2013/12/04 by Andrey V. Olypher, Olypher, Andrey, Jean Vaillant +1
Neuroscience · Biochemistry, Genetics and Molecular Biology · Computer Science · #Neural dynamics and brain function #Fractal and DNA sequence analysis #Neural Networks and Applications
paper · pdf · doi:10.48550/arxiv.1312.1206
Information processing in certain neuronal networks in the brain can be\nconsidered as a map of binary vectors, where ones (spikes) and zeros (no\nspikes) of input neurons are transformed into spikes and no spikes of output\nneurons. A simple but fundamental characteristic of such a map is how it\ntransforms distances between input vectors. In particular what is the mean\ndistance between output vectors given certain distance between input vectors?\nUsing combinatorial approach we found an exact solution to this problem for\nnetworks of perceptrons with binary weights. he resulting formulas allow for\nprecise analysis how network connectivity and neuronal excitability affect the\ntransformation of distances between the vectors of neuronal spiking. As an\napplication, we considered a simple network model of information processing in\nthe hippocampus, a brain area critically implicated in learning and memory, and\nfound a combination of parameters for which the output neurons discriminated\nsimilar and distinct inputs most effectively. A decrease of threshold values of\nthe output neurons, which in biological networks may be associated with\ndecreased inhibition, impaired optimality of discrimination.\n