Fast and Robust Learning by Reinforcement Signals: Explorations in the Insect Brain

Huerta, Ramon and Nowotny, Thomas (2009) Fast and Robust Learning by Reinforcement Signals: Explorations in the Insect Brain. Neural Computation, 21 (8). pp. 2123-2151. ISSN 0899-7667

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Abstract

We propose a model for pattern recognition in the insect brain. Departing from a well-known body of knowledge about the insect brain, we investigate which of the potentially present features may be useful to learn input patterns rapidly and in a stable manner. The plasticity underlying pattern recognition is situated in the insect mushroom bodies and requires an error signal to associate the stimulus with a proper response. As a proof of concept, we used our model insect brain to classify the well-known MNIST database of handwritten digits, a popular benchmark for classifiers. We show that the structural organization of the insect brain appears to be suitable for both fast learning of new stimuli and reasonable performance in stationary conditions. Furthermore, it is extremely robust to damage to the brain structures involved in sensory processing. Finally, we suggest that spatiotemporal dynamics can improve the level of confidence in a classification decision. The proposed approach allows testing the effect of hypothesized mechanisms rather than speculating on their benefit for system performance or confidence in its responses.

Item Type: Article
Additional Information: Copyright notice: The copyright to this publication is held by The MIT Press.
Keywords: Classification; Olfaction; Insects; Handwritten digit recognition;
Schools and Departments: School of Engineering and Informatics > Informatics
Subjects: Q Science > QA Mathematics > QA0076 Computer software
Depositing User: Chris Keene
Date Deposited: 23 Jan 2012 10:19
Last Modified: 07 Mar 2017 10:40
URI: http://sro.sussex.ac.uk/id/eprint/7691
Google Scholar:7 Citations

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