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Stochastic activity in low-rank recurrent neural networks

Fig 4

Rank-one RNN receiving high-dimensional stochastic inputs.

A–B–C. Example of a simulated network with . In A: covariance spectrum. In B: overlap between two principal components (the strongest and the weakest) estimated from simulated activity and the theoretically-estimated vectors and (top), or vectors m and n (bottom). Overlaps are quantified via Eq 4. In C: simulated activity projected on two different pairs of PCs. D–E–F. Same as in A–B–C, example with . Note that, although the qualitative behaviour of activity in the two examples is similar, activity in the example network in A–B–C is overall higher dimensional.

doi: https://doi.org/10.1371/journal.pcbi.1013371.g004