Selfreparing neural networks: A model for recovery from brain damage

Abstract

We introduce selfrepairing neural networks as a model for recovery from brain damage. Small lesions are repaired through reinstatement of the redundancy in the network's connections. With mild lesions, this process can model autonomous recovery. Moderate lesions require patterned input. In this paper, we discuss implementations in three types of network of increasing biological plausibility. We also mention some results from random graph theory. Finally, we discuss the implications for rehabilitation theory.