Title
Learning Contrast-Invariant Cancellation of Redundant Signals in Neural Systems.
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.
Year
DOI
Venue
2013
10.1371/journal.pcbi.1003180
PLOS COMPUTATIONAL BIOLOGY
Keywords
Field
DocType
action potentials,electric fish
Signal processing,Bursting,Biological system,Biology,Invariant (physics),Computational model,Artificial intelligence,Invariant (mathematics),Parallel fiber,Genetics,Sensory system,Electric fish
Journal
Volume
Issue
Citations 
9
9
4
PageRank 
References 
Authors
0.43
8
5
Name
Order
Citations
PageRank
Jorge F. Mejías1385.30
Gary Marsat271.57
Kieran Bol371.23
Leonard Maler47811.44
André Longtin526047.87