Effect of Neuromodulation of Short-Term Plasticity on Information Processing in Hippocampal Interneuron Synapses

Neurons convey information about the complex dynamic environment in the form of signals. Computational neuroscience provides a theoretical foundation toward enhancing our understanding of nervous system. The aim of this dissertation is to present techniques to study the brain and how it processes information in particular neurons in hippocampus. We begin with a brief review of the history of neuroscience and biological background of basic neurons. To appreciate the importance of information theory, familiarity with the information theoretic basics is required, these basics are presented in Chapter 2. In Chapter 3, we use information theory to estimate the amount of information postsynaptic responses carry about the preceding temporal activity of hippocampal interneuron synapses and estimate the amount of synaptic memory. In Chapter 4, we infer parsimonious approximation of the data through analytical expression for calcium concentration and postsynaptic response distribution when calcium decay time is significantly smaller that the interspike intervals. In Chapter 5, we focus on the study and use of Causal State Splitting Reconstruction (CSSR) algorithm to capture the structure of the postsynaptic responses. The CSSR algorithm captures patterns in the data by building a machine in the form of visible Markov Models. One of the main advantages of CSSR with respect to Markov Models is that it builds states containing more than one histories, so the obtained machines are smaller than the equivalent Markov Model

Data Driven Models of Short-Term Synaptic Plasticity

Simple models of short term synaptic plasticity that incorporate facilitation and/or depression have been created in abundance for different synapse types and circumstances. The analysis of these models has included computing mutual information between a stochastic input spike train and some sort of representation of the postsynaptic response. While this approach has proven useful in many contexts, for the purpose of determining the type of process underlying a stochastic output train, it ignores the ordering of the responses, leaving an important characterizing feature on the table. In this paper we use a broader class of information measures on output only, and specifically construct hidden Markov models (HMMs) (known as epsilon machines or causal state models) to differentiate between synapse type, and classify the complexity of the process. We find that the machines allow us to differentiate between processes in a way not possible by considering distributions alone. We are also able to understand these differences in terms of the dynamics of the model used to create the output response, bringing the analysis full circle. Hence this technique provides a complimentary description of the synaptic filtering process, and potentially expands the interpretation of future experimental results

Data_Sheet_1_Data Driven Models of Short-Term Synaptic Plasticity.pdf

<p>Simple models of short term synaptic plasticity that incorporate facilitation and/or depression have been created in abundance for different synapse types and circumstances. The analysis of these models has included computing mutual information between a stochastic input spike train and some sort of representation of the postsynaptic response. While this approach has proven useful in many contexts, for the purpose of determining the type of process underlying a stochastic output train, it ignores the ordering of the responses, leaving an important characterizing feature on the table. In this paper we use a broader class of information measures on output only, and specifically construct hidden Markov models (HMMs) (known as epsilon machines or causal state models) to differentiate between synapse type, and classify the complexity of the process. We find that the machines allow us to differentiate between processes in a way not possible by considering distributions alone. We are also able to understand these differences in terms of the dynamics of the model used to create the output response, bringing the analysis full circle. Hence this technique provides a complimentary description of the synaptic filtering process, and potentially expands the interpretation of future experimental results.</p