCM) learning rules: in the derivation of this rule account is taken of the coupling of GABA interneurons to the tripartite synapse. The rule modulates the plasticity level by shifting the plasticity window, associated with STDP, up and down the vertical axis as a function of postsynaptic neural activity. Specifically when neurons are inactive, the window is shifted up the vertical axis (open) and as the postsynaptic neuron activity increases and, as learning progresses, the plasticity window moves down the vertical axis until learning ceases. Simulation results are presented which show that the proposed approach can still maintain the network performance even with a fault density approaching 80% and because the rule is implemented using a minimal computational overhead it has potential for large scale spiking neural networks in hardware.

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