Inherently stochastic spiking neurons for probabilistic neural computation

Handle URI:
http://hdl.handle.net/10754/577114
Title:
Inherently stochastic spiking neurons for probabilistic neural computation
Authors:
Al-Shedivat, Maruan ( 0000-0001-9037-1005 ) ; Naous, Rawan ( 0000-0001-6129-7926 ) ; Neftci, Emre; Cauwenberghs, Gert; Salama, Khaled N. ( 0000-0001-7742-1282 )
Abstract:
Neuromorphic engineering aims to design hardware that efficiently mimics neural circuitry and provides the means for emulating and studying neural systems. In this paper, we propose a new memristor-based neuron circuit that uniquely complements the scope of neuron implementations and follows the stochastic spike response model (SRM), which plays a cornerstone role in spike-based probabilistic algorithms. We demonstrate that the switching of the memristor is akin to the stochastic firing of the SRM. Our analysis and simulations show that the proposed neuron circuit satisfies a neural computability condition that enables probabilistic neural sampling and spike-based Bayesian learning and inference. Our findings constitute an important step towards memristive, scalable and efficient stochastic neuromorphic platforms. © 2015 IEEE.
KAUST Department:
Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Electrical Engineering Program; Sensors Lab
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)
Conference/Event name:
7th International IEEE/EMBS Conference on Neural Engineering, NER 2015
Issue Date:
Apr-2015
DOI:
10.1109/NER.2015.7146633
Type:
Conference Paper
Appears in Collections:
Conference Papers; Computer Science Program; Electrical Engineering Program; Sensors Lab; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorAl-Shedivat, Maruanen
dc.contributor.authorNaous, Rawanen
dc.contributor.authorNeftci, Emreen
dc.contributor.authorCauwenberghs, Gerten
dc.contributor.authorSalama, Khaled N.en
dc.date.accessioned2015-09-10T14:18:48Zen
dc.date.available2015-09-10T14:18:48Zen
dc.date.issued2015-04en
dc.identifier.doi10.1109/NER.2015.7146633en
dc.identifier.urihttp://hdl.handle.net/10754/577114en
dc.description.abstractNeuromorphic engineering aims to design hardware that efficiently mimics neural circuitry and provides the means for emulating and studying neural systems. In this paper, we propose a new memristor-based neuron circuit that uniquely complements the scope of neuron implementations and follows the stochastic spike response model (SRM), which plays a cornerstone role in spike-based probabilistic algorithms. We demonstrate that the switching of the memristor is akin to the stochastic firing of the SRM. Our analysis and simulations show that the proposed neuron circuit satisfies a neural computability condition that enables probabilistic neural sampling and spike-based Bayesian learning and inference. Our findings constitute an important step towards memristive, scalable and efficient stochastic neuromorphic platforms. © 2015 IEEE.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.titleInherently stochastic spiking neurons for probabilistic neural computationen
dc.typeConference Paperen
dc.contributor.departmentComputer Science Programen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentElectrical Engineering Programen
dc.contributor.departmentSensors Laben
dc.identifier.journal2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)en
dc.conference.date22 April 2015 through 24 April 2015en
dc.conference.name7th International IEEE/EMBS Conference on Neural Engineering, NER 2015en
dc.contributor.institutionInstitute for Neural Computation, University of California, San Diego, La Jolla, CA 92093, USAen
kaust.authorAl-Shedivat, Maruanen
kaust.authorSalama, Khaled N.en
kaust.authorNaous, Rawanen
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