Adaptive coding for dynamic sensory inference.

Abstract:

:Behavior relies on the ability of sensory systems to infer properties of the environment from incoming stimuli. The accuracy of inference depends on the fidelity with which behaviorally relevant properties of stimuli are encoded in neural responses. High-fidelity encodings can be metabolically costly, but low-fidelity encodings can cause errors in inference. Here, we discuss general principles that underlie the tradeoff between encoding cost and inference error. We then derive adaptive encoding schemes that dynamically navigate this tradeoff. These optimal encodings tend to increase the fidelity of the neural representation following a change in the stimulus distribution, and reduce fidelity for stimuli that originate from a known distribution. We predict dynamical signatures of such encoding schemes and demonstrate how known phenomena, such as burst coding and firing rate adaptation, can be understood as hallmarks of optimal coding for accurate inference.

journal_name

Elife

journal_title

eLife

authors

Młynarski WF,Hermundstad AM

doi

10.7554/eLife.32055

subject

Has Abstract

pub_date

2018-07-10 00:00:00

issn

2050-084X

pii

32055

journal_volume

7

pub_type

杂志文章

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