Author: Vinod Valsalam
Self-organization of connection patterns within brain areas of animals begins prenatally, and has been shown to depend on internally generated patterns of neural activity. Such activity is genetically controlled and has been proposed to give the neural system an appropriate bias so that it can learn reliably from complex environmental stimuli. We demonstrate this idea computationally using competitive learning networks for recognizing handwritten digits. Animations of the learning process show how training the network with patterns from an evolved pattern generator before training with the actual training set improves learning performance.

Demo website
James A. Bednar Postdoctoral Alumni jbednar [at] inf ed ac uk
Risto Miikkulainen Faculty risto [at] cs utexas edu
Vinod Valsalam Ph.D. Alumni vkv [at] alumni utexas net
Developing Complex Systems Using Evolved Pattern Generators 2007
Vinod K. Valsalam, James A. Bednar and Risto Miikkulainen, IEEE Transactions on Evolutionary Computation (2007), pp. 181-198.
Establishing an Appropriate Learning Bias Through Development 2006
Vinod K. Valsalam, James A. Bednar, and Risto Miikkulainen, In Proceedings of the Fifth International Conference on Development and Learning (ICDL-2006) 2006.
Constructing Good Learners Using Evolved Pattern Generators 2005
Vinod K. Valsalam, James A. Bednar, and Risto Miikkulainen, In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-2005, H.-G. Beyer and others (Eds.), pp. 11-18 2005.