Attractor Density Models with Application to Analyzing the Stability of Biological Neural Networks

An attractor modeling algorithm is introduced which draws upon techniques found in nonlineax dynamics and pattern recognition. The technique is motivated. by the need for quantitative measures that are able to assess the stability of biological neural networks which utilize nonlinear dynamics to process information. Attractor Density Models with Application to Analyzing the Stability of Biological Neural Networks Christian Storm and Walter J. Freeman University of California at Berkeley, Graduate Group in Biophysics, 9 Wellman Court, Berkeley, CA USA 94720 [email protected], [email protected] http://sulcus.berkeley.edu Abstract. An attractor modeling algorithm is introduced which draws upon techniques found in nonlinear dynamics and pattern recognition. The technique is motivated by the need for quantitative measures that are able to assess the stability of biological neural networks which utilize nonlinear dynamics to process information. An attractor modeling algorithm is introduced which draws upon techniques found in nonlinear dynamics and pattern recognition. The technique is motivated by the need for quantitative measures that are able to assess the stability of biological neural networks which utilize nonlinear dynamics to process information.