Analysis of the neural hypercolumn in parallel PCSIM simulations

Large and sudden changes in pitch or loudness occur statistically less frequently than gradual fluctuations, which means that natural sounds typically exhibit 1/f spectra. Experiments conducted on human subjects showed that listeners indeed prefer 1/f distributed melodies to melodies with faster or slower dynamics. It was recently demonstrated by using animal models, that neurons in primary auditory cortex of anesthetized ferrets exhibit a pronounced preference to stimuli that exhibit 1/f statistics. In the visual modality, it was shown that neurons in primary visual cortex of macaque monkeys exhibit tuning to sinusoidal gratings featuring 1/f dynamics.One might therefore suspect that neurons in mammalian cortex exhibit Self-Organizing Criticality. Indeed, we have found SOC-like phenomena in neurophysiological data collected in rat primary somatosensory cortex. In this paper we concentrated on investigation of the dynamics of cortical hypercolumn consisting of about 128 thousand simulated neurons. The set of 128 Liquid State Machines, each consisting 1024 neurons, was simulated on a simple cluster built of two double quad-core machines (16 cores).PCSIM was designed as a tool for simulating artificial biological-like neural networks composed of different models of neurons and different types of synapses. The simulator was written in C++ with a primary interface dedicated for the Python programming language. As its authors ensure it is intended to simulate networks containing up to millions of neurons and on the order of billions of synapses. This is achieved by distributing the network over different nodes of a computing cluster by using Message Passing Interface.The results obtained for Leaky Integrate-and-Fire model of neurons used for the construction of the hypercolumn and varying density of inter-column connections will be discussed. Benchmarking results for using the PCSIM on the cluster and predictions for grid computing will be presented to some extent. Research presented herein makes a good starting point for the simulations of very large parts of mammalian brain cortex and in some way leading to better understanding of the functionality of human brain.