Tuning positive feedback for signal detection in noisy dynamic environments.
Learning from previous actions is a key feature of decision-making. Diverse biological systems, from neuronal assemblies to insect societies, use a combination of positive feedback and forgetting of stored memories to process and respond to input signals. Here we look how these systems deal with a dynamic two-armed bandit problem of detecting a very weak signal in the presence of a high degree of noise. We show that by tuning the form of positive feedback and the decay rate to appropriate values, a single tracking variable can effectively detect dynamic inputs even in the presence of a large degree of noise. In particular, we show that when tuned appropriately a simple positive feedback algorithm is Fisher efficient, in that it can track changes in a signal on a time of order L(h)=(|h|/σ)(-2), where |h| is the magnitude of the signal and σ the magnitude of the noise.
Mathematics Department, Uppsala University, Uppsala, Sweden.
Anders
A
Johansson
Kai
K
Ramsch
Martin
M
Middendorf
David J T
DJ
Sumpter
D003198:Computer Simulation; D004777:Environment; D005246:Feedback; D017603:Signal Detection, Psychological