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Title: Dynamic and stochastic behaviour of neocortical synapses
Author: Brémaud, Antoine
ISNI:       0000 0004 2715 5563
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2011
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The neocortex receives inputs from many other brain regions, it contains many different types of neurones in 6 layers and processes large volumes of information. This thesis deals with some of the properties of the local synaptic circuitry of the neocortex. Dual intracellular recordings with biocytin labelling were performed in slices of adult rat neocortex in vitro using conventional sharp micro-electrodes. Responses of postsynaptic cells to trains of presynaptic action potentials were recorded. Histological processing identified the cells recorded and laminar location. The amplitude of each excitatory postsynaptic potential (EPSP), in each sweep was measured. Subsets of measurements for which conditions were deemed to be stable were selected. For recordings that included multiple data subsets whose amplitudes differed primarily because of differences in presynaptic release probability (p), the binomial parameters n (number of synapses), p and q (quantal amplitude) were estimated by fitting relationships between EPSP coefficient of variation, variance or proportion of failures of release and mean amplitude, with equations based on simple binomial models. Striking differences in the binomial parameters estimated for different classes of connections were found. To determine how far the outcomes of this analysis depended on the assumption of a simple binomial model in which p and q are identical at all synapses, Monte-Carlo simulations of simple and more complex binomial models of synaptic release were generated. These models demonstrated the wide range of conditions under which analysis based on simple binomial models can provide reliable estimates of n, p and q. Computational models (NEURON) that integrate short term synaptic dynamics with a stochastic simulation of synaptic transmission were developed. These models display properties similar to those displayed by synapses, but not observed in traditional deterministic models of release. For example, recovery from synaptic depression has peaks and troughs superimposed on a smooth exponential decay.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available