Paper Info
Title
Methods for estimating the computational power and generalization capability of neural microcircuits
Abstract
<p>\&nbsp;</p> <div>What makes a neural microcircuit computationally powerful? Or more precisely, which measurable quantities could explain why one microcircuit <img width="16" height="13" border="0" align="bottom" src="http://www.igi.tugraz.at/Abstracts/MaassETAL:04/img1.png" alt="$C$" /> is better suited for a particular family of computational tasks than another microcircuit <img width="20" height="14" border="0" align="bottom" src="http://www.igi.tugraz.at/Abstracts/MaassETAL:04/img2.png" alt="$C{\textquoteright}$" />? 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.</div> <p>\&nbsp;</p>
Year
Venue
Keywords
2004
NIPS
membrane potential
Field
DocType
Citations 
Measure (mathematics),Computer science,Artificial intelligence,Electronic circuit,Machine learning
Conference
15
PageRank 
References 
Authors
1.78
1
3
Name
Order
Citations
PageRank
Wolfgang Maass13717391.51
Robert Legenstein260744.70
Nils Bertschinger322521.10