# Modeling hippocampal spatial cells in rodents navigating in 3D environments

## TL;DR

This paper introduces a deep learning model to simulate spatial cell activity in rodents navigating 3D environments.

## Contribution

The novel contribution is a deep autoencoder network that models place and grid cells in 3D navigation.

## Key findings

- The model's bottleneck layer encodes spatial cell-like responses similar to place and grid cells.
- The model was validated in two simulated 3D environments.
- The study demonstrates how deep networks can model spatial navigation in 3D.

## Abstract

Studies on the neural correlates of navigation in 3D environments are plagued by several issues that need to be solved. For example, experimental studies show markedly different place cell responses in rats and bats, both navigating in 3D environments. In this study, we focus on modelling the spatial cells in rodents in a 3D environment. We propose a deep autoencoder network to model the place and grid cells in a simulated agent navigating in a 3D environment. The input layer to the autoencoder network model is the HD layer, which encodes the agent’s HD in terms of azimuth (θ) and pitch angles (ϕ). The output of this layer is given as input to the Path Integration (PI) layer, which computes displacement in all the preferred directions. The bottleneck layer of the autoencoder model encodes the spatial cell-like responses. Both grid cell and place cell-like responses are observed. The proposed model is verified using two experimental studies with two 3D environments. This model paves the way for a holistic approach using deep neural networks to model spatial cells in 3D navigation.