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Corrigendum: generating functionals for computational intelligence: the fisher information as an objective function for self-limiting hebbian learning rules

Subject: Health & Medicine

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Published: December 15, 2021
Updated: December 15, 2021
University / College: University of South Carolina
Language: English
Downloads: 33

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 presynapticywas 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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