Woltmann, Daniela (2013) A statistical and information theoretic analysis of auditory coding in the ribbon synapse. PhD thesis, University of Warwick.

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Abstract

This thesis investigates the neural coding strategy of the first coding synapse in the auditory system, the inner hair cell ribbon synapse. First, the distribution of the inter-spike intervals and the correlations between consecutive spikes in the experimental data were established for spontaneous release events in the absence of stimulation. This was then used as a benchmark to determine how well existing stochastic models of the inner hair cell ribbon synapse replicate the statistics of the experimental data. It was found that the Sumner model replicates the statistics well within behaviourally relevant timescales. The Sumner model was used as the basis for an information-theoretic analysis of synaptic coding. In order to estimate the information transmission across the synapse, the auditory pathway was first interpreted as a communication channel in the information theoretic sense. This, together with the Sumner model, allowed us then to use naturalistic speech inputs, in the form of the BKB sentences, to estimate the channel capacity of the ribbon synapse. Furthermore, the information rate and information efficiency of a population of auditory nerve fibres was estimated in order to probe the functional role of spontaneous release in auditory coding. It was found that the coding strategies differ for quiet sounds and conversational speech. For quiet sounds, the information rate and information efficiency are maximised by maximising the spontaneous release of all fibres in the population. However, for conversational speech the information efficiency is maximised by minimising the spontaneous release rate of at least one fibre, whereas the information rate is maximised by a distribution of medium to high spontaneous rate fibres.

Item Type:	Thesis (PhD)
Subjects:	Q Science > QP Physiology
Library of Congress Subject Headings (LCSH):	Synapses, Auditory pathways, Neural networks (Neurobiology), Hair cells
Official Date:	September 2013
Dates:	Date Event September 2013 Submitted
Institution:	University of Warwick
Theses Department:	Systems Biology Doctoral Training Centre
Thesis Type:	PhD
Publication Status:	Unpublished
Supervisor(s)/Advisor:	Stocks, Nigel
Extent:	ix, 207 leaves : illustrations, charts.
Language:	eng

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