Design and implementation of BCM rule based on spike-timing dependent plasticity

Rahimi Azghadi, Mostafa, Al-Sarawi, Said, Iannella, Nicolangelo, and Abbott, Derek (2012) Design and implementation of BCM rule based on spike-timing dependent plasticity. In: Proceedings of the 2012 International Joint Conference on Neural Networks. pp. 1-7. From: 2012 International Joint Conference on Neural Networks (IJCNN), 10-15 June 2012, Brisbane, QLD, Australia.

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The Bienenstock-Cooper-Munro (BCM) and Spike Timing-Dependent Plasticity (STDP) rules are two experimentally verified form of synaptic plasticity where the alteration of synaptic weight depends upon the rate and the timing of pre- and post-synaptic firing of action potentials, respectively. Previous studies have reported that under specific conditions, i.e. when a random train of Poissonian distributed spikes are used as inputs, and weight changes occur according to STDP, it has been shown that the BCM rule is an emergent property. Here, the applied STDP rule can be either classical pair-based STDP rule, or the more powerful triplet-based STDP rule. In this paper, we demonstrate the use of two distinct VLSI circuit implementations of STDP to examine whether BCM learning is an emergent property of STDP. These circuits are stimulated with random Poissonian spike trains. The first circuit implements the classical pair-based STDP, while the second circuit realizes a previously described triplet-based STDP rule. These two circuits are simulated using 0.35 μm CMOS standard model in HSpice simulator. Simulation results demonstrate that the proposed triplet-based STDP circuit significantly produces the threshold-based behaviour of the BCM. Also, the results testify to similar behaviour for the VLSI circuit for pair-based STDP in generating the BCM.

Item ID: 45712
Item Type: Conference Item (Research - E1)
ISBN: 978-1-4673-1489-3
Funders: Australian Research Council (ARC)
Date Deposited: 29 Aug 2017 02:27
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 @ 100%
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