Assessment and manipulation of the computational capacity of in vitro neuronal networks through criticality in neuronal avalanches

The brain is an effective and efficient computational machine, yet the precise mechanisms it uses to perform computations are poorly understood. As demand for technologies capable of storing and processing large amounts of data increases, it would be beneficial to harness the computational power of the brain in engineerable computing hardwares; however, to recapitulate the desired behaviors, we must first grasp the dynamics underlying the communication within networks of neurons. To this end, a preliminary analysis of the electrophysiological behavior of in vitro neuronal networks of primary rat cortical neurons was performed to identify when the networks are in a critical state based on the size distribution of network-wide avalanches of activity. The critical state is defined as a transitional state between static or cyclical behavior and highly disordered or hyperactive behavior, and systems in the critical state are thought to be in the optimal conditions to perform computational tasks. The neuronal networks were observed as they matured from day in vitro 7 to 51 and were chemically perturbed with GABA on day 51 to determine if networks that do not reach the critical state during normal maturation can be manipulated into the critical state by reducing the excitation-to-inhibition ratio. The results presented here demonstrate the importance of selecting appropriate parameters in the evaluation of the size distribution and indicate that it is possible to perturb networks showing highly synchronized—or supercritical—behavior into the critical state by increasing the level of inhibition in the network. The classification of critical versus non-critical networks is valuable in identifying networks that can be expected to perform well on computational tasks, and it is expected that perturbed networks or disease models may show different behaviors with regard to criticality during the course of maturation. This study is part of a larger research project, the overarching aim of which is to develop computational models that are able to reproduce target behaviors observed in in vitro neuronal networks. These models will ultimately be used to aid in the realization of these behaviors in nanomagnet arrays to be used in novel computing hardwares.