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A Solid-State Neuron for Spiking Neural Network Implementation

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Yajie Chen, Yajie, Hall, Steve, McDaid, Liam, Buiu, Octavian and Kelly, Peter (2008) A Solid-State Neuron for Spiking Neural Network Implementation. Engineering Letters, 16 (1). pp. 83-89. [Journal article]

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URL: http://www.engineeringletters.com/issues_v16/issue_1/EL_16_1_13.pdf

Abstract

This paper presents a compact analog neuron cell incorporating an array of charge-coupledsynapses connected via a common output terminal. The novel silicon synapse is based on a two stage charge-coupled device where the weighting functionality can be integrated into the first stage. A pre-synaptic spike to the second gate allows the charge under the first gate to drift onto the floating diffusion output stage to produce a current, or voltage spike. Parallel defined synapses are each assigned to the left hand side of a current mirror gate where the right hand side feeds into a thresholding inverter. The decay of the membrane potential is mimicked by the charge leakage through a reverse-biased diode, whose model is verified by comparing the simulations and measured data. Spice simulation results show that the proposed neuron cell is capable of capturing the summing and thresholding dynamics of biological neurons.

Item Type:Journal article
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Intelligent Systems Research Centre
ID Code:20667
Deposited By:Professor Liam McDaid
Deposited On:05 Jan 2012 13:55
Last Modified:05 Jan 2012 13:55

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