Biology subjects

Pettersen, M. B.

Publications and source records attributed to Pettersen, M. B..

3 recordsLinked to original sources

bioRxiv2024

Decoding the Cognitive map: Learning place cells and remapping

Hippocampal place cells are known for their spatially selective firing and are believed to encode an animals location while forming part of a cognitive map of space. These cells exhibit marked tuning curve and rate changes when an animals environment is sufficiently manipulated, in a process known as remapping. Place cells are accompanied by many other spatially tuned cells such as border cells and grid cells, but how these cells interact during navigation and remapping is unknown. In this work, we build a normative place cell model wherein a neural network is tasked with accurate position reconstruction and path integration. Motivated by the notion of a cognitive map, the networks position is estimated directly from its learned representations. To obtain a position estimate, we propose a non-trainable decoding scheme applied to network output units, inspired by the localized firing patterns of place cells. We find that output units learn place-like spatial representations, while upstream recurrent units become boundary-tuned. When the network is trained to perform the same task in multiple simulated environments, its place-like units learn to remap like biological place cells, displaying global, geometric and rate remapping. These remapping abilities appear to be supported by rate changes in upstream units. While the model does not learn grid-like units, its place unit centers form clusters organized in a hexagonal lattice in open fields. When we decode the center locations of CA1 place fields in mice, we find preliminary evidence of a similar clustering tendency. This suggests a potential mechanism for the interaction between place cells, border cells, and grid cells. Our model provides a normative framework for learning spatial representations previously reserved for biological place cells, providing new insight into place cell field formation and remapping.

neuroscience↗

bioRxiv2024

Hexagons all the way down: Grid cells as a conformal isometric map of space

The brains ability to navigate is often attributed to spatial cells in the hippocampus and entorhinal cortex. Grid cells, found in the entorhinal cortex, are known for their hexagonal spatial activity patterns and are traditionally believed to be the neural basis for path integration. However, recent studies have cast grid cells as a distance-preserving representation. We further investigate this role in a model of grid cells based on a superposition of plane waves. In a module of such grid cells, we optimise their phases to form a conformal isometry (CI) of two-dimensional flat space. With this setup, we demonstrate that a module of at least seven grid cells can achieve a CI, with phases forming a regular hexagonal arrangement. This pattern persists when increasing the number of cells, significantly diverging from a random uniform distribution. In particular, when optimised for CI, the phase distribution becomes distinctly regular and hexagonal, offering a clear experimentally testable prediction. Moreover, grid modules encoding a CI maintain constant energy expenditure across space, providing a new perspective on the role of energy constraints in normative models of grid cells. Finally, we investigate the minimum number of grid cells required for various spatial encoding tasks, including a unique representation of space, the population activity forming a torus, and achieving a CI, where we find that all three are achieved when the module encodes a CI. Our study not only underscores the versatility of grid cells beyond path integration but also highlights the importance of geometric principles in neural representations of space.

neuroscience↗

bioRxiv2022

Navigating Multiple Environments with Emergent Grid Cell Remapping

Animals employ a neural system to map physical environmental position to neural activity and encode allocentric location. Grid cells, proposedly a vital component of this system, form a population code of space by firing in characteristic tessellated triangles of locations. This population code remaps across environments and behavioural states, independently of specific sensory inputs, pointing to a substrate of standard computation across environments, which many speculate to be path integration. However, testing whether these cells are crucial for path integration is outside the scope of current experiments and calls for complementary methods, possibly given by computational models. Recently, normative artificial neural network models have shown that path integration and grid-cell-like activity can be found in recurrent neural networks (RNNs) trained to navigate in a simulated two-dimensional environment. Remarkably, the emergent spatial profile of these grid-like cells is similar to biological cell responses in that they set up a toroidal structure. Here, we extend the RNN normative model to multiple environments and show that cells that form the toroidal structure are crucial for path integration. However, cells selected through the grid cell score, a common defining property of grid cells, are much less important and comparable to randomly selected cells. Moreover, we show that the model can navigate multiple environments and that toroidal cells remap across environments in a biologically plausible way. Results demonstrate a causal relation between toroidal cells and path integration in virtual agents and propose a mechanism of remapping in grid cells based on remapping in place cells. The work is anticipated to impact both experimental and computational neuroscience and machine learning due to the methods employed and the evaluation of results. For example, we propose explicit experiments that can evaluate both the models validity and the role of grid cells in navigation. Moreover, the model may elucidate how high-dimensional data is mapped to low-dimensional structures, possibly providing a substrate for interpolation.

neuroscience↗