Evidence Accumulation and Change Rate Inference in Dynamic Environments.
In a constantly changing world, animals must account for environmental volatility when making decisions. To appropriately discount older, irrelevant information, they need to learn the rate at which the environment changes. We develop an ideal observer model capable of inferring the present state of the environment along with its rate of change. Key to this computation is an update of the posterior probability of all possible change point counts. This computation can be challenging, as the number of possibilities grows rapidly with time. However, we show how the computations can be simplified in the continuum limit by a moment closure approximation. The resulting low-dimensional system can be used to infer the environmental state and change rate with accuracy comparable to the ideal observer. The approximate computations can be performed by a neural network model via a rate-correlation-based plasticity rule. We thus show how optimal observers accumulate evidence in changing environments and map this computation to reduced models that perform inference using plausible neural mechanisms.
Department of Mathematics, University of Houston, Houston, TX 77204, U.S.A. adrian@math.uh.edu.
Adrian E
AE
Radillo
Department of Mathematics, University of Dayton, Dayton, OH 45469, U.S.A. avelizcuba1@udayton.edu.
Alan
A
Veliz-Cuba
Department of Mathematics, University of Houston, Houston, TX 77204, U.S.A.; Department of Biology and Biochemistry, University of Houston, Houston, TX 77204, U.S.A.; and Department of BioSciences, Rice University, Houston, TX 77251, U.S.A. josic@math.uh.edu.
Krešimir
K
Josić
Department of Mathematics, University of Houston, Houston, TX 77204; Department of Applied Mathematics, University of Colorado, Boulder, CO 80309, U.S.A.; and Department of Physiology and Biophysics, University of Colorado School of Medicine, Aurora, CO 80045, U.S.A. zpkilpat@colorado.edu.
Zachary P
ZP
Kilpatrick