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The paper examines how reduced myonuclear number during muscle growth affects nuclear positioning, finding more regular longitudinal distribution reduces cytoplasmic regions distant from nuclei.

This paper introduces a biologically inspired model of cognitive mapping that learns spatial representations efficiently in novel environments using neuromodulatory signals.

This paper introduces ANCA, a neocortex-inspired model with movable sensors that naturally exhibits active sensing, improving classification performance and scalability without explicit attention mechanisms.

This paper introduces an exponential map framework to generate interpretable neural spatial representations, showing how algebraic conditions enable path and translational invariance and biologically relevant spatial tuning.

This paper investigates grid cells as a conformal isometric map of space, showing that a module of at least seven grid cells can achieve a distance-preserving representation with regular hexagonal phase patterns.

This study shows that MEC and POR encode task- and behavior-related variables during non-spatial learning, with MEC showing stronger within-cluster correlations and anatomical organization.

This paper introduces a similarity-based objective function that enables neural networks to learn place-like representations and exhibit remapping behaviors when encoding multiple contexts.

This paper shows that grid-like spatial representations can emerge in neural networks without path integration, suggesting distance preservation and capacity constraints may be key factors.

This paper develops a neural network model that learns place cell-like spatial representations and remapping behaviors, showing clustering patterns similar to biological place cells in mice.

The paper demonstrates how causal inference methods like instrumental variables and difference in differences can reduce confounding in optogenetic experiments to estimate effective neural connectivity.

The paper shows that toroidal cells, not grid cells, are crucial for path integration in virtual agents using RNN models.

This study shows that perineuronal nets stabilize grid cell networks by maintaining inhibitory activity and spatial representations in adult rats.

This paper introduces a computational method for simulating neural populations using 2D point spiking neuron models to study noise effects in complex systems.

The paper presents an open-source system for behavioral tracking, electrophysiology, and closed-loop stimulation using Open Ephys and Bonsai.

The paper introduces Exdir, an open standard for organizing data storage in experimental pipelines as an alternative to HDF5, improving data sharing and reproducibility.