INFERNO: A Novel Architecture for Generating Long Neuronal Sequences with Spikes View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2017

AUTHORS

Alex Pitti , Philippe Gaussier , Mathias Quoy

ABSTRACT

Human working memory is capable to generate dynamically robust and flexible neuronal sequences for action planning, problem solving and decision making. However, current neurocomputational models of working memory find hard to achieve these capabilities since intrinsic noise is difficult to stabilize over time and destroys global synchrony. As part of the principle of free-energy minimization proposed by Karl Friston, we propose a novel neural architecture to optimize the free-energy inherent to spiking recurrent neural networks to regulate their activity. We show for the first time that it is possible to stabilize iteratively the long-range control of a recurrent spiking neurons network over long sequences. We identify our architecture as the working memory composed by the Basal Ganglia and the Intra-Parietal Lobe for action selection and we make some comparisons with other networks such as deep neural networks and neural Turing machines. We name our architecture INFERNO for Iterative Free-Energy Optimization for Recurrent Neural Network. abstract environment. More... »

PAGES

421-428

Book

TITLE

Advances in Neural Networks - ISNN 2017

ISBN

978-3-319-59071-4
978-3-319-59072-1

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-59072-1_50

DOI

http://dx.doi.org/10.1007/978-3-319-59072-1_50

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1086389855


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