Fig. 8.

Filter bank approaches for CL and RL. a Schematic diagram of a filter bank implementation of ISO-learning. b Signal structure for signals u. c Serial compound representation: the simulated stimulus x(t) is split into traces u ^1…n₁(t) with only one non-zero value in time, which cover the duration until the reward is presented. Each of these traces has its own weight ω^1…in₁ which gets updated after each time step according to the delta error δ. d Using TD-learning the output is zero before learning, and thus the delta error δ peaks at the same time step the reward occurs. e After learning δ has moved forward in front of the reward, while the output υ arises with the stimulus and ends with the reward (Suri 2002; Wörgötter and Porr 2005)