  In this work, we report the preliminary analysis of the electrophysiological
behavior of in vitro neuronal networks to identify when the networks are in a
critical state based on the size distribution of network-wide avalanches of
activity. 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, as criticality is widely considered to be the
state in which a system is best suited for computation. This type of analysis
is expected to enable the identification of networks that are well-suited for
computation and the classification of networks as perturbed or healthy. 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.
