Corrigendum: Generating Functionals for Computational Intelligence: The Fisher Information as an Objective Function for Self-Limiting Hebbian Learning Rules

Echeveste, Rodrigo; Gros, Claudius

doi:10.3389/frobt.2015.00002

GENERAL COMMENTARY article

Front. Robot. AI , 19 February 2015

Sec. Computational Intelligence in Robotics

Volume 2 - 2015 | https://doi.org/10.3389/frobt.2015.00002

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

This article is a commentary on:

Generating Functionals for Computational Intelligence: The Fisher Information as an Objective Function for Self-Limiting Hebbian Learning Rules
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$\r\n Rodrigo Echeveste$ Rodrigo Echeveste

Claudius Gros*

Institute for Theoretical Physics, Goethe University Frankfurt, Frankfurt, Germany

A corrigendum on

Generating functionals for computational intelligence: the Fisher information as an objective function for self-limiting Hebbian learning rules
by Echeveste, R., and Gros, C. (2014). Front. Robot. AI 1:1. doi: 10.3389/frobt.2014.00001

In formula (10) of (Echeveste and Gros, 2014) the Fisher information with respect to the synaptic flux was formulated formally as an integral over the postsynaptic activity y, without stating explicitly that the postsynaptic activity y = y(y) is actually a function of the N_w presynaptic activities $y = (y_{1}, \dots, y_{N_{w}}) .$ The correct version of equation (10) is hence

F_{w} = {\int (\sum_{j = 1}^{N_{w}} w_{j} \frac{\partial}{\partial w_{j}} \ln (p (y (y)))}^{2} \times p (y) d y .

The functional dependence of the postsynaptic y on the presynaptic y was implicitly used in equation (12) and in the derivation of the synaptic plasticity rules, but otherwise not explicitly stated.

Acknowledgments

The support of the German Science Foundation (DFG) and the German Academic Exchange Service (DAAD) are acknowledged.

Reference

Echeveste, R., and Gros, C. (2014). Generating functionals for computational intelligence: the Fisher information as an objective function for self-limiting Hebbian learning rules. Front. Robot. AI 1:1. doi:10.3389/frobt.2014.00001

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Keywords: Hebbian learning, generating functionals, synaptic plasticity, objective functions, Fisher information, homeostatic adaption

Citation: Echeveste R and Gros C (2015) Corrigendum: Generating functionals for computational intelligence: the Fisher information as an objective function for self-limiting Hebbian learning rules. Front. Robot. AI 2:2. doi: 10.3389/frobt.2015.00002

Received: 16 January 2015; Accepted: 04 February 2015;
Published online: 19 February 2015.

Edited by:

Mikhail Prokopenko, The University of Sydney, Australia

Reviewed by:

Michael Harre, The University of Sydney, Australia
Rosalind Wang, CSIRO, Australia

Copyright: © 2015 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.

*Correspondence:Z3JvczA3QGl0cC51bmktZnJhbmtmdXJ0LmRl

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

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