Neuronal implementation of cognitive maps for navigation

Introduction

Intelligent behavior is based on internal models of the world that enable mental simulations and strategic planning. A leading model to study such “cognitive maps” are networks of place cells and grid cells in the mammalian hippocampal-entorhinal system.

Place Cells and Grid Cells

These neurons are active at defined locations and collectively represent a physical environment as a low-dimensional topographic map in neural activity space. However, a mechanistic understanding of the underlying computations and their biological implementation remains elusive.

Research Methods

Using new methods for brain-wide activity imaging during behavior, members of our team discovered an abundance of place cells in the telencephalon of zebrafish. We will combine this approach with:

1. Volume electron microscopy to reconstruct large-scale connectivity in the same brains at synaptic resolution.
2. Transcriptomic profiling to identify molecular cell types, taking advantage of small brain size.

Analysis Goals

The joint analysis of network connectivity and population dynamics will allow us to determine how functional properties of place cells are established by interactions between specific subsets of neurons across brain areas. Additionally, we will investigate how network structure constrains population activity to topographically organized attractor manifolds.

Expected Outcomes

The results will disambiguate computational models that make conflicting assumptions about network connectivity. We will further explore the emergence of cell types during development and the concomitant structural and functional maturation of neuronal circuits.

Cognitive Maps and Neuronal Computations

The relevance of cognitive maps for neuronal computations and behaviors involving internally generated predictions will be explored by:

• Activity measurements in a virtual reality.
• Functional manipulations of genetically targeted neurons.

Conclusion

Capitalizing on the unique combination of expertise among team members, this project is expected to fundamentally advance our mechanistic understanding of biological and potentially artificial intelligence.