Simple computational models, using reverse correlations (e.g., Berry et al., 1997; deCharms et al., 1998), can illustrate the pattern classification abilities of reader/actuator mechanisms (Rumelhart and Zipser, 1986). Various population patterns generated by a network of model neurons can evoke spiking responses in one or just a few reader cells. A given reader neuron or assembly of readers can respond to a random pattern of neuronal discharge in the input layer and during the learning process it becomes selective to it and only to it. Thus, only a specific pattern becomes meaningful to this reader. To provide biological meaning to a second pattern, another reader, selectively tuned to the second pattern, is needed (Fig. 1C). Learning to discriminate numerous patterns requires increasing numbers of selective readers (Masquelier et al., 2009). For example, the 50,000 reader KCs in the mushroom body, in principal, can respond to 50,000 odorant combinations (Fig. 3A; Jortner et al., 2007; Perez-Orive et al., 2002). Discriminating between two trajectories (assembly sequences) of hippocampal or prefrontal neurons by downstream readers, corresponding to two different choices, is a relatively
