Rivest, F., & Shultz, T. R. (2005). Learning with both adequate computational power and biological realism. Proceedings of the 2005 Canadian Artificial Intelligence Conference: Workshop on Correlation Learning (pp. 15-23). University of Victoria, Victoria, BC.



Computational learning rules considered to be biologically realistic are not only rare but are also known to be seriously underpowered in the sense that they cannot, by themselves, implement the learning that humans and other mammals are capable of. We show mathematically that the computationally-powerful learning rules used in the cascade-correlation family of algorithms can be rewritten in a form that is a small extension of the Hebb rule, which is widely regarded as being biologically realistic. This suggests a way in which computationally-sufficient learning rules could be implemented in real neurons.


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