Magee and Johnston, 1997; Markram et al., 2007), is often exploited in computational models to modify circuit connections. The learning process may be facilitated by some supervisory mechanism and/or feedback modifying mechanisms. Supervision can simply mean just a time constraint, such as oscillation-induced silencing of readers and their potential upstream assemblies or it can refer to other complex top-down effects, which a priori allow some combinations and disallow others. Alternatively, the reader’s ability to identify a unique upstream constellation of neuronal patterns can be strengthened by reinforcers (i.e. goals), which optimize the connectivity of the upstream assembly post-hoc so that it will activate the reader more effectively on future occasions (Izhikevich 2007; Legenstein and Maas, 2007; Maas et al., 2002; Seung, 2003). If multiple readers send their outputs to a downstream integrator/reader, the readers in the input layer become assembly partners from the perspective of the downstream reader (Figure 1C).18 In turn, the links between the ‘hidden layer’ readers (Rumelhart and Zipser, 1986) may be modified by any of the above mechanisms. Although neither the generality nor the biological viability of these hypothetical selection processes is firmly supported by physiological data, the reader-centric perspective of assembly organization provides a disciplined
