The geometry of representational drift in natural and artificial neural networks
Fig 6
Artificial neural networks: Additional properties of drift geometry.
[a-d] Various quantities as a function of relative/absolute training time (in epochs). Means are shown as dark lines, with 95% confidence intervals shaded behind. Raw data is scattered behind. (a) Angle between SVC classifiers (normal vectors) as a function of the time difference. The grey dashed line is the average for the novel Hit data shown in Fig 4g. (b) Cross classification accuracy as a function of time difference between classifier training time (Class.) and testing time (Data). (c) Difference in angle of a stimulus group’s readout as a function of the time difference. Note the different vertical scale from (a). (d) Deviation of the angle between a stimulus group’s drift and the respective readout from perpendicular (i.e. 90 degrees). The dashed green line is the average across time. The dotted black line is the angle between two randomly drawn vectors in a feature space of the same dimension. (e) Fits of variance explained versus angle of drift with respect to PC direction for regular node dropout (purple), targeted maximum variance node dropout (pink), and targeted minimum variance node dropout (yellow). The inset shows the r-values of the respective fits. (f) Difference in response vector angle as a function of Δt. The dashed vertical line indicates the time scale on which the node dropouts are updated (1/epoch).