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 ARG-STDP.;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. DP.