A neuromorphic VLSI design for spike timing and rate based synaptic plasticity

Rahimi Azghadi, Mostafa, Al-Sarawi, Said, Abbott, Derek, and Iannella, Nicolangelo (2013) A neuromorphic VLSI design for spike timing and rate based synaptic plasticity. Neural Networks, 45. pp. 70-82.

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Triplet-based Spike Timing Dependent Plasticity (TSTDP) is a powerful synaptic plasticity rule that acts beyond conventional pair-based STDP (PSTDP). Here, the TSTDP is capable of reproducing the outcomes from a variety of biological experiments, while the PSTDP rule fails to reproduce them. Additionally, it has been shown that the behaviour inherent to the spike rate-based Bienenstock–Cooper–Munro (BCM) synaptic plasticity rule can also emerge from the TSTDP rule. This paper proposes an analogue implementation of the TSTDP rule. The proposed VLSI circuit has been designed using the AMS 0.35 μm CMOS process and has been simulated using design kits for Synopsys and Cadence tools. Simulation results demonstrate how well the proposed circuit can alter synaptic weights according to the timing difference amongst a set of different patterns of spikes. Furthermore, the circuit is shown to give rise to a BCM-like learning rule, which is a rate-based rule. To mimic an implementation environment, a 1000 run Monte Carlo (MC) analysis was conducted on the proposed circuit. The presented MC simulation analysis and the simulation result from fine-tuned circuits show that it is possible to mitigate the effect of process variations in the proof of concept circuit; however, a practical variation aware design technique is required to promise a high circuit performance in a large scale neural network. We believe that the proposed design can play a significant role in future VLSI implementations of both spike timing and rate based neuromorphic learning systems.

Item ID: 45711
Item Type: Article (Research - C1)
ISSN: 1879-2782
Keywords: synaptic plasticity; neuromorphic VLSI; spike timing dependent plasticity; rate based plasticity; BCM
Funders: Australian Research Council (ARC)
Date Deposited: 06 Dec 2016 02:58
FoR Codes: 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation @ 33%
10 TECHNOLOGY > 1007 Nanotechnology > 100705 Nanoelectronics @ 33%
09 ENGINEERING > 0906 Electrical and Electronic Engineering > 090604 Microelectronics and Integrated Circuits @ 34%
SEO Codes: 97 EXPANDING KNOWLEDGE > 970108 Expanding Knowledge in the Information and Computing Sciences @ 33%
97 EXPANDING KNOWLEDGE > 970109 Expanding Knowledge in Engineering @ 33%
97 EXPANDING KNOWLEDGE > 970110 Expanding Knowledge in Technology @ 34%
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