The place cell activity in three-dimensional space generated by multiple grid cell inputs

Xu, Xuying; Wang, Yihong; Wang, Rubin

doi:10.1007/s11071-022-07247-3

Cited by 3 publications

(3 citation statements)

References 36 publications

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“…Subsequent research confirmed that head direction cell coding follows a continuous attractor family (Taube et al, 1990;Zhang, 1996;Kim et al, 2017). Similarly, place cells have been found to exhibit analogous coding behavior for position within 2D environments (O' Keefe and Burgess, 1996;McNaughton et al, 2006;Xu et al, 2022). Collectively, these findings support the widespread presence of the continuous dynamic shift mechanism in biological neural systems.…”

Section: Introductionmentioning

confidence: 68%

A new dynamic shift mechanism based on cyclic group theory for continuous attractor neural networks

Zhang,

Tang,

Zhao

et al. 2024

Preprint

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Spatial cognition plays a crucial role in enabling animals to navigate effectively, whether hunting prey or exploring for food. Continuous attractor neural networks (CANNs) have been developed to model spatial cells, such as head direction cells, place cells and grid cells. However, how to maintain long-term spatial memory and accurately transfer network activity in CANNs is still unclear. We design a modified version of CANNs that can maintain long-term spatial memory without external cues. Based on this redesigned CANN, we propose a novel dynamic shift mechanism. It leverages cyclic group theory to dynamically adjust connections between neurons. These synaptic changes effectively shift the network activity according to speed information. Interestingly, our method exhibits anticipatory and delayed transfers, analogous to the predictive coding observed in the brain's entorhinal speed cells and grid cells. Compared to the existing biological shift mechanism, our method demonstrates significant advantages in both computation speed and path-integration accuracy. Notably, our approach facilitates vector-based navigation with high accuracy. We rigorously verified its effectiveness through mathematical analysis and further validated it with simulated and real-world data. Experimental results demonstrate that our method accurately determines the navigation vector, enabling precise navigation between the current location and the target position. This work has established a comprehensive theoretical framework encompassing the entire process from inputs to cognition coding, and further extending to vector-based navigation, laying the foundation for practical implementation in bio-inspired robot navigation systems.

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Section: Introductionmentioning

confidence: 68%

A new dynamic shift mechanism based on cyclic group theory for continuous attractor neural networks

Zhang,

Tang,

Zhao

et al. 2024

Preprint

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“…Fig. 12 The trajectory-dependency of grid fields on smooth surfaces in 3D space Grid patterns with different orientations and wavelengths are first generated on the surfaces in 3D space, then these patterns are summed by the similar designed synaptic weights (Xu et al 2022b).…”

Section: Application Of Neural Energy Coding In Brain Navigationmentioning

confidence: 99%

Brain works principle followed by neural information processing: a review of novel brain theory

Wang

et al. 2023

Artif Intell Rev

Self Cite

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The way the brain work and its principle of work has long been a big scientific question that scientists have dreamed of solving. However, as is known to all, the brain works at different levels, and the operation at different levels is interactional and mutually coupled. Unfortunately, until now, we still do not know how the nervous system at different levels is interacting and coupling with each other. This review provides some preliminary discussions on how to address these scientific questions, for which we propose a novel theory of the brain called neural energy. Such a theoretical and research approach can couple neural information with neural energy to address the interactions of the nervous system at various levels. Therefore, this review systematically summarizes the neural energy theories and methods proposed by our research in the field of brain science, as well as the internal relationship between mechanics and neural energy theory. Focuses on how to construct a Wang–Zhang (W–Z) neuron model equivalent to Hodgkin–Huxley (H–H) model by using the idea of analytical dynamics. Then, based on this model, we proposed a large-scale neural model and a theoretical framework of global neural coding of the brain in the field of neuroscience. It includes information processing of multiple sensory and perceptual nervous systems such as visual perception, neural mechanism of coupling between default mode network and functional network of brain, memory switching and brain state switching, brain navigation, prediction of new working mechanism of neurons, and interpretation of experimental phenomena that are difficult to be explained by neuroscience. It is proved that the new W–Z neuron model and neural energy theory have unique functions and advantages in neural modeling, neural information processing and methodology. The idea of large-scale neuroscience research with neural energy as the core will provide a potentially powerful research method for promoting the fusion of experimental neuroscience and theoretical neuroscience in the future, and propose a widely accepted brain theory system between experimental neuroscience and theoretical neuroscience. It is of great scientific significance to abandon the shortcomings of reductive and holism research methods in the field of neuroscience, and effectively integrate their respective advantages in methodology.

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“…Pattern formation was often investigated by bifurcation [1] , [2] , [3] , [4] , [5] , [6] , [7] , [8] in the reaction–diffusion or network-organized system. Turing first explained the mechanism of biological pattern formation in the reaction–diffusion system [9] .…”

Section: Introductionmentioning

confidence: 99%

Pattern mechanism in stochastic SIR networks with ER connectivity

Zheng

Shen

et al. 2022

Physica A: Statistical Mechanics and its Applications

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The place cell activity in three-dimensional space generated by multiple grid cell inputs

Cited by 3 publications

References 36 publications

A new dynamic shift mechanism based on cyclic group theory for continuous attractor neural networks

A new dynamic shift mechanism based on cyclic group theory for continuous attractor neural networks

Brain works principle followed by neural information processing: a review of novel brain theory

Pattern mechanism in stochastic SIR networks with ER connectivity

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