Figure 5. Multiple feedforward pat terns are embedded via uniform path lengths between units.

A. Average disynaptic efficiency between coactive (unshaded) and non-coactive (shaded) unit pairs in an RNN trained for a single feedforward target. Units that are coactive during a trajectory have a higher mean efficiency compared to non-coactive pairs. Efficiency values were normalized to the mean to aid visualization. B. Same as A but showing the same initial network trained such that more units are coactive at a given time (lower temporal sparsity). The efficiency across the network is more uniform. C. Same as in A now trained for 10 targets. In A–C, postsynaptic efficiency values were aligned according to the activation order of the presynaptic unit within the same target order and averaged across units. D. Average efficiency of connections between coactive and non-coactive units according to the number of trained trajectories. Efficiency increases sharply from the naive weights. As more trajectories are encoded, efficiency becomes uniform across the network, i.e. the difference between coactive and non-coactive units decreases.