A special class of neurons in the hippocampal formation broadly known as the spatial cells, whose subcategories include place cells, grid cells, and head direction cells, are considered to be the building blocks of the brain's map of the spatial world. We present a general, deep learning‐based modeling framework that describes the emergence of the spatial‐cell responses and can also explain responses that involve a combination of path integration and vision. The first layer of the model consists of head direction (HD) cells that code for the preferred direction of the agent. The second layer is the path integration (PI) layer with oscillatory neurons: displacement of the agent in a given direction modulates the frequency of these oscillators. Principal component analysis (PCA) of the PI‐cell responses showed the emergence of cells with grid‐like spatial periodicity. We show that the Bessel functions could describe the response of these cells. The output of the PI layer is used to train a stack of autoencoders. Neurons of both the layers exhibit responses resembling grid cells and place cells. The paper concludes by suggesting the wider applicability of the proposed modeling framework beyond the two simulated studies.

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A special class of hippocampal neurons broadly known as the spatial cells, whose subcategories include place cells, grid cells and head direction cells, are considered to be the building blocks of the brain’s map of the spatial world. We present a general, deep learning-based modeling framework that describes the emergence of the spatial cell responses and can also explain behavioral responses that involve a combination of path integration and vision. The first layer of the model consists of Head Direction (HD) cells that code for preferred direction of the agent. The second layer is the path integration (PI) layer with oscillatory neurons: displacement of the agent in a given direction modulates the frequency of these oscillators. Principal Component Analysis (PCA) of the PI cell responses showed emergence of cells with grid-like spatial periodicity. We show that the response of these cells could be described by Bessel functions. The output of PI layer is used to train stack of autoencoders. Neurons of both the layers exhibit responses resembling grid cells and place cells. The paper concludes by suggesting a wider applicability of the proposed modeling framework beyond the two simulated behavioral studies.

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.