Generalisation of structural knowledge in the hippocampal-entorhinal system
A central problem to understanding intelligence is the concept of generalisation. This allows previously learnt structure to be exploited to solve tasks in novel situations differing in their particularities. We take inspiration from neuroscience, specifically the hippocampal-entorhinal system known to be important for generalisation. We propose that to generalise structural knowledge, the representations of the structure of the world, i.e. how entities in the world relate to each other, need to be separated from representations of the entities themselves. We show, under these principles, artificial neural networks embedded with hierarchy and fast Hebbian memory, can learn the statistics of memories and generalise structural knowledge. Spatial neuronal representations mirroring those found in the brain emerge, suggesting spatial cognition is an instance of more general organising principles. We further unify many entorhinal cell types as basis functions for constructing transition graphs, and show these representations effectively utilise memories. We experimentally support model assumptions, showing a preserved relationship between entorhinal grid and hippocampal place cells across environments.
It is an interesting study that tackles one of very important questions in computational neuroscience - how generalisation across stimuli and environments is achieved. It is similar to the concept of schemas, which are thought to primarily rely on frontal cortical areas. In this particular case the focus is on entorhinal grid cells and hippocampal place cells, which authors assert code for the environment and conjunction of environment and stimulus respectively. The authors present a computational model that aims to address the question of whether place cells encode a conjunctive outcome of environment and stimulus representations. It is an interesting hypothesis, which if shown convincingly, would be a major breakthrough in neuroscience. However, this study suffers from lack of clarity in methodological descriptions as well as from seemingly arbitrary choices in analyses, which at least in the current version does not convincingly show that the stated hypothesis is supported by data. Both of these aspects should be improved for the paper to be acceptable to NIPS.  More specifically, - it is clear (and supported by data) that grid cells have different frequencies (that go along anatomical gradients); however, for place cells the meaning of frequency is more ambiguous - is there evidence of similar anatomical gradients (based on projections from EC) of place field radii, which may suggest the support of frequencies for place cells as well as the actual hypothesis of conjunctive coding - it's not exactly clear which exactly formulations and equations are used to simulate the model & learning - Bayesian, Hebbian or BPTT - the latter being considerably less biologically realistic. I would suggest to move theoretical background to the appendix and instead present model description and simulation clearly in the main text, including all steps and their justification (including of some seemingly arbitrary choices) - it's not clear how exactly grid cells (and place cells) result from model constraints (or are they more hand-designed?) - considering noisiness of biological place cells, it doesn't seem very reasonable to consider peak activation as their centre - wouldn't it be better to fit a Gaussian and consider its centre? - Why are trials 1 vs 4 selected to evaluate the match between different environments? To avoid bias, it would be a lot better to include all pairwise comparisons of (1,5) vs (2,3,4) - In presenting the "within environment" comparison it would be equally interesting to see if comparing 2 vs 3 vs 4 lead to similar results as 1 vs 5, and if not, why not? - Correlations between measures seem a bit too low to support the claim regarding the relationship between grid and place cells in remapping (even if they are statistically significant). Mindist measure seems considerably smaller between environments than within the same environment (even if marginally significant). - Statistics in 7b seem to indicate a considerable mismatch between the experiment and model prediction - Finally what is meant by "anonymous authors" for ref. 3? - There are also many typos that should be corrected.  --- I thank the authors for their rebuttal. As a result, paper clarity is going to be improved. However, I'm still not very convinced about biological plausibility of network structure and the way it's trained. Furthermore, some of my technical concerns (e.g. why 2-3-4 are not used for within environment comparison, as 1-5 are, or why novelty implies that only #4 should be used) have not been convincingly addressed either. Therefore, I do not change my score. I believe this story holds great potential but I'm not convinced yet.