EMBC'11 Paper Abstract

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Paper SaP18.3

Meng, Yicong (Massachusetts Institute of Technology), Poon, Chi-Sang (Massachusetts Institute of Technology), Monzon, Joshua (MIT), Zhou, Kuan (University of New Hampshire)

Iono-Neuromorphic Implementation of Spike-Time-Dependent Synaptic Plasticity

Scheduled for presentation during the Poster Session "Advances in Brain Physiology and Modeling" (SaP18), Saturday, September 3, 2011, 09:30−11:00, America Ballroom Westin

33rd Annual International IEEE EMBS Conference, August 30 - September 3, 2011, Boston Marriott Copley Place, Boston, MA, USA

This information is tentative and subject to change. Compiled on April 20, 2014

Keywords Brain physiology and modeling - Neural circuits, Brain physiology and modeling - Neuron modeling, Brain physiology and modeling - Neural dynamics and computation

Abstract

Spike-timing-dependent plasticity (STDP) is the ability of a synapse to increase or decrease its efficacy in response to specific temporal pairing of pre- and post-synaptic activities. It is widely believed that such activity-dependent long-term changes in synaptic connection strength underlie the brainís capacity of learning and memory. However, current phenomenological models of STDP fail to reproduce classical forms of synaptic plasticity that are based on stimulus frequency (BCM rule) instead of timing (STDP rule). In this paper, we implemented a novel biophysical synaptic plasticity model by using analog VLSI (aVLSI) circuits biased in the subthreshold regime. We show that the aVLSI synapse model successfully emulates both the STDP and BCM forms of synaptic plasticity as predicted by the biophysical model.

 

 

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