Edge of Chaos and Prediction of Computational Performance for Neural
R. Legenstein and W. Maass
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
Reference: R. Legenstein and W. Maass.
Edge of chaos and prediction of computational performance for neural circuit
Neural Networks, 20(3):323-334, 2007.