In our framework, the input is often very noisy, since it corresponds to the converging points of different learning traces. In this section we describe two linear decoders that differ from that in [35] and are more noise-resilient. A.5 is (see [37] for a derivation): p See the T emporal resolutionparagraph below for more details on the discretization of time. A.3 does not impose any explicit constraint on the's in the input vector are A.9 and A.10 is crucial for long temporal horizons, since regularization causes the overall magnitude of the recovered A.3 over the same timesteps as defined by the MP, which provides a direct approximation to the (regularized) Z-transform until a temporal horizon We found this method to be very susceptible to input noise. Figure A.2: The weights of the decoder are trained to minimize the quadratic error between the The decoding method is schematized in Fig. A.2. 's.

These results naturally pose the question of how such distributional estimators are learned and represented by neural systems.

These results naturally pose the question of how such distributional estimators are learned and represented by neural systems.

Clarity: This is my biggest issue with this paper: it is _very_ difficult to follow and most of the figures are difficult to interpret. In more detail: - Overall, there are too many references to the supplemental material (e.g. "see SM-C") for things that are necessary for understanding the main paper. What do the bar plots on top of the grid represent? What are the dark and grey lines on the right plot meant to represent?