Spike timing dependent adaptation : minimising the effect of transistor mismatch in an analogue VLSI neuromorphic system
Year: 2006
Type: dissertation
Abstract: Neuromorphic systems often call for subthreshold operation where transistor mismatch is a particular problem and this mismatch can affect the time constants of the design.This work is an investigation into whether Spike Timing Dependent Plasticity (STDP), a neural algorithm capable of adapting time delays within neural systems, can provide a method to minimise the effect of transistor mismatch.This work is set within the context of a depth-from-motion algorithm, the performance of which will be degraded by mismatch when implemented in analogue VLSI.A circuit is designed which predicts the arrival of a spike from the timing of two earlier spikes.The error between the actual spike arrival time and the prediction is used to improve future predictions.Two spike timing dependent adaptation methods are described.These were fabricated using an Austria Microsystems (AMS) 0.35 jtm process and the results are reported.The key measure is the prediction error: before adaptation the error reflects the amount of mismatch within the prediction circuitry; after adaptation the error indicates to what extent the adaptive circuitry can minimise the effect of transistor mismatch.For both designs it is shown that the effect of transistor mismatch can be greatly reduced through spike timing dependent adaptation.Many people have made the last four years great fun.These are just some of them.Alan Murray as my supervisor provided me with advice and support.Adria Bofill-i-Petit and Sebastian Loeda introduced me to the world of spiking neural networks and STDP.
Source: ERA
Author: Katherine Cameron
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2302016097