Why grid cells function as a metric for space

Deep Learning-Emerged Grid Cells-Based Bio-Inspired Navigation in Robotics

Grid cells in the brain's entorhinal cortex are essential for spatial navigation and have inspired advancements in robotic navigation systems. This paper first provides an overview of recent research on grid cell-based navigation in robotics, focusing on deep learning models and algorithms capable of handling uncertainty and dynamic environments. We then present experimental results where a grid cell network was trained using trajectories from a mobile unmanned ground vehicle (UGV) robot. After training, the network's units exhibited spatially periodic and hexagonal activation patterns characteristic of biological grid cells, as well as responses resembling border cells and head-direction cells. These findings demonstrate that grid cell networks can effectively learn spatial representations from robot trajectories, providing a foundation for developing advanced navigation algorithms for mobile robots. We conclude by discussing current challenges and future research directions in this field.

Neuronal Plasma Membranes as Supramolecular Assemblies for Biological Memory

Biological memory is the ability to develop, retain, and retrieve information over time. Currently, it is widely accepted that memories are stored in synapses (i.e., connections between brain cells throughout the brain) through a process known as synaptic plasticity, which leads to either long-term potentiation (LTP) or long-term depression (LTD). However, the strengthening (LTP) and weakening (LTD) of synapses involve post-translational modifications to neural networks requiring de novo gene expression, a lengthy and energetically expensive process. Recently, we observed that lipid bilayers in the absence of peptides/proteins are capable of LTP, not unlike what has been observed in mammals and birds. As such, this finding has prompted us to postulate that the lipid bilayer provides a good model for understanding the molecular basis of biological memory. In this article, we discuss the status, challenges, and opportunities of neuronal plasma membranes as structures for biological memory and learning, therapeutic targets for various brain disorders, and platforms for neural network developments.

The medial entorhinal cortex encodes multisensory spatial information

Animals employ spatial information in multisensory modalities to navigate their natural environments. However, it is unclear whether the brain encodes such information in separate cognitive maps or integrates it all into a single, universal map. We address this question in the microcircuit of the medial entorhinal cortex (MEC), a cognitive map of space. Using cellular-resolution calcium imaging, we examine the MEC of mice navigating virtual reality tracks, where visual and auditory cues provide comparable spatial information. We uncover two cell types: "unimodality cells" and "multimodality cells." The unimodality cells specifically represent either auditory or visual spatial information. They are anatomically intermingled and maintain sensory preferences across multiple tracks and behavioral states. The multimodality cells respond to both sensory modalities, with their responses shaped differentially by auditory or visual information. Thus, the MEC enables accurate spatial encoding during multisensory navigation by computing spatial information in different sensory modalities and generating distinct maps.

A spatial transformation-based CAN model for information integration within grid cell modules

The hippocampal-entorhinal circuit is considered to play an important role in the spatial cognition of animals. However, the mechanism of the information flow within the circuit and its contribution to the function of the grid-cell module are still topics of discussion. Prevailing theories suggest that grid cells are primarily influenced by self-motion inputs from the Medial Entorhinal Cortex, with place cells serving a secondary role by contributing to the visual calibration of grid cells. However, recent evidence suggests that both self-motion inputs and visual cues may collaboratively contribute to the formation of grid-like patterns. In this paper, we introduce a novel Continuous Attractor Network model based on a spatial transformation mechanism. This mechanism enables the integration of self-motion inputs and visual cues within grid-cell modules, synergistically driving the formation of grid-like patterns. From the perspective of individual neurons within the network, our model successfully replicates grid firing patterns. From the view of neural population activity within the network, the network can form and drive the activated bump, which describes the characteristic feature of grid-cell modules, namely, path integration. Through further exploration and experimentation, our model can exhibit significant performance in path integration. This study provides a new insight into understanding the mechanism of how the self-motion and visual inputs contribute to the neural activity within grid-cell modules. Furthermore, it provides theoretical support for achieving accurate path integration, which holds substantial implications for various applications requiring spatial navigation and mapping.

The medial entorhinal cortex encodes multisensory spatial information

Animals employ spatial information in multisensory modalities to navigate their natural environments. However, it is unclear whether the brain encodes such information in separate cognitive maps or integrates all into a single, universal map. We addressed this question in the microcircuit of the medial entorhinal cortex (MEC), a cognitive map of space. Using cellular-resolution calcium imaging, we examined the MEC of mice navigating virtual reality tracks, where visual and auditory cues provided comparable spatial information. We uncovered two cell types: "unimodality cells" and "multimodality cells". The unimodality cells specifically represent either auditory or visual spatial information. They are anatomically intermingled and maintain sensory preferences across multiple tracks and behavioral states. The multimodality cells respond to both sensory modalities with their responses shaped differentially by auditory and visual information. Thus, the MEC enables accurate spatial encoding during multisensory navigation by computing spatial information in different sensory modalities and generating distinct maps.

Empirically validated theoretical analysis of visual-spatial perception under change of nervous system arousal

Introduction

Visual-spatial perception is a process for extracting the spatial relationship between objects in the environment. The changes in visual-spatial perception due to factors such as the activity of the sympathetic nervous system (hyperactivation) or parasympathetic nervous system (hypoactivation) can affect the internal representation of the external visual-spatial world. We formulated a quantitative model of the modulation of visual-perceptual space under action by hyperactivation or hypoactivation-inducing neuromodulating agents. We showed a Hill equation based relationship between neuromodulator agent concentration and alteration of visual-spatial perception utilizing the metric tensor to quantify the visual space.

