Triplet Spike-Timing Dependent Plasticity in a Spiking Neuronal Network for Speech Recognition
Keywords
Loading...
Authors
Issue Date
2022-08-01
Language
en
Document type
Journal Title
Journal ISSN
Volume Title
Publisher
Title
ISSN
Volume
Issue
Startpage
Endpage
DOI
Abstract
Advances in Automatic Speech Recognition (ASR) show promising results, achieving
∼98% accuracy [1]. Yet, these remarkable engineering efforts usually aim at improving
recognition accuracy, without direct investigation of biological processes or their dynamics.
The mechanisms underlying human language learning and processing are still not
fully understood: a key puzzle is how synapses strengthen their interconnections to allow
the formation of stable linguistic representations. In this thesis I investigate the dynamics
of the triplet rule [2], a synaptic plasticity rule designed around the idea that it is
the timing between pre- and post-synaptic spikes that determines the magnitude of a
synaptic binding. The hypothesis is that the sparse spatio-temporal structure of spikeencoded
language stimuli could elicit stronger synaptic development when plasticity is
governed by the triplet rule, rather than by the voltage rule. Using a spiking neural
network model proposed in previous studies, I show that this hypothesis does not appear
to be confirmed by the experiments I have carried out.
Description
Citation
Supervisor
Faculty
Faculteit der Sociale Wetenschappen