DYNAMICS OF BURST PATTERNS GENERATED BY MONOLAYER NETWORKS IN CULTURE

Abstract

Publisher Summary Over the past few decades, cross-fertilization between the fields of neurophysiology, physics, mathematics, and computer science has helped in the complex efforts to understand the mechanisms used by the central nervous system and has provided a wealth of challenging theoretical problems. On the engineering side, neuronally inspired models and devices have proved themselves very useful in applied pattern recognition and robotics. Artificial neural nets are used commercially for complex technological processes and for providing rapid solutions to optimization problems. This chapter explains how very simple neuronal networks, composed of only a few hundred neurons, can be complicated and dynamically rich. The main challenge is to better control and understand this dynamics at the cellular and ensemble level with emphasis on the emerging network properties. It is also essential to supplement pharmacological manipulations with electrical stimulation of increasing pattern complexity at single electrodes and on a spatial pattern of electrodes. The observed richness of the dynamical repertoire of small neuronal networks serves a definite purpose in living systems, and such studies may become a possible source of inspiration for yet another generation of hardware devices and computing algorithms.