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Abstract:
This paper revisits the classical neuroscience paradigm of
Hebbian learning. We show that a necessary requirement for
effective associative memory learning is that the efficacies of the
incoming synapses should be uncorrelated. This requirement is
difficult to achieve in a robust manner by Hebbian synaptic
learning, since it depends on network level information. Effective
learning can yet be obtained by a neuronal process that maintains a
zero sum of the incoming synaptic efficacies. This normalization
drastically improves the memory capacity of associative networks,
from an essentially bounded capacity to one that linearly scales
with the network's size. Such neuronal normalization 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 strongly suggest that effective
associative learning with Hebbian synapses alone is biologically
implausible and that Hebbian synapses must be continuously
remodeled by neuronally-driven regulatory processes in the
brain.
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