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The Role of Short-Term Synaptic Plasticity in Neural Network Spiking Dynamics and in the Learning of Multiple Distal Rewards


In this thesis, we assess the role of short-term synaptic plasticity in an artificial neural

network constructed to emulate two important brain functions: self-sustained activity and

signal propagation. We employ a widely used short-term synaptic plasticity model (STP)

in a symbiotic network, in which two subnetworks with differently tuned STP behaviors are

weakly coupled. This enables both self-sustained global network activity, generated by one

of the subnetworks, as well as faithful signal propagation within subcircuits of the other

subnetwork. Finding the parameters for a properly tuned STP network is difficult. We

provide a theoretical argument for a method which boosts the probability of finding the

elusive STP parameters by two orders of magnitude, as demonstrated in tests.

We then combine STP with a novel critic-like synaptic learning algorithm, which we call

ARG-STDP for attenuated-reward-gating of STDP. STDP refers to a commonly used long-

term synaptic plasticity model called spike-timing dependent plasticity. With ARG-STDP,

we are able to learn multiple distal rewards simultaneously, improving on the previous reward

modulated STDP (R-STDP) that could learn only a single distal reward. However, we also

provide a theoretical upperbound on the number of distal reward that can be learned using


We also consider the problem of simulating large spiking neural networks. We describe

an architecture for efficiently simulating such networks. The architecture is suitable for

implementation on a cluster of General Purpose Graphical Processing Units (GPGPU). Novel

aspects of the architecture are described and an analysis of its performance is benchmarked

on a GPGPU cluster. With the advent of inexpensive GPGPU cards and compute power,

the described architecture offers an affordable and scalable tool for the design, real-time

simulation, and analysis of large scale spiking neural networks.


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