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Synergies Between Intrinsic and Synaptic Plasticity Mechanisms

triesch{at}fias.uni-frankfurt.de Frankfurt Institute for Advanced Studies, Johann Wolfgang Goethe University, 60438 Frankfurt am Main, Germany, and Department of Cognitive Science, University of California, San Diego, La Jolla, CA 92093-0515, U.S.A.

We propose a model of intrinsic plasticity for a continuous activation model neuron based on information theory. We then show how intrinsic and synaptic plasticity mechanisms interact and allow the neuron to discover heavy-tailed directions in the input. We also demonstrate that intrinsic plasticity may be an alternative explanation for the sliding threshold postulated in the BCM theory of synaptic plasticity. We present a theoretical analysis of the interaction of intrinsic plasticity with different Hebbian learning rules for the case of clustered inputs. Finally, we perform experiments on the "bars" problem, a popular nonlinear independent component analysis problem.

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