(a) Schematic of an animal running left-to-right along a track. 50 cells phase precess, generating theta sweeps (e.g. grey box) that compress spatial behaviour into theta timescales (10 Hz). (b) We …
(a) Agents traversed a 5 m circular track in one direction (left-to-right) with 50 evenly distributed CA3 spatial basis features (example thresholded Gaussian place field shown in blue, radius …
(a) Figure 2 panels a-e have been repeated (additional 30 min simulation carried out) for ease of comparison. (b) We repeat the experiment with non-uniform running speed. Here, running seed is …
In the original model weights are set to the identity before learning and kept (‘anchored’) there, only updated on aggregate after learning. In these panels, we explore variations to this set-up. (a)…
(a) A table showing all parameters used in this paper and the ranges over which the hyperparameter sweep was performed. For each parameter setting, we estimate performance metrics to judge whether …
(a) We model phase precession as a von Mises centred at a preferred theta phase which precesses in time. This factor modulates the spatial firing field. It is parameterised by (von Mises width …
(a) In the loop maze (motion left-to-right), STDP place cells skew and shift backwards, and strongly resemble place cells obtained via temporal difference learning. This is not the case when theta …
(a) An agent explores a 1D loop maze with 150 places cells of different sizes (50 small, 50 medium, and 50 large) evenly distributed along the track. (b) In rodent hippocampus, place cells are …
Top, model schematic – each CA1 neuron receives input from multiple CA3 neurons with contiguous place fields. Bottom, position vs phase plot for an indicative CA1 neuron, showing strong phase …