Studies on the neural correlates of navigation in 3D environments are plagued by several unresolved issues. For example, experimental studies show markedly different place cell responses in rats and bats, both navigating in 3D environments. In an effort to understand this divergence, we propose a deep autoencoder network to model the place cells and grid cells in a simulated agent navigating in a 3D environment. We also explore the possibility of a vital role that Head Direction (HD) tuning plays in determining the isotropic or anisotropic nature of the observed place fields in different species. 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 integrates velocity information into the phase of oscillating neural activity. The output of the PI layer is modulated and passed through a low pass filter to make it purely a function of space before passing it to an autoencoder. 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 in each. This model paves the way for a holistic approach of using deep neural networks to model spatial cells in 3D navigation.

Studies on the neural correlates of navigation in 3D environments are plagued by several unresolved issues. For example, experimental studies show markedly different place cell responses in rats and bats, both navigating in 3D environments. In an effort to understand this divergence, we propose a deep autoencoder network to model the place cells and grid cells in a simulated agent navigating in a 3D environment. We also explore the possibility of a vital role that Head Direction (HD) tuning plays in determining the isotropic or anisotropic nature of the observed place fields in different species. The input layer to the autoencoder network model is the HD layer which encodes the agents 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 integrates velocity information into the phase of oscillating neural activity. The output of the PI layer is modulated and passed through a low pass filter to make it purely a function of space before passing it to an autoencoder. 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 in each. This model paves the way for a holistic approach of using deep networks to model spatial cells in 3D navigation.