Stereotypical actions can be generated by relatively simple feed-forward excitatory mechanisms (such as a ‘synfire’ chain; Abeles 1991; Hahnloser et al., 2002; Sompolinsky and Kanter 1986;). However, generating multiple neuronal trajectories (i.e., neural sentence structures) serving different action sequences requires more sophisticated solutions. For example, nightingales or marsh warblers can sing dozens of unique songs. In this more complex case, the activation probability of a given assembly in the network likely depends not only on the immediately preceding but also on the previous sequence of a few (or several) assemblies. In strongly recurrently connected systems of large size, equipped with appropriate syntactical rules, very large numbers of trajectories (neural sentences) can be generated. In such model systems the evolution of the assembly sequences (i.e., the uniquely different neural sentences) can be described by a transition rule where the future sequence is probabilistically defined by the previous ordering of assemblies (Jin 2009; Rabinovich et al., 2008a, b; Sakata and Brainard, 2006; Woolley and Rubel, 1997). Indeed, the ability of the brain to sweep through sequences of neuronal assemblies is expected to support our ability to reminisce, think, reason and plan ahead.
