Determinantal point process attention over grid cell code supports out of distribution generalization.

ABSTRACT: Deep neural networks have made tremendous gains in emulating human-like intelligence, and have been used increasingly as ways of understanding how the brain may solve the complex computational problems on which this relies. However, these still fall short of, and therefore fail to provide insight into how the brain supports strong forms of generalization of which humans are capable. One such case is out-of-distribution (OOD) generalization - successful performance on test examples that lie outside the distribution of the training set. Here, we identify properties of processing in the brain that may contribute to this ability. We describe a two-part algorithm that draws on specific features of neural computation to achieve OOD generalization, and provide a proof of concept by evaluating perf

SUBMITTER: Mondal SS 

PROVIDER: S-EPMC11293867 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature