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Found 10 papers, 4 papers with code
Emergence of Grid-like Representations by Training Recurrent Networks with Conformal Normalization
As the agent moves, the vector is transformed by an RNN that takes the velocity of the agent as input.
Conformal Isometry of Lie Group Representation in Recurrent Network of Grid Cells
Recurrent neural networks have been proposed to explain the properties of the grid cells by updating the neural activity vector based on the velocity input of the animal.
Neural tuning and representational geometry
A central goal of neuroscience is to understand the representations formed by brain activity patterns and their connection to behavior.
Learning identifiable and interpretable latent models of high-dimensional neural activity using pi-VAE
Specifically, we propose to construct latent variable models of neural activity while simultaneously modeling the relation between the latent and task variables (non-neural variables, e. g. sensory, motor, and other externally observable states).
A Representational Model of Grid Cells' Path Integration Based on Matrix Lie Algebras
The grid cells in the mammalian medial entorhinal cortex exhibit striking hexagon firing patterns when the agent navigates in the open field.
On Path Integration of Grid Cells: Group Representation and Isotropic Scaling
In this paper, we conduct theoretical analysis of a general representation model of path integration by grid cells, where the 2D self-position is encoded as a higher dimensional vector, and the 2D self-motion is represented by a general transformation of the vector.
A zero-inflated gamma model for deconvolved calcium imaging traces
However, statistical modeling of deconvolved calcium signals (i. e., the estimated activity extracted by a pre-processing pipeline) is just as critical for interpreting calcium measurements, and for incorporating these observations into downstream probabilistic encoding and decoding models.
Emergence of functional and structural properties of the head direction system by optimization of recurrent neural networks
Overall, our results show that optimization of RNNs in a goal-driven task can recapitulate the structure and function of biological circuits, suggesting that artificial neural networks can be used to study the brain at the level of both neural activity and anatomical organization.
Emergence of grid-like representations by training recurrent neural networks to perform spatial localization
As a new way to understand these neural representations, we trained recurrent neural networks (RNNs) to perform navigation tasks in 2D arenas based on velocity inputs.
Efficient Neural Codes under Metabolic Constraints
The advantage could be as large as one-fold, substantially larger than the previous estimation.
