Efficient VLSI Architecture for Odor Recognition with a Spiking Neural Network
Sakthivel R.1, Savika Singha2, Alaida H. M.3, Akhila S4
1Sakthivel R, Department of Electronics Engineering, VIT, Vellore (Tamil Nadu), India.
2Savika Singha, Department of Electronics Engineering, VIT, Vellore (Tamil Nadu), India.
3Alaida H.M, Department of Electronics Engineering, VIT, Vellore (Tamil Nadu), India.
4Akhila S, Department of Electronics Engineering, VIT, Vellore (Tamil Nadu), India.
Manuscript received on 14 December 2019 | Revised Manuscript received on 22 December 2019 | Manuscript Published on 31 December 2019 | PP: 6-9 | Volume-9 Issue-1S3 December 2019 | Retrieval Number: A10021291S319/19©BEIESP | DOI: 10.35940/ijeat.A1002.1291S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Olfactory System, Spike Timing, Spiking Neural Network, STDP.
Scope of the Article: Pattern Recognition
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