RNN model architecture.

Abstract

Shown is the architecture of the 7-hidden-unit RNNs. (A) One-hot vectors corresponding to each of the 6 stimulus types are fed into the input layer, which projects to an LSTM layer with 7 hidden units. This hidden layer in turn projects to an output unit with a binary target activation (0 = non-match, 1 = match). (B) Example input and target output sequences. Two delay timesteps were installed after each stimulus presentation timestep to emulate the delay period in the 2-back EEG task. 60-hidden-unit RNNs have the same architecture except that they have 60 LSTM hidden units, and two input units that take a vector [cos 2θ, sin 2θ] (θ denoting the angle of grating orientations used in Wan et al. [13]) instead of a one-hot vector.</p