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The importance of recurrent top-down synaptic connections for the anticipation of dynamic emotions
Mermillod, Martial Bourrier, Yannick David, Erwan Kauffmann, Louise Chauvin, Alan Guyader, Nathalie Dutheil, Frederic Peyrin, Carole
Mermillod, Martial
Bourrier, Yannick
David, Erwan
Kauffmann, Louise
Chauvin, Alan
Guyader, Nathalie
Dutheil, Frederic
Peyrin, Carole
Abstract
Different studies have shown the efficiency of a feed-forward neural network in categorizing basic emotional facial expressions. However, recent findings in psychology and cognitive neuroscience suggest that visual recognition is not a pure bottom-up process but likely involves top-down recurrent connectivity. In the present computational study, we compared the performances of a pure bottom-up neural network (a standard multi-layer perceptron, MLP) with a neural network involving recurrent top-down connections (a simple recurrent network, SRN) in the anticipation of emotional expressions. In two complementary simulations, results revealed that the SRN outperformed the MLP for ambiguous intensities in the temporal sequence, when the emotions were not fully depicted but when sufficient contextual information (related to previous time frames) was provided. Taken together, these results suggest that, despite the cost of recurrent connections in terms of energy and processing time for biological organisms, they can provide a substantial advantage for the fast recognition of uncertain visual signals.
Keywords
Predictive brain, Neural network modeling, Emotional facial expressions, Top-down processes
Date
2019
Type
Journal article
Journal
Neural Networks
Book
Volume
109
Issue
Page Range
19-30
Article Number
ACU Department
Collections
Relation URI
Source URL
Event URL
Open Access Status
License
File Access
Controlled