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Title: Optimal prediction with resource constraints using the information bottleneck
Responding to stimuli requires that organisms encode information about the external world. Not all parts of the input are important for behavior, and resource limitations demand that signals be compressed. Prediction of the future input is widely beneficial in many biological systems. We compute the trade-offs between representing the past faithfully and predicting the future using the information bottleneck approach, for input dynamics with different levels of complexity. For motion prediction, we show that, depending on the parameters in the input dynamics, velocity or position information is more useful for accurate prediction. We show which motion representations are easiest to re-use for accurate prediction in other motion contexts, and identify and quantify those with the highest transferability. For non-Markovian dynamics, we explore the role of long-term memory in shaping the internal representation. Lastly, we show that prediction in evolutionary population dynamics is linked to clustering allele frequencies into non-overlapping memories.  more » « less
Award ID(s):
1734030 1652617
PAR ID:
10230569
Author(s) / Creator(s):
; ; ;
Editor(s):
Faisal, Aldo A
Date Published:
Journal Name:
PLOS Computational Biology
Volume:
17
Issue:
3
ISSN:
1553-7358
Page Range / eLocation ID:
e1008743
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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