The geometry of representational drift in natural and artificial neural networks
Fig 2
Passive data: Feature space, drift, and drift’s dependence on time.
[a-d] Data from an exemplar mouse. (a) Response vectors (dots), mean response vectors (X’s), and first two PC dimensions (lines, scaled by variance explained), for two stimulus groups in a given session. Plotted in stimulus group 1’s PC space. (b) Same as previous subplot, but response vectors of stimulus group 1 across two different sessions, plotted in session 1’s PC space. (c) Pairwise angle between the response vectors of the 30 1-second time-blocks across the first five movie repeats of a single session. (d) Pairwise angle between mean response vectors across the three different sessions, same color scale as (c) (Methods). [e-h] Various metrics as a function of the time between earlier and later session, Δt, for all mice. All metrics are computed for each individual stimulus group, then averaged across all 30 groups. Colored curves are linear regression fits and shaded regions represent all fits within 95% confidence intervals of slope and intercept. (e) Average angle between mean response vectors. (f) Average (L2) magnitude of drift relative to magnitude of earlier session’s mean response vector. (g) Average change in variational space dimension, D, from later session to earlier session. (h) Average drift magnitude within earlier session’s variational space, ratio relative to full drift magnitude, see Eq (9). In yellow, the same metric if drift were randomly oriented in neural state space (mean ± s.e., across mice).