A brain-inspired compact cognitive mapping system

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.

Modeling the grid cell activity based on cognitive space transformation

The grid cells in the medial entorhinal cortex are widely recognized as a critical component of spatial cognition within the entorhinal-hippocampal neuronal circuits. To account for the hexagonal patterns, several computational models have been proposed. However, there is still considerable debate regarding the interaction between grid cells and place cells. In response, we have developed a novel grid-cell computational model based on cognitive space transformation, which established a theoretical framework of the interaction between place cells and grid cells for encoding and transforming positions between the local frame and global frame. Our model not only can generate the firing patterns of the grid cells but also reproduces the biological experiment results about the grid-cell global representation of connected environments and supports the conjecture about the underlying reason. Moreover, our model provides new insights into how grid cells and place cells integrate external and self-motion cues.

Innate Orientating Behavior of a Multi-Legged Robot Driven by the Neural Circuits of C. elegans

The objective of this research is to achieve biologically autonomous control by utilizing a whole-brain network model, drawing inspiration from biological neural networks to enhance the development of bionic intelligence. Here, we constructed a whole-brain neural network model of Caenorhabditis elegans (C. elegans), which characterizes the electrochemical processes at the level of the cellular synapses. The neural network simulation integrates computational programming and the visualization of the neurons and synapse connections of C. elegans, containing the specific controllable circuits and their dynamic characteristics. To illustrate the biological neural network (BNN)'s particular intelligent control capability, we introduced an innovative methodology for applying the BNN model to a 12-legged robot's movement control. Two methods were designed, one involving orientation control and the other involving locomotion generation, to demonstrate the intelligent control performance of the BNN. Both the simulation and experimental results indicate that the robot exhibits more autonomy and a more intelligent movement performance under BNN control. The systematic approach of employing the whole-brain BNN for robot control provides biomimetic research with a framework that has been substantiated by innovative methodologies and validated through the observed positive outcomes. This method is established as follows: (1) two integrated dynamic models of the C. elegans' whole-brain network and the robot moving dynamics are built, and all of the controllable circuits are discovered and verified; (2) real-time communication is achieved between the BNN model and the robot's dynamical model, both in the simulation and the experiments, including applicable encoding and decoding algorithms, facilitating their collaborative operation; (3) the designed mechanisms using the BNN model to control the robot are shown to be effective through numerical and experimental tests, focusing on 'foraging' behavior control and locomotion control.

Variations of the grid and place cells in the entorhinal cortex and dentate gyrus of 6 individuals aged 56 to 87 years

INTRODUCTION

The relationship between the entorhinal cortex (EC) and the hippocampus has been studied by different authors, who have highlighted the importance of grid cells, place cells, and the trisynaptic circuit in the processes that they regulate: the persistence of spatial, explicit, and recent memory and their possible impairment with ageing.

OBJECTIVE

We aimed to determine whether older age causes changes in the size and number of grid cells contained in layer III of the EC and in the granular layer of the dentate gyrus (DG) of the hippocampus.

METHODS

We conducted post-mortem studies of the brains of 6 individuals aged 56-87 years. The brain sections containing the DG and the adjacent EC were stained according to the Klüver-Barrera method, then the ImageJ software was used to measure the individual neuronal area, the total neuronal area, and the number of neurons contained in rectangular areas in layer III of the EC and layer II of the DG. Statistical analysis was subsequently performed.

RESULTS

We observed an age-related reduction in the cell population of the external pyramidal layer of the EC, and in the number of neurons in the granular layer of the DG.

CONCLUSION

Our results indicate that ageing causes a decrease in the size and density of grid cells of the EC and place cells of the DG.

Bridging the gap between EEG and DCNNs reveals a fatigue mechanism of facial repetition suppression

Facial repetition suppression, a well-studied phenomenon characterized by decreased neural responses to repeated faces in visual cortices, remains a subject of ongoing debate regarding its underlying neural mechanisms. Our research harnesses advanced multivariate analysis techniques and the prowess of deep convolutional neural networks (DCNNs) in face recognition to bridge the gap between human electroencephalogram (EEG) data and DCNNs, especially in the context of facial repetition suppression. Our innovative reverse engineering approach, manipulating the neuronal activity in DCNNs and conducted representational comparisons between brain activations derived from human EEG and manipulated DCNN activations, provided insights into the underlying facial repetition suppression. Significantly, our findings advocate the fatigue mechanism as the dominant force behind the facial repetition suppression effect. Broadly, this integrative framework, bridging the human brain and DCNNs, offers a promising tool for simulating brain activity and making inferences regarding the neural mechanisms underpinning complex human behaviors.

Entorhinal-hippocampal interactions lead to globally coherent representations of space

The firing maps of grid cells in the entorhinal cortex are thought to provide an efficient metric system capable of supporting spatial inference in all environments. However, whether spatial representations of grid cells are determined by local environment cues or are organized into globally coherent patterns remains undetermined. We propose a navigation model containing a path integration system in the entorhinal cortex and a cognitive map system in the hippocampus. In the path integration system, grid cell network and head direction (HD) cell network integrate movement and visual information, and form attractor states to represent the positions and head directions of the animal. In the cognitive map system, a topological map is constructed capturing the attractor states of the path integration system as nodes and the transitions between attractor states as links. On loop closure, when the animal revisits a familiar place, the topological map is calibrated to minimize odometry errors. The change of the topological map is mapped back to the path integration system, to correct the states of the grid cells and the HD cells. The proposed model was tested on iRat, a rat-like miniature robot, in a realistic maze. Experimental results showed that, after familiarization of the environment, both grid cells and HD cells develop globally coherent firing maps by map calibration and activity correction. These results demonstrate that the hippocampus and the entorhinal cortex work together to form globally coherent metric representations of the environment. The underlying mechanisms of the hippocampal-entorhinal circuit in capturing the structure of the environment from sequences of experience are critical for understanding episodic memory.

Variations of the grid and place cells in the entorhinal cortex and dentate gyrus of 6 individuals aged 56 to 87 years

INTRODUCTION

The relationship between the entorhinal cortex and the hippocampus has been studied by different authors, who have highlighted the importance of grid cells, place cells, and the trisynaptic circuit in the processes that they regulate: the persistence of spatial, explicit, and recent memory and their possible impairment with ageing.

OBJECTIVE

We aimed to determine whether older age causes changes in the size and number of grid cells contained in layer III of the entorhinal cortex and in the granular layer of the dentate gyrus of the hippocampus.

METHODS

We conducted post-mortem studies of the brains of 6 individuals aged 56-87 years. The brain sections containing the dentate gyrus and the adjacent entorhinal cortex were stained according to the Klüver-Barrera method, then the Image J software was used to measure the individual neuronal area, the total neuronal area, and the number of neurons contained in rectangular areas in layer III of the entorhinal cortex and layer II of the dentate gyrus. Statistical analysis was subsequently performed.

RESULTS

We observed an age-related reduction in the cell population of the external pyramidal layer of the entorhinal cortex, and in the number of neurons in the granular layer of the dentate gyrus.

CONCLUSION

Our results indicate that ageing causes a decrease in the size and density of grid cells of the entorhinal cortex and place cells of the dentate gyrus.