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In this work, we report the preliminary analysis of the electrophysiologicalbehavior of in vitro neuronal networks to identify when the networks are in acritical state based on the size distribution of network-wide avalanches ofactivity. The results presented here demonstrate the importance of selectingappropriate parameters in the evaluation of the size distribution and indicatethat it is possible to perturb networks showing highly synchronized---orsupercritical---behavior into the critical state by increasing the level ofinhibition in the network. The classification of critical versus non-criticalnetworks is valuable in identifying networks that can be expected to performwell on computational tasks, as criticality is widely considered to be thestate in which a system is best suited for computation. This type of analysisis expected to enable the identification of networks that are well-suited forcomputation and the classification of networks as perturbed or healthy. Thisstudy is part of a larger research project, the overarching aim of which is todevelop computational models that are able to reproduce target behaviorsobserved in in vitro neuronal networks. These models will ultimately be used toaid in the realization of these behaviors in nanomagnet arrays to be used innovel computing hardwares.

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