Assessing Degree of Functionality of Simple and Microcolumnar Neuronal Networks by Measuring Aspects of Information Processing: A Computational Approach

Abstract

Some areas in the cerebral cortex are characterized by microcolumns: arrays of interconnected neurons which may constitute a fundamental computational unit in the brain. These microcolumns (also known as minicolumns), are formed by small, vertical columns of neurons that can span all layers in the gray matter. Although correlative studies have established that microcolumns can lose their primary characteristics in normal aging, neurological, and neurodegenerative diseases, the exact function of these structures has not been established. Using computer simulations of highly detailed neuronal networks, we study whether there is a functional advantage in neuronal networks with a microcolumnar geometry as compared to other geometrically distributed networks. In particular, these simulations take into account microcolumnar, crystalline, and random distributions of neurons. At the assigned location of each neuron, we position unique, geometrically precise neurons and determine synaptic connectivity of the networks via three models: fully-connected, gaussian, and hypergeometric connectivity models, taking into account the physical distances between the neurons. By utilizing the neuronal simulation package NEURON, we perform functional tests on these neuronal networks, such as information maintenance and scaling effects of network growth. Results on the advantages and disadvantages of each network topology are presented, and arguments are formulated that could explain microcolumnar neuronal networks as a natural evolution to maximize information processing in the brain.