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Impact of symmetry in local learning rules on predictive neural representations and generalization in spatial navigation

Fig 3

Successor representations are learned for a variety of inputs, dynamics, and parameters.

Top: Convergence of recurrent (red) and feedforward (blue) matrices to their theoretical limit with random features in circular (left) and arbitrary (right) random walks. Bottom: Convergence of recurrent weight (left) and feedforward weight (right) for different parameters . The other set of parameters is fixed to (1,0) and in these experiments, respectively. In graphs, we measure convergence by the loss term as explained in Methods section. In the bottom row, we compute the fraction of the loss at the final step over the initial loss and display the result in a logscale. Thus, negative values indicate converging towards the target. Note that the values on the antidiagonal are approximately 0.

doi: https://doi.org/10.1371/journal.pcbi.1013056.g003