Impact of symmetry in local learning rules on predictive neural representations and generalization in spatial navigation
Fig 4
Symmetric learning rule leads to more stable place fields in linear track.
We simulated an experiment with a rat repeatedly running on a linear track, similar to [37]. A two-layer SR network was used where the recurrent weights had a symmetric (A, middle row) or asymmetric (A, top row) learning rule. In the the symmetric case, there is less shift of the centre of mass of place fields in the modelled CA3 population (red) than in the CA1 population (blue), which is not the case in the asymmetric version. Histograms show distribution of shifts comparing last five laps versus first five laps, while the rightmost plot shows shift relative to the 12-th lap. The results in the symmetric case are qualitatively similar to data (A, bottom row) from Ca2 + recordings of hippocampal neurons in a similar experiment - figure adapted from Dong, C., Madar, A. D., & Sheffield, M. E. (2021)([37]). In B, we show firing rates of an exemplar cell from CA3 and CA1 respectively, where the symmetric learning rule is used for CA3. The firing rates in each position are averaged over the first and last five laps, and plotted relative to the centre of mass in the first laps. With experience, only the place field in CA1, not the place field in CA3 shifts backwards (arrow indicates direction of travel).