Maximizing the coding capacity of neuronal networks

Information in neuronal networks is encoded as spatiotemporal patterns of activity. The capacity of a network may thus be thought of as the number of stable spatiotemporal patterns it can generate. To understand what structural attributes of a network enable it to generate a profusion of stable patterns, we simulated an array of 9 x 9 neurons modelled as pulse-coupled oscillators. The structure of the network was inspired by the popular puzzle Sudoku such that its periodic responses mapped to solutions of the puzzle. Given that there are nearly a 109 possible Sudokus, this networks could possibly generate 109 spatiotemporal patterns. We show that the number of stable patterns were maximized when excitatory and inhibitory inputs to each neuron were balanced. When this balance was disrupted, only a subset of patterns with certain symmetries survived.