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NIPS
2004

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

9 years 14 days ago
Methods for Estimating the Computational Power and Generalization Capability of Neural Microcircuits
What makes a neural microcircuit computationally powerful? Or more precisely, which measurable quantities could explain why one microcircuit 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 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 m...
Wolfgang Maass, Robert A. Legenstein, Nils Bertsch
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2004
Where NIPS
Authors Wolfgang Maass, Robert A. Legenstein, Nils Bertschinger
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