Regulation of specialists and generalists by neural variability improves pattern recognition performance
EntityUAM. Departamento de Ingeniería Informática
10.1016/j.neucom.2014.09.073Neurocomputing 151.P1 (2015): 69-77
Funded byThis work was supported by the Spanish Government project TIN2010-19607 and predoctoral research grant BES-2011- 049274. R.H. acknowledges partial support by NIDCDR01DC011422- 01.
ProjectGobierno de España. TIN2010-19607
SubjectsArtificial neural networks; Neural threshold; Neural variability; Heterogeneity; Homogeneity; Olfactory system; Pattern recognition; Generalist neuron; Specialist neuron; Odor learning; Odor classification; Odor discrimination; Gain control; Unsupervised learning; Neural sparseness condition; Informática
NoteThis is the author’s version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neurocomputing, VOL 151, Part 1, (2015), DOI 10.1016/j.neucom.2014.09.073
Rights© 2015 Elsevier B.V. All rights reserved
Esta obra está bajo una licencia de Creative Commons Reconocimiento-NoComercial-SinObraDerivada 4.0 Internacional.
To analyze the impact of neural threshold variability in the mushroom body (MB) for pattern recognition, we used a computational model based on the olfactory system of insects. This model is a single-hidden-layer neural network (SLN) where the input layer represents the antennal lobe (AL). The remaining layers are in the MBs that are formed by the Kenyon cell (KC) layer and the output neurons that are responsible for odor learning. The binary code obtained for each odorant in the output layer by unsupervised learning was used to measure the classification error. This classification error allows us to identify the neural variability paradigm that achieves a better odor classification. The neural variability is provided by the neural threshold of activation. We compare two hypotheses: a unique threshold for all the neurons in the MB layer, which leads to no variability (homogeneity), and different thresholds for each MB layer (heterogeneity). The results show that, when there is threshold variability, odor classification performance improves. Neural variability induces populations of neurons that are specialists and generalists. Specialist neurons respond to fewer stimulus than the generalists. The proper combination of these two neuron types leads to performance improvement in the bioinspired classifier.
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