From in vitro to computational models of the brain: What can criticality tell us about neural computation?

It has been hypothesized that some biological systems, including parts of the brain, may operate near the critical point of a phase transition, delicately balanced between ordered and disordered behavior. Criticality maximizes a number of properties that are favorable for computation, such as dynamic range, information transmission, and the number of input–output mappings; put more simply, systems at criticality are well-suited to take inputs for some computational problem, perform transformations on the inputs as they propagate through the system, and give meaningful outputs. In this talk, I will present our ongoing work on evaluating the closeness of in vitro neuronal networks to criticality and discuss the implications of our findings for understanding neural computation and the development of bioinspired computing methods and hardware. To determine the closeness of networks to criticality, the spatiotemporal scaling behavior of neuronal avalanches is observed, and we compare this behavior across networks with different seeding densities as they mature. The functional connectivity is also obtained for these networks and the small-worldness assessed using the small-world propensity measure. Additionally, the excitation-to-inhibition ratio was chemically manipulated to evaluate its effect on the network dynamics. Although none of our networks tended to mature toward the critical state, we observed consistent differences across the two considered densities in the avalanche scaling and branching ratio indicative of a greater dominance of network bursts in the higher-density networks. Additionally, the lower-density networks tended to show small-world organization in their functional connectivity. Chemical perturbation to increase inhibition brought the networks closer to criticality, and the higher-density networks showed greater disruption to both their functional activity and avalanche behavior under chemical perturbation. From these findings, we aim to find connectivity and activity patterns correlated with closeness to criticality so that we may emulate these features in models for bioinspired computation. Future work will also involve evaluating measures of information processing in these networks as indicators of the suitability of these networks for computation.