The geometry of representational drift in natural and artificial neural networks
Fig 4
Behavioral data: Experimental setup and drift geometry.
(a) Summary of experimental setup. (b) Summary of session ordering, trial types, and extraction of response vectors from dF/F values. Bottom plot shows dF/F values over time, with colored columns representing image flashes where different colors are different images. [c-e] Various drift metrics of Hit trials and their dependence on PCi direction of the earlier session’s variational space. Dark colors correspond to drift between familiar sessions, while lighter colors are those between novel sessions. Metrics are plotted as a function of each PCi’s ratio of variance explained, vi. Colored curves are again linear regression fits. (c) Magnitude of drift along a given PCi direction, relative to full magnitude of drift. (d) Angle of drift with respect to PCi direction. (e) Post-drift variance explained along PCi direction (dotted line is equality). Linear regression fit to log(var. exp). [f-h] Various metrics as a function of session(s). Dark solid dots/lines show mean values with ± s.e. Light colored dots/lines show raw mice data. (f) Mean performance metric over engaged trails, d′ (Methods). (g) Angle between SVC normal vectors. (h) Cross classification accuracy, as a function of trained data session (Class.) and tested data session (Data). The “–” marker again shows average classification accuracy when SVCs are randomly rotated by same angle that separates respective sessions’ classifiers.