Methods for Estimating the Computational Power and Generalization
Capability of Neural Microcircuits
W. Maass, R. Legenstein, and N. Bertschinger
What makes a neural microcircuit computationally powerful? Or more precisely,
which measurable quantities could explain why one microcircuit
suited for a particular family of computational tasks than another
? 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 models drawn from
different distributions. We validate the proposed measures by comparing their
prediction with direct evaluations of the computational 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 generalization capability of circuits in different
dynamic regimes (UP- and DOWN-states) that have been demonstrated through
intracellular recordings in vivo.
Reference: W. Maass, R. Legenstein, and N. Bertschinger.
Methods for estimating the computational power and generalization capability
of neural microcircuits.
In L. K. Saul, Y. Weiss, and L. Bottou, editors, Advances in Neural
Information Processing Systems, volume 17, pages 865-872. MIT Press, 2005.