Rahimi Azghadi, Mostafa, Al-Sarawi, Said, Iannella, Nicolangelo, and Abbott, Derek (2011) Emergent BCM via neuromorphic VLSI synapses with STDP. In: Australian Workshop on Computational Neuroscience. p. 31. From: 5th Australian Workshop on Computational Neuroscience, 13-14 December 2011, University of Western Sydney, Campbelltown, NSW.

PDF (Published Version) - Published Version Restricted to Repository staff only

Abstract

The Bienenstock-Cooper-Munro (BCM) rule1-3 is an experimentally verified form of synaptic plasticity where the alteration of synaptic weight depends upon the rate of pre and postsynaptic firing of action potentials. Previous theoretical studies have investigated how the precise timing of random pre- and post-synaptic spike activity and spike timing-dependent plasticity (STDP) leads to changes in the efficacy of synaptic weights, assuming Poissonian spike statistics. In particular, when a particular class of STDP rule, based upon multiple spike interactions are used, the time averaged behaviour of synaptic weight changes was shown to exhibit analogous behaviour to the classical BCM rule and can inherit its functional properties. Here, we present two distinct neuromorphic VLSI circuit implementations and some of their behaviour. The first circuit implements the classical pair-based STDP4, while the second realizes a previously described triplet-based STDP rule5. We use these different circuits to examine whether BCM learning is an emergent property. A 0.35 µm standard CMOS process model has been used in HSpice to implement the two mentioned circuits. Simulation results demonstrate how well, the proposed triplet-based STDP circuit produces the threshold-based behaviour of the BCM. Also, the results testify to similar behaviour for the VLSI circuit for pair-based STDP ingenerating the BCM.

Item ID:	45723
Item Type:	Conference Item (Abstract / Summary)
Related URLs:	Organisation
Date Deposited:	20 Dec 2016 22:29
FoR Codes:	08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation @ 50% 09 ENGINEERING > 0906 Electrical and Electronic Engineering > 090604 Microelectronics and Integrated Circuits @ 50%
SEO Codes:	97 EXPANDING KNOWLEDGE > 970109 Expanding Knowledge in Engineering @ 30% 97 EXPANDING KNOWLEDGE > 970110 Expanding Knowledge in Technology @ 40% 97 EXPANDING KNOWLEDGE > 970108 Expanding Knowledge in the Information and Computing Sciences @ 30%
Downloads:	Total: 1
	More Statistics