Fig. 4. Potential architectures for a flexible temporal basis.

Three example networks that could implement a FLEX theory. Top, network schematic. Middle, the activity of one example neuron in the network. Bottom, network activity before and after training. Each network initially only has transient responses to stimuli, modifying plastic connections (in blue) during association to develop a specific temporal basis to reward-predictive stimuli. a Feed-orward neural sequences or “chains” could support a FLEX model, if the chain could recruit more members during learning, exclusive to reward-predictive stimuli. b A population of neurons with homogenous recurrent connections have a characteristic decay time that is related to the strength of the weights. The cue-relative time can then be read out by the mean level of activity in the network. c A population of neurons with heterogenous and large recurrent connections (liquid state machine) can represent cue-relative time by treating the activity vector at time t as the “microstate” representing time t (as opposed to the homogenous case, where only mean activity is used).