Relating circuit dynamics to computation: robustness and dimension-specific computation in cortical dynamics

Shaul Druckmann

(Stanford University)

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Date: February 5, 2018


Neural dynamics represent the hard-to-interpret substrate of circuit computations. Advances in large-scale recordings have highlighted the sheer spatiotemporal complexity of circuit dynamics, portraying in detail the difficulty of interpreting such dynamics and relating it to computation. Considering preparatory activity in a delayed response task we utilized neural recordings performed simultaneously with optogenetic perturbations to probe circuit dynamics. First, we revealed a surprising robustness in the detailed evolution of certain dimensions of the population activity, beyond what was thought to be the case experimentally and theoretically. Second, the robust dimension in activity space carries nearly all of the decodable behavioral information whereas other non-robust dimensions contained nearly no decodable information, consistent with the circuit being setup to make informative dimensions stiff, i.e., resistive to perturbations, leaving uninformative dimensions sloppy, i.e., sensitive to perturbations. This constitutes important evidence for earlier theoretical proposals of dimension specific computation since the circuit is built to selectively stabilize the computationally significant dimensions. Finally, we show that this robustness can be achieved by a modular organization of circuitry, whereby modules whose dynamics normally evolve independently can correct each other’s dynamics when an individual module is perturbed, a common design feature in robust systems engineering.

Created: Monday, February 5th, 2018