Methods

We computed the dynamics of the psilocybin (hyperactivation-inducing agent) and chlorpromazine (hypoactivation-inducing agent) in brain tissue. Then, we validated our quantitative model by analyzing the findings of different independent behavioral studies where subjects were assessed for alterations in visual-spatial perception under the action of psilocybin and under chlorpromazine. To validate the neuronal correlates, we simulated the effect of the neuromodulating agent on the computational model of the grid-cell network, and also performed diffusion MRI-based tractography to find the neural tracts between the cortical areas involved: V2 and the entorhinal cortex.

Results

We applied our computational model to an experiment (where perceptual alterations were measured under psilocybin) and found that for n (Hill-coefficient) = 14.8 and k = 1.39, the theoretical prediction followed experimental observations very well (χ2 test robustly satisfied, p > 0.99). We predicted the outcome of another psilocybin-based experiment using these values (n = 14.8 and k = 1.39), whereby our prediction and experimental outcomes were well corroborated. Furthermore, we found that also under hypoactivation (chlorpromazine), the modulation of the visual-spatial perception follows our model. Moreover, we found neural tracts between the area V2 and entorhinal cortex, thus providing a possible brain network responsible for encoding visual-spatial perception. Thence, we simulated the altered grid-cell network activity, which was also found to follow the Hill equation.

Conclusion

We developed a computational model of visuospatial perceptual alterations under altered neural sympathetic/parasympathetic tone. We validated our model using analysis of behavioral studies, neuroimaging assessment, and neurocomputational evaluation. Our quantitative approach may be probed as a potential behavioral screening and monitoring methodology in neuropsychology to analyze perceptual misjudgment and mishaps by highly stressed workers.

Biologically-Based Computation: How Neural Details and Dynamics Are Suited for Implementing a Variety of Algorithms

The Neural Engineering Framework (Eliasmith & Anderson, 2003) is a long-standing method for implementing high-level algorithms constrained by low-level neurobiological details. In recent years, this method has been expanded to incorporate more biological details and applied to new tasks. This paper brings together these ongoing research strands, presenting them in a common framework. We expand on the NEF's core principles of (a) specifying the desired tuning curves of neurons in different parts of the model, (b) defining the computational relationships between the values represented by the neurons in different parts of the model, and (c) finding the synaptic connection weights that will cause those computations and tuning curves. In particular, we show how to extend this to include complex spatiotemporal tuning curves, and then apply this approach to produce functional computational models of grid cells, time cells, path integration, sparse representations, probabilistic representations, and symbolic representations in the brain.

Are Grid-Like Representations a Component of All Perception and Cognition?

Grid cells or grid-like responses have been reported in the rodent, bat and human brains during various spatial and non-spatial tasks. However, the functions of grid-like representations beyond the classical hippocampal formation remain elusive. Based on accumulating evidence from recent rodent recordings and human fMRI data, we make speculative accounts regarding the mechanisms and functional significance of the sensory cortical grid cells and further make theory-driven predictions. We argue and reason the rationale why grid responses may be universal in the brain for a wide range of perceptual and cognitive tasks that involve locomotion and mental navigation. Computational modeling may provide an alternative and complementary means to investigate the grid code or grid-like map. We hope that the new discussion will lead to experimentally testable hypotheses and drive future experimental data collection.

Homing tasks and distance matching tasks reveal different types of perceptual variables associated with perceiving self-motion during over-ground locomotion

Self-motion perception refers to the ability to perceive how the body is moving through the environment. Perception of self-motion has been shown to depend upon the locomotor action patterns used to move the body through the environment. Two separate lines of enquiry have led to the establishment of two distinct theories regarding this effect. One theory has proposed that distances travelled during locomotion are perceived via higher order perceptual variables detected by the haptic perceptual system. This theory proposes that two higher order haptic perceptual variables exist, and that the implication of one of these variables depends upon the type of gait pattern that is used. A second theory proposes that self-motion is perceived via a higher order perceptual variable termed multimodally specified energy expenditure (MSEE). This theory proposes that the effect of locomotor actions patterns upon self-motion perception is related to changes in the metabolic cost of locomotion per unit of perceptually specified traversed distance. Here, we test the hypothesis that the development of these distinct theories is the result of different choices in methodology. The theory of gait type has been developed based largely on the results of homing tasks, whereas the effect of MSEE has been developed based on the results of distance matching tasks. Here we test the hypothesis that the seemly innocuous change in experimental design from using a homing task to using a distance matching task changes the type of perceptual variables implicated in self-motion perception. To test this hypothesis, we closely replicated a recent study of the effect of gait type in all details bar one-we investigated a distance matching task rather than a homing task. As hypothesized, this change yielded results consistent with the predictions of MSEE, and distinct from gait type. We further show that, unlike the effect of gait type, the effect of MSEE is unaffected by the availability of vision. In sum, our findings support the existence of two distinct types of higher order perceptual variables in self-motion perception. We discuss the roles of these two types of perceptual variables in supporting effective human wayfinding.

Spatialization of Time in the Entorhinal-Hippocampal System

The functional role of the entorhinal-hippocampal system has been a long withstanding mystery. One key theory that has become most popular is that the entorhinal-hippocampal system represents space to facilitate navigation in one's surroundings. In this Perspective article, I introduce a novel idea that undermines the inherent uniqueness of spatial information in favor of time driving entorhinal-hippocampal activity. Specifically, by spatializing events that occur in succession (i.e., across time), the entorhinal-hippocampal system is critical for all types of cognitive representations. I back up this argument with empirical evidence that hints at a role for the entorhinal-hippocampal system in non-spatial representation, and computational models of the logarithmic compression of time in the brain.