Generating functionals for computational intelligence: the Fisher information as an objective function for self-limiting Hebbian learning rules

  • Generating functionals may guide the evolution of a dynamical system and constitute a possible route for handling the complexity of neural networks as relevant for computational intelligence.We propose and explore a new objective function, which allows to obtain plasticity rules for the afferent synaptic weights. The adaption rules are Hebbian, self-limiting, and result from the minimization of the Fisher information with respect to the synaptic flux. We perform a series of simulations examining the behavior of the new learning rules in various circumstances.The vector of synaptic weights aligns with the principal direction of input activities, whenever one is present. A linear discrimination is performed when there are two or more principal directions; directions having bimodal firing-rate distributions, being characterized by a negative excess kurtosis, are preferred. We find robust performance and full homeostatic adaption of the synaptic weights results as a by-product of the synaptic flux minimization. This self-limiting behavior allows for stable online learning for arbitrary durations.The neuron acquires new information when the statistics of input activities is changed at a certain point of the simulation, showing however, a distinct resilience to unlearn previously acquired knowledge. Learning is fast when starting with randomly drawn synaptic weights and substantially slower when the synaptic weights are already fully adapted.

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Author:Rodrigo Echeveste, Claudius GrosORCiDGND
Parent Title (German):Frontiers in robotics and AI
Place of publication:Lausanne
Document Type:Article
Date of Publication (online):2014/05/19
Date of first Publication:2014/05/19
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2016/05/30
Tag:Fisher information; Hebbian learning; generating functionals; homeostatic adaption; objective functions; synaptic plasticity
Issue:article 1
Page Number:14
First Page:1
Last Page:14
© 2014 Echeveste and Gros. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Corrigendum erschienen in: Frontiers in robotics and AI, 2.2015, Art. 2, doi:10.3389/frobt.2015.00002
Institutes:Physik / Physik
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):License LogoCreative Commons - Namensnennung-Nicht kommerziell-Keine Bearbeitung 3.0