Automatic Adaptation to Fast Input Changes in a Time-Invariant Neural Circuit.

ABSTRACT: Neurons must faithfully encode signals that can vary over many orders of magnitude despite having only limited dynamic ranges. For a correlated signal, this dynamic range constraint can be relieved by subtracting away components of the signal that can be predicted from the past, a strategy known as predictive coding, that relies on learning the input statistics. However, the statistics of input natural signals can also vary over very short time scales e.g., following saccades across a visual scene. To maintain a reduced transmission cost to signals with rapidly varying statistics, neuronal circuits implementing predictive coding must also rapidly adapt their properties. Experimentally, in different sensory modalities, sensory neurons have shown such adaptations within 100 ms of an input ch

SUBMITTER: Bharioke A 

PROVIDER: S-EPMC4527762 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature