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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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 > Intelligent Systems Research Centre|
Computer Science Research Institute
|Deposited By:||Professor Liam McDaid|
|Deposited On:||05 Jan 2012 13:55|
|Last Modified:||05 Jan 2012 13:55|
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