Rahimi Azghadi, S. Mostafa (2014) Neuromorphic VLSI designs for spike timing and rate-based synaptic plasticity with application in pattern classification. PhD thesis, The University of Adelaide.

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Abstract

This thesis presents a versatile study on the design and Very Large Scale Integration(VLSI) implementation of various synaptic plasticity rules ranging from phenomenological rules, to biophysically realistic ones. In particular, the thesis aims at developing novel spike timing-based learning circuits that advance the current neuromorphic systems, in terms of power consumption, compactness and synaptic modification (learning) abilities. Furthermore, the thesis investigates the usefulness of the developed designs and algorithms in specific engineering tasks such as pattern classification. To follow the mentioned goals, this thesis makes several original contributions to the field of neuromorphic engineering, which are briefed in the following.

First, a programmable multi-neuron neuromorphic chip is utilised to implement a number of desired rate- and timing-based synaptic plasticity rules. Specific software programs are developed to set up and program the neuromorphic chip, in a way to show the required neuronal behaviour for implementing various synaptic plasticity rules. The classical version of Spike Timing Dependent Plasticity (STDP), as well as the triplet-based STDP and the rate-based Bienenstock-Cooper-Munro (BCM) rules are implemented and successfully tested on this neuromorphic device. In addition, the implemented triplet STDP learning mechanism is utilised to train a feedforward spiking neural network to classify complex rate-based patterns, with a high classification performance.

In the next stage, VLSI designs and implementations of a variety of synaptic plasticity rules are studied and weaknesses and strengths of these implementations are highlighted. In addition, the applications of these VLSI learning networks, which build upon various synaptic plasticity rules are discussed. Furthermore, challenges in the way of implementing these rules are investigated and effective ways to address those challenges are proposed and reviewed. This review provides us with deep insight into the design and application of synaptic plasticity rules in VLSI.

Next, the first VLSI designs for the triplet STDP learning rule are developed, which significantly outperform all their pair-based STDP counterparts, in terms of learning capabilities. It is shown that a rate-based learning feature is also an emergent property of the new proposed designs. These primary designs are further developed to generate two different VLSI circuits with various design goals. One of these circuits that has been fabricated in VLSI as a proof of principle chip, aimed at maximising the learning performance—but this results in high power consumption and silicon real estate. The second design, however, slightly sacrifices the learning performance, while remarkably improves the silicon area, as well as the power consumption of the design, in comparison to all previous triplet STDP circuits, as well as many pair-based STDP circuits. Besides, it significantly outperforms other neuromorphic learning circuits with various biophysical as well as phenomenological plasticity rules, not only in learning but also in area and power consumption. Hence, the proposed designs in this thesis can play significant roles in future VLSI implementations of both spike timing and rate based neuromorphic learning systems with increased learning abilities. These systems offer promising solutions for a wide set of tasks, ranging from autonomous robotics to brain machine interfaces.

Item ID:	45721
Item Type:	Thesis (PhD)
Keywords:	neuromorphic engineering; VLSI; neural systems; synapse; neurons; synaptic plasticity; STDP; BCM; pattern classification
Additional Information:	This thesis is openly accessible from the link to the University of Adelaide's institutional repository above.
Date Deposited:	01 Feb 2017 01:54
FoR Codes:	08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation @ 30% 10 TECHNOLOGY > 1007 Nanotechnology > 100705 Nanoelectronics @ 30% 09 ENGINEERING > 0906 Electrical and Electronic Engineering > 090604 Microelectronics and Integrated Circuits @ 40%
SEO Codes:	97 EXPANDING KNOWLEDGE > 970109 Expanding Knowledge in Engineering @ 30% 97 EXPANDING KNOWLEDGE > 970110 Expanding Knowledge in Technology @ 30% 97 EXPANDING KNOWLEDGE > 970108 Expanding Knowledge in the Information and Computing Sciences @ 40%
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