DocumentCode
1273718
Title
Silicon-Based Dynamic Synapse With Depressing Response
Author
Dowrick, T. ; Hall, Sebastian ; McDaid, Liam J.
Author_Institution
Dept. of Electr. Eng. & Electron., Univ. of Liverpool, Liverpool, UK
Volume
23
Issue
10
fYear
2012
Firstpage
1513
Lastpage
1525
Abstract
A compact implementation of a dynamic charge transfer synapse cell, capable of implementing synaptic depression, is presented. The cell is combined with a simple current mirror summing node to produce biologically plausible postsynaptic potentials (PSPs). A single charge packet is effectively transferred from the synapse to the summing node, whenever a presynaptic pulse is applied to one of its terminals. The charge packet is “weighted” by a voltage applied to the second terminal of the synapse. A voltage applied to the third terminal determines the charge recovery time in the synapse, which can be adjusted over several orders of magnitude. This voltage determines the paired pulse ratio for the synapse. The fall time of the PSP is also adjustable and is set by the gate voltage of a metal-oxide-semiconductor field-effect transistor operating in subthreshold. Results extracted from chips fabricated in a 0.35-μm complementary metal-oxide-semiconductor process, alongside theoretical and simulation results, confirm the ability of the cell to produce PSPs that are characteristic of real synapses. The concept addresses a key requirement for scalable hardware neural networks.
Keywords
field effect transistors; neural nets; charge recovery time; compact implementation; complementary metal oxide semiconductor process; depressing response; dynamic charge transfer synapse cell; gate voltage; implementing synaptic depression; metal oxide semiconductor field effect transistor; mirror summing node; postsynaptic potential; presynaptic pulse; pulse ratio; scalable hardware neural network; silicon based dynamic synapse; single charge packet; Discrete cosine transforms; Hardware; Logic gates; MOSFETs; Mathematical model; Neurons; Depressing synapses; neural network hardware; neuromorphic engineering; spike-timing-dependent plasticity (STDP);
fLanguage
English
Journal_Title
Neural Networks and Learning Systems, IEEE Transactions on
Publisher
ieee
ISSN
2162-237X
Type
jour
DOI
10.1109/TNNLS.2012.2211035
Filename
6287129
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