Training Excitatory-Inhibitory Recurrent Neural Networks for Cognitive Tasks: A Simple and Flexible Framework
Fig 1
Recurrent neural network (RNN).
A trained RNN of excitatory and inhibitory rate units r(t) receives time-varying inputs u(t) and produces the desired time-varying outputs z(t). Inputs encode task-relevant sensory information or internal rules, while outputs indicate a decision in the form of an abstract decision variable, probability distribution, or direct motor output. Only the recurrent units have their own dynamics: inputs are considered to be given and the outputs are read out from the recurrent units. Each unit of an RNN can be interpreted as the temporally smoothed firing rate of a single neuron or the spatial average of a group of similarly tuned neurons.