Extended Data Fig. 3: Distinct contribution of noise from forward synapses and recurrent synapses to representational drift in the 1D place cell model.

To further verify the role of noise in feedforward synapses, we simulated models of representational drift in 1D place cells, and compared the correlation coefficient of population vectors of the principal output neurons in three different noise scenarios: full model with all synaptic noises (blue); noise only in the forward synapses \({{{\mathbf{W}}}}\) (\(\sigma _M = 0\), red); and noise only in recurrent synapses \({{{\mathbf{M}}}}\) (\(\sigma _W = 0\), gray). These models are further explored in main text Fig. 5 and Extended Data Fig. 4. In both the simplified 1D place cell model (a) and the more detailed network model with inhibitory neurons (b), noise in the forward matrix has much larger influence on the representational drift. For the network with inhibitory neurons, forward noise corresponds to all noises in matrices \({{{\mathbf{M}}}},{{{\mathbf{W}}}}^{EI},{{{\mathbf{W}}}}^{IE}\) are set to 0. Shading: mean ± SD, n = 200 output neurons. Parameters used are in Supplementary Table 1 of SI.