To date, very little experimental evidence is available regarding the exact mechanisms that allow readers to segregate complex trajectories (MacLeod et al., 1998). In the simplest case, a particular temporal pattern of neurons converges on a given reader neuron due to the hard-wired features of a circuit. This simple but non-realistic example assumes that the readers are in a constant ‘alert’ state, ready to integrate. In a more realistic situation, the readers may be influenced by other inputs as well (e.g., subcortical neuromodulators); therefore their pattern segregating may be strongly influenced by the state of the neural network. To forge a special relationship between readers and their assemblies, words or sentences, further learning or selection rules, which can bring about long-term modification of the relationship between neurons, may be needed. Synaptic plasticity, particularly spike timing-dependent plasticity (Levy and Steward, 1979; 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,
