Optimization of Neuronal Morphologies for Pattern Recognition

Abstract

This thesis addresses the problem of how the dendritic structure and other morphological properties of the neuron can determine its pattern recognition performance. The techniques used in this work for generating dendritic trees with different morphologies included the following three methods. Firstly, dendritic trees were produced by exhaustively generating every possible morphology. Where this was not possible due to the size of morphological space, I sampled systematically from the possible morphologies. Lastly, dendritic trees were evolved using an evolutionary algorithm, which varied existing morphologies using selection, mutation and crossover. From these trees, I constructed full compartmental conductance-based models of neurons. I then assessed the performance of the resulting neuronal models by quantifying their ability to discriminate between learned and novel input patterns. The morphologies generated were tested in the presence and absence of active conductances. The results have shown that the morphology does have a considerable effect on pattern recognition performance. In fact, neurons with a small mean depth of their dendritic tree are the best pattern recognizers. Moreover, the performance of neurons is anti-correlated with mean depth. Interestingly, the symmetry of the neuronal morphology does not correlate with performance. This research has also revealed that the evolutionary algorithm could find effective morphologies for both passive models and models with active conductances. In the active model, there was a considerable change in the performance of the original population of neurons, which largely resulted from changes in the morphological parameters such as dendritic compartmental length and tapering. However, no single parameter setting guaranteed good neuronal performance; in three separate runs of the evolutionary algorithm, different sets of well performing parameters were found. In fact, the evolved neurons performed at least five times better than the original hand-tuned neurons. In summary, the combination of morphological parameters plays a key role in determining the performance of neurons in the pattern recognition task and the right combination produces very well performing neurons.