Open AccessEfficient VLSI Architecture for Odor Recognition with a Spiking Neural Network
Author(s) -
R. Sakthivel,
Santanu Singha,
H. M. Alaida,
S. Akhila
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.a1002.1291s319
Subject(s) - spike timing dependent plasticity , odor , spiking neural network , neuroscience , computer science , spike (software development) , postsynaptic potential , synaptic plasticity , artificial neural network , artificial intelligence , plasticity , pattern recognition (psychology) , biology , physics , thermodynamics , biochemistry , receptor , software engineering
In this paper spiking neural network (SNN) is presented which can discriminate odor data. Spike timing dependent synaptic plasticity (STDP) means a plasticity which is controlled by the presynaptic and postsynaptic spikes time difference. Using this STDP rule the synaptic weights are modified after the mitral and before the cortical cells. In order to determine whether the circuit has correctly identified the odor the SNN has either a high or a low response at the output for any odor given as the input.