TY - JOUR A1 - Echeveste, Rodrigo A1 - Eckmann, Samuel A1 - Gros, Claudius T1 - The Fisher information as a neural guiding principle for independent component analysis T2 - Entropy N2 - The Fisher information constitutes a natural measure for the sensitivity of a probability distribution with respect to a set of parameters. An implementation of the stationarity principle for synaptic learning in terms of the Fisher information results in a Hebbian self-limiting learning rule for synaptic plasticity. In the present work, we study the dependence of the solutions to this rule in terms of the moments of the input probability distribution and find a preference for non-Gaussian directions, making it a suitable candidate for independent component analysis (ICA). We confirm in a numerical experiment that a neuron trained under these rules is able to find the independent components in the non-linear bars problem. The specific form of the plasticity rule depends on the transfer function used, becoming a simple cubic polynomial of the membrane potential for the case of the rescaled error function. The cubic learning rule is also an excellent approximation for other transfer functions, as the standard sigmoidal, and can be used to show analytically that the proposed plasticity rules are selective for directions in the space of presynaptic neural activities characterized by a negative excess kurtosis. KW - Fisher information KW - guiding principle KW - excess kurtosis KW - objective functions KW - synaptic plasticity KW - Hebbian learning KW - independent component analysis Y1 - 2015 UR - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/40266 UR - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-402660 SN - 1099-4300 N1 - c 2015 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/). IS - 6 SP - 3838 EP - 3856 PB - MDPI CY - Basel ER -