Inherently stochastic spiking neurons for probabilistic neural computation

Maruan Al-Shedivat, Rawan Naous, Emre Neftci, Gert Cauwenberghs, Khaled N. Salama

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Scopus citations


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.
Original languageEnglish (US)
Title of host publication2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages4
ISBN (Print)9781467363891
StatePublished - Apr 2015


Dive into the research topics of 'Inherently stochastic spiking neurons for probabilistic neural computation'. Together they form a unique fingerprint.

Cite this