Deep visual learning with spike-timing dependent plasticity

UNSPECIFIED (2017) Deep visual learning with spike-timing dependent plasticity. PhD thesis, University of Lincoln.

28660 Liu Daqi - Computing - March 2017.pdf
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Item Type:Thesis (PhD)
Item Status:Live Archive


For most animal species, reliable and fast visual pattern recognition is vital for
their survival. Ventral stream, a primary pathway within visual cortex, plays an important
role in object representation and form recognition. It is a hierarchical system
consisting of various visual areas, in which each visual area extracts different level of
abstractions. It is known that the neurons within ventral stream use spikes to represent
these abstractions. To increase the level of realism in a neural simulation, spiking
neural network (SNN) is often used as the neural network model. From SNN point of
view, the analog output values generated by traditional artificial neural network (ANN)
can be considered as the average spiking firing rates. Unlike traditional ANN, SNN
can not only use spiking rates but also specific spiking timing sequences to represent
the structural information of the input visual stimuli, which greatly increases the distinguishability.
To simulate the learning procedure of the ventral stream, various research questions
need to be resolved. In most cases, traditional methods use winner-take-all strategy to
distinguish different classes. However, such strategy works not well for overlapped
classes within decision space. Moreover, neurons within ventral stream tends to recognize
new input visual stimuli in a limited time window, which requires a fast learning
procedure. Furthermore, within ventral stream, neurons receive continuous input visual
stimuli and can only access local information during the learning procedure. However,
most traditional methods use separated visual stimuli as the input and incorporate
global information within the learning period. Finally, to verify the universality of the
proposed SNN framework, it is necessary to investigate its classification performance
for complex real world tasks such as video-based face disguise recognition.
To address the above problems, a novel classification method inspired by the soft
winner-take-all strategy has been proposed firstly, in which each associated class will
be assigned with a possibility and the input visual stimulus will be classified as the
class with the highest possibility. Moreover, to achieve a fast learning procedure, a
novel feed-forward SNN framework equipped with an unsupervised spike-timing dependent
plasticity (STDP) learning rule has been proposed. Furthermore, an eventdriven
continuous STDP (ECS) learning method has been proposed, in which two
novel continuous input mechanisms have been used to generate a continuous input
visual stimuli and a new event-driven STDP learning rule based on the local information
has been applied within the training procedure. Finally, such methodologies have
also been extended to the video-based disguise face recognition (VDFR) task in which
human identities are recognized not just on a few images but the sequences of video
stream showing facial muscle movements while speaking

Keywords:Pattern recognition
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:28660
Deposited On:05 Sep 2017 11:24

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