Methods for Estimating the Computational Power and Generalization Capability of Neural Microcircuits

Abstract

What makes a neural microcircuit computationally powerful? Or more precisely, which measurable quantities could explain why one microcir- cuit C is better suited for a particular family of computational tasks than another microcircuit C ? We propose in this article quantitative measures for evaluating the computational power and generalization capability of a neural microcircuit, and apply them to generic neural microcircuit mod- els drawn from different distributions. We validate the proposed mea- sures by comparing their prediction with direct evaluations of the com- putational performance of these microcircuit models. This procedure is applied first to microcircuit models that differ with regard to the spatial range of synaptic connections and with regard to the scale of synaptic efficacies in the circuit, and then to microcircuit models that differ with regard to the level of background input currents and the level of noise on the membrane potential of neurons. In this case the proposed method allows us to quantify differences in the computational power and gen- eralization capability of circuits in different dynamic regimes (UP- and DOWN-states) that have been demonstrated through intracellular record- ings in vivo.