A recurrent neural network model of prefrontal brain activity during a working memory task
Fig 2
The geometry of cued mnemonic representations learnt by the RNNs.
A. Visualisation of the pre- (left) and post-cue (right) geometry of cued items reported in [10]. Population responses from LPFC binned into 4 colour categories (denoted by marker colour) for each stimulus location (upper, lower–denoted by marker shape). Data plotted in reduced dimensionality space, defined by the first 3 principal components (PCs) of the 8 location-colour pairs. Planes of best fit for each location shown as grey quadrilaterals. B. Analogous visualisation of the hidden activity patterns from two example RNN models for the pre-cue (left) and post-cue (right) delay. Two locations (L1, L2) correspond to triangles and squares, respectively. The percentage of total variance explained by each PC axis shown in square brackets. Chosen models (in this and subsequent figures) correspond to the ones with geometries qualitatively most and least similar to the group-level average (in this case, the average post-cue geometry). Note that the principal components were calculated separately for each delay (and model), thus the PC axes shown e.g. in the upper left (example model 1, pre-cue) panel are not the same as the ones shown in the upper right (example model 1, post-cue) panel. C.-D. Between-plane angles θ for the pre- (red dots) and post-cue delay (navy triangles) timepoints, respectively. Lighter and darker colours correspond to the values for individual models and grand averages, respectively. E. Phase alignment angles ψ between the cued planes in the post-cue delay. F. Proportion of total variance explained (PVE) by the first 3 PCs for the PCA models fit to activation patterns from individual networks. Values for individual models shown in lighter, and grand averages in darker colours. G. Subspace alignment index (AI) between location-specific planes during the pre- (red) and post-cue (navy) delay intervals. Individual models shown as points, bars correspond to the grand averages. AI reported for 2- and 3-dimensional subspaces H. AI for the unrotated (light grey) and rotated (dark grey) subspaces–for description, refer to the main text. Statistically significant contrasts denoted by asterisks (*: p < .05, **: p < .01, ***: p < .001).