In Algorithm 1 we present the memorizing and recalling procedures of the single-layer tPC.Algorithm 1 Memorizing and recalling with single-layer tPC Here we present the proof for Property 1 in the main text, that the single-layer tPC can be viewed as a "whitened" version of the AHN. When applied to the data sequence, it whitens the data such that (i.e., Eq.16 in the main text): These observations are consistent with our numerical results shown in Figure 1. MCAHN has a much larger MSE than that of the tPC because of the entirely wrong recalls. In Figure 1 we also present the online recall results of the models in MovingMNIST, CIFAR10 and UCF101. In Fig 4 we show a natural example of aliased sequences where a movie of a human doing push-ups is memorized and recalled by the model.

However, none of these models were examined in memory tasks.

Further experimental details are described in Ref. [4]. Each spatiotemporal stimulus spanned over 400 ms corresponding to the retinal integration timescale. Figure 1: (a) The log-likelihood of empirical data for each PMF averaged over cells. Black line is the identity line. The central 20 20 arrays are shown.