Document Type
Article
Publication Date
2013
College/Unit
Eberly College of Arts and Sciences
Department/Program/Center
Biology
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.
Digital Commons Citation
Mejias, Jorge F.; Marsat, Gary; Bol, Kieran; Maler, Leonard; and Longtin, Andre, "Learning Contrast-Invariant Cancellation of Redundant Signals in Neural Systems" (2013). Faculty & Staff Scholarship. 2633.
https://researchrepository.wvu.edu/faculty_publications/2633
Source Citation
Mejias JF, Marsat G, Bol K, Maler L, Longtin A (2013) Learning Contrast-Invariant Cancellation of Redundant Signals in Neural Systems. PLoS Comput Biol 9(9): e1003180. https://doi.org/10.1371/journal.pcbi.1003180
Comments
© 2013 Mejias et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.