Topological schemas of cognitive maps and spatial learning in the hippocampus

TL;DR

This paper introduces a computational framework for understanding how the hippocampus encodes spatial environments through topological schemas, revealing that these schemas are learned faster than neural network training.

Contribution

It proposes a novel schema-based approach to model hippocampal spatial maps, emphasizing topological relations and their rapid learning compared to neural networks.

Findings

Schemas encode large-scale environmental features

Schemas are learned faster than neural network training

Different topological schemas provide diverse environmental insights

Abstract

Spatial navigation in mammals is based on building a mental representation of their environment---a cognitive map. However, both the nature of this cognitive map and its underpinning in neural structures and activity remains vague. A key difficulty is that these maps are collective, emergent phenomena that cannot be reduced to a simple combination of inputs provided by individual neurons. In this paper we suggest computational frameworks for integrating the spiking signals of individual cells into a spatial map, which we call schemas. We provide examples of four schemas defined by different types of topological relations that may be neurophysiologically encoded in the brain and demonstrate that each schema provides its own large-scale characteristics of the environment---the schema integrals. Moreover, we find that, in all cases, these integrals are learned at a rate which is faster than the rate of complete training of neural networks. Thus, the proposed schema framework differentiates between the cognitive aspect of spatial learning and the physiological aspect at the neural network level.