Blackman, AV; (2015) Target-cell-specific synaptic properties in neocortical microcircuits. Doctoral thesis , UCL (University College London).

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Abstract

A major focus of modern neuroscience is to establish the links between structure, physiology and function in neural cells and circuits. One key strand of this effort is in establishing the number and properties of cardinal cell types; increasing evidence suggests that many physiological and functional properties of neural circuits may be cell- and synapse-specific. Cortical interneurons are one group of cells which may be comprised of a large number of distinct classes with differing genetic, physiological and functional properties. Studies suggest that axonal morphology may be one of the most useful and simple indicators of these interneuronal types. The results presented in this thesis contribute to knowledge of both anatomical cell-type classification and the function of presynaptic NMDA receptors in visual cortex. Firstly, the utility of two-photon microscopy to create neural reconstructions suitable for cell-type classification is validated. However, reconstructions created from two-photon imaging suffer from errors when used in computer modelling due to overestimation of neurite diameters when compared to biocytin reconstructions of the same cells. Cell-type classification from two-photon imaging is then utilised in elucidating the target-cell-specific expression and function of presynaptic NMDA receptors (preNMDARs) in layer 5 of visual cortex; controversy regarding the existence of these receptors may be explained by their selective expression at synapses from pyramidal cells onto particular postsynaptic cell types. The target-specific expression of preN M DARs, along with synapse-specific differences in short-term plasticity, contributes to the spatiotemporal remapping of inhibition across the somatodendritic axis of pyramidal cells during high-frequency firing, mediated by somatostatin and parvalbumin - expressing interneurons. Finally, the reconstructions, cell types and results from this work are used to develop and validate a time-saving approach based on Sholl analysis to classify cells from bitmap images without the need for laborious manual reconstructions – something which should facilitate high-throughput future studies of neural anatomy and morphology.

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