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Found 7 papers, 2 papers with code
Disentanglement via Latent Quantization
In this work, we construct an inductive bias towards encoding to and decoding from an organized latent space.
Actionable Neural Representations: Grid Cells from Minimal Constraints
We suggest the brain must represent this consistent meaning of actions across space, as it allows you to find new short-cuts and navigate in unfamiliar settings.
Disentanglement with Biological Constraints: A Theory of Functional Cell Types
Neurons in the brain are often finely tuned for specific task variables.
How to build a cognitive map: insights from models of the hippocampal formation
Learning and interpreting the structure of the environment is an innate feature of biological systems, and is integral to guiding flexible behaviours for evolutionary viability.
Relating transformers to models and neural representations of the hippocampal formation
Many deep neural network architectures loosely based on brain networks have recently been shown to replicate neural firing patterns observed in the brain.
Constellation: Learning relational abstractions over objects for compositional imagination
Learning structured representations of visual scenes is currently a major bottleneck to bridging perception with reasoning.
Generalisation of structural knowledge in the hippocampal-entorhinal system
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.
