Document Detail


A superposable silicon synapse with programmable reversal potential.
MedLine Citation:
PMID:  23366006     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
We present a novel log-domain silicon synapse designed for subthreshold analog operation that emulates common synaptic interactions found in biology. Our circuit models the dynamic gating of ion-channel conductances by emulating the processes of neurotransmitter release-reuptake and receptor binding-unbinding in a superposable fashion: Only a single circuit is required to model the entire population of synapses (of a given type) that a biological neuron receives. Unlike previous designs, which are strictly excitatory or inhibitory, our silicon synapse implements-for the first time in the log-domain-a programmable reversal potential (i.e., driving force). To demonstrate our design's scalability, we fabricated in 180nm CMOS an array of 64K silicon neurons, each with four independent superposable synapse circuits occupying 11.0×21.5 µm(2) apiece. After verifying that these synapses have the predicted effect on the neurons' spike rate, we explored a recurrent network where the synapses' reversal potentials are set near the neurons' threshold, acting as shunts. These shunting synapses synchronized neuronal spiking more robustly than nonshunting synapses, confirming that reversal potentials can have important network-level implications.
Authors:
Ben V Benjamin; John V Arthur; Peiran Gao; Paul Merolla; Kwabena Boahen
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference     Volume:  2012     ISSN:  1557-170X     ISO Abbreviation:  Conf Proc IEEE Eng Med Biol Soc     Publication Date:  2012  
Date Detail:
Created Date:  2013-01-31     Completed Date:  2013-09-06     Revised Date:  2014-08-21    
Medline Journal Info:
Nlm Unique ID:  101243413     Medline TA:  Conf Proc IEEE Eng Med Biol Soc     Country:  United States    
Other Details:
Languages:  eng     Pagination:  771-4     Citation Subset:  IM    
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MeSH Terms
Descriptor/Qualifier:
Action Potentials
Bioengineering
Models, Neurological*
Neurons / physiology
Silicon
Synapses / physiology*
Transistors, Electronic
Grant Support
ID/Acronym/Agency:
DPI-OD000965/OD/NIH HHS
Chemical
Reg. No./Substance:
Z4152N8IUI/Silicon

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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