Effective neuronal learning with ineffective Hebbian learning rules.
In this article we revisit the classical neuroscience paradigm of Hebbian learning. We find that it is difficult to achieve effective associative memory storage by Hebbian synaptic learning, since it requires network-level information at the synaptic level or sparse coding level. Effective learning can yet be achieved even with nonsparse patterns by a neuronal process that maintains a zero sum of the incoming synaptic efficacies. This weight correction improves the memory capacity of associative networks from an essentially bounded one to a memory capacity that scales linearly with network size. It also enables the effective storage of patterns with multiple levels of activity within a single network. Such neuronal weight correction can be successfully carried out by activity-dependent homeostasis of the neuron's synaptic efficacies, which was recently observed in cortical tissue. Thus, our findings suggest that associative learning by Hebbian synaptic learning should be accompanied by continuous remodeling of neuronally driven regulatory processes in the brain.
Center for Neural Computation, Hebrew University, Jerusalem, Israel, and School of Mathematical Sciences, Tel-Aviv University, Tel Aviv, 69978, Israel.
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Chechik
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Meilijson
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Ruppin
D000465:Algorithms; D001245:Association Learning / Q000502:physiology; D017774:Long-Term Potentiation; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D013569:Synapses / Q000502:physiology