Learning contrast-invariant cancellation of redundant signals in neural systems

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Title: Learning contrast-invariant cancellation of redundant signals in neural systems
Authors: Mejias, Jorge F
Marsat, Gary
Bol, Kieran
Maler, Leonard
Longtin, André
Date: 2013
Abstract: Cancellation of redundant information is a highly desirable feature of sensory systems, since it would potentially lead to a more efficient detection of novel information. However, biologically plausible mechanisms responsible for such selective cancellation, and especially those robust to realistic variations in the intensity of the redundant signals, are mostly unknown. In this work, we study, via in vivo experimental recordings and computational models, the behavior of a cerebellar-like circuit in the weakly electric fish which is known to perform cancellation of redundant stimuli. We experimentally observe contrast invariance in the cancellation of spatially and temporally redundant stimuli in such a system. Our model, which incorporates heterogeneously-delayed feedback, bursting dynamics and burst-induced STDP, is in agreement with our in vivo observations. In addition, the model gives insight on the activity of granule cells and parallel fibers involved in the feedback pathway, and provides a strong prediction on the parallel fiber potentiation time scale. Finally, our model predicts the existence of an optimal learning contrast around 15% contrast levels, which are commonly experienced by interacting fish.
URL: http://hdl.handle.net/10393/31673
http://www.ploscompbiol.org/article/fetchObject.action?uri=info%3Adoi%2F10.1371%2Fjournal.pcbi.1003180&representation=PDF
DOI: 10.1371/journal.pcbi.1003180
CollectionLibre accès uOttawa - Publications // uOttawa Open Access - Publications
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