Fig 2. Sketch of the network structure.

A) The architecture constitutes a recurrent network of excitatory and inhibitory neurons. Excitatory neurons are stimulated by external sources providing sequence-element specific inputs “A”,“D”, etc. The excitatory neuron population is composed of subpopulations containing neurons with identical stimulus preference (gray circles). Connections between and within the excitatory subpopulations are random and sparse. Inhibitory neurons are mutually unconnected. Each neuron in the inhibitory population is recurrently connected to a specific subpopulation of excitatory neurons. B) Initial connectivity matrix for excitatory connections to excitatory neurons (EE connections). Target and source neurons are grouped into stimulus-specific subpopulations (“A”,…,“F”). Before learning, the excitatory neurons are sparsely and randomly connected via immature synapses (light gray dots). C) During learning, sequence specific, sparsely connected subnetworks with mature synapses are formed (light blue arrows: {A, D, B, E}, dark blue arrows: {F, D, B, C}). D) EE connectivity matrix after learning. During the learning process, subsets of connections between subpopulations corresponding to subsequent sequence elements become mature and effective (light and dark blue dots). Mature connections are context specific (see distinct connectivity between subpopulations “D” and “B” corresponding to different sequences), thereby providing the backbone for a reliable propagation of sequence-specific activity. In panels B and D, only 5% of sequence non-specific EE connections are shown for clarity. Dark gray dots in panel D correspond to mature connections between neurons that remain silent after learning. For details on the network structure, see Tables 1 and 2.