Extended Data Fig. 6: Representational drift in a modified 1D place cell model with alternating learning and forgetting periods.

We introduced a forgetting time scale (1/η_forget) to our learning rules. The model is described in detail in SI Section 4. (a) 100 synaptic updates (shaded region) are sequentially followed by a forgetting period with 500 synaptic updates. Including a slower forgetting time scale significantly enhances the stability of learned representation as quantified by the similarity matrix alignment (RSA), defined in equation (41) of SI (upper). The representational similarity matrices \({{{\mathbf{Y}}}}^ \top {{{\mathbf{Y}}}}\) after the last forgetting period for three different forgetting time scales (lower). (b) Place fields of 3 exemplar output neurons in the presence of input and synaptic noise. Time starts from when the system has fully learned the representation. (c) Even with slow forgetting time scale, the representation still drifts during ‘experiment’ sessions as shown by the decay of coefficients of population vectors across learning sessions (shaded regions in (a)). Parameters are listed in Supplementary Table 1 of SI. Shading: mean ± SD, n = 200 output neurons. In (a) and (b), \(\eta _{forget} = 10^{ - 3}\).