PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Edge of Chaos and Prediction of Computational Performance for Neural Circuit Models
Robert Legenstein and Wolfgang Maass
Neural Networks Number Special Issue on Echo State and Liquid State Networks, 2007.


We analyze in this article the significance of the edge of chaos for real time computations in neural microcircuit models consisting of spiking neurons and dynamic synapses. We find that the edge of chaos predicts quite well those values of circuit parameters that yield maximal computational performance. But obviously it makes no prediction of their computational performance for other parameter values. Therefore, we propose a new method for predicting the computational performance of neural microcircuit models. The new measure estimates directly the kernel property and the generalization capability of a neural microcircuit. We validate the proposed measure by comparing its prediction with direct evaluations of the computational performance of various neural microcircuit models. The proposed method also allows us to quantify differences in the computational performance and generalization capability of neural circuits in different dynamic regimes (UP and DOWN-states) that have been demonstrated through intracellular recordings in vivo.

PDF - PASCAL Members only - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:2598
Deposited By:Wolfgang Maass
Deposited On:22 November 2006