A recurrent neural network model of prefrontal brain activity during a working memory task
Fig 8
Neural geometry findings from the probabilistic cue paradigm.
A. Distribution of plane angles θ and phase-alignment angles ψ formed in the delay intervals for all trained networks. Values for the pre-cue, post-cue and post-probe delays shown as red circles, blue triangles and grey squares. Data from individual models shown in opaque, and grand averages in solid colours. Plots for models trained under trained under the deterministic (retrocue validity = 100%) and non-deterministic retrocue validity conditions (75% and 50%) ordered top to bottom rows, respectively. B. Colour discriminability index (CDI) for the pre-cue, post-cue and post- colour planes. Pre-cue values were averaged across both locations, whilst the post-cue values for the cued and uncued subspaces were averaged across valid and invalid trials. Bars correspond to the mean across trained network, error bars denote SEM. Panels correspond to results from models trained under 100%, 75% and 50% retrocue validity conditions, from left to right. C. Scatterplots showing the relationship between the normalised CDI benefit (y-axis, see main text) and the difference in mixture model parameter estimates on valid and invalid trials (x-axes). Panels correspond to the memory precision parameter K, probability of choosing the target item pT, probability of choosing the non-target item pNT, and probability of making random guesses pU, from left to right. Results of a Spearman correlation between the variables shown on the x- and y-axis shown on each panel. Lines of best fit plotted in red.