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Biophysical modeling of synaptic plasticity and its function in the dynamics of neuronal networks

Plasticity of neuronal circuitry in the brain is a fundamental process thought to underlie behavior, cognition and memory. Recent experimental evidence suggest that plasticity in individual synaptic afferent from CA3 pyramidal cells onto CA1 postsynaptic neurons in the hippocampus involve discrete synaptic states. 
In Chapter 2 we develop a theoretical framework to study the biophysical origin of this observed discrete transitions in the synaptic states. The developed biophysical model is tested on various plasticity induction protocols. The key feature of the model is that it provides a natural bound on changes in the synaptic strength. 
The later part of the thesis; explores functional significance of synaptic plasticity in neuronal networks. In particular, in Chapter 3 we study the dynamics of song learning in oscine birds. We develop a dynamical model for the song system nuclei and suggest an important dynamical role for synaptic plasticity in the control and maintenance of learned adult birdsong. 
In Chapter 4 we study yet another application of synaptic plasticity function in networks. We develop a neuronal network, termed an "Interspike Interval Recognition Unit", (IRU) that uses synaptic plasticity of inhibitory synapses to train itself onto a given pattern of input spike sequences and is then able to selectively respond to the same input pattern on subsequent presentation. 
It is known that neurons communicate through short voltage pulses called 'spikes'. If all the spikes are similar in shape and structure then, all the information must be encoded in the interspike intervals of these spike sequences. The IRU thus proposes to provide an answer to an important biological question: What kind of neural circuits in the brain can decode the information in the inter spike interval sequence and what learning mechanism mediates this decoding process? 
Finally in Chapter 5, we develop an electronic circuit model for type I neurons and use it to construct a time delay circuit, which is an abstraction from the song system anterior forebrain pathway. The circuit is able to produce precise time delays on the order of 10--100 ms, controlled by strength of intrinsic synaptic strength. We give two examples demonstrating the function of the circuit in producing precise time delays.