An Energy Model of Place Cell Network in Three Dimensional Space

Wang, Yihong; Xu, Xuying; Wang, Rubin

doi:10.3389/fnins.2018.00264

Cited by 16 publications

(14 citation statements)

References 67 publications

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“…Why the amount of data in biological visual system would significantly reduce but not affect the external world visual cognition could also be explained by energy theory (Zhong and Wang 2020 ). Quantitative neural energy analysis of spatial cognition and maximum information coding of various animals also obtained results that well matched the experimental data (Wang et al 2018 , 2019 ).…”

Section: Introductionsupporting

confidence: 78%

Biophysical mechanism of the interaction between default mode network and working memory network

2021

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Default mode network (DMN) is a functional brain network with a unique neural activity pattern that shows high activity in resting states but low activity in task states. This unique pattern has been proved to relate with higher cognitions such as learning, memory and decision-making. But neural mechanisms of interactions between the default network and the task-related network are still poorly understood. In this paper, a theoretical model of coupling the DMN and working memory network (WMN) is proposed. The WMN and DMN both consist of excitatory and inhibitory neurons connected by AMPA, NMDA, GABA synapses, and are coupled with each other only by excitatory synapses. This model is implemented to demonstrate dynamical processes in a working memory task containing encoding, maintenance and retrieval phases. Simulated results have shown that: (1) AMPA channels could produce significant synchronous oscillations in population neurons, which is beneficial to change oscillation patterns in the WMN and DMN. (2) Different NMDA conductance between the networks could generate multiple neural activity modes in the whole network, which may be an important mechanism to switch states of the networks between three different phases of working memory. (3) The number of sequentially memorized stimuli was related to the energy consumption determined by the network's internal parameters, and the DMN contributed to a more stable working memory process. (4) Finally, this model demonstrated that, in three phases of working memory, different memory phases corresponded to different functional connections between the DMN and WMN. Coupling strengths that measured these functional connections differed in terms of phase synchronization. Phase synchronization characteristics of the contained energy were consistent with the observations of negative and positive correlations between the WMN and DMN reported in referenced fMRI experiments. The results suggested that the coupled interaction between the WMN and DMN played important roles in working memory.

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Section: Introductionsupporting

confidence: 78%

Biophysical mechanism of the interaction between default mode network and working memory network

2021

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“…A fully connected feed-forward network was implemented to determine activations of place cells and sensors as presented in 62 . Visual scenes were used to train the place cell network post navigation, speeding up the learning process of the place cell network and statistical analyses.…”

Section: Resultsmentioning

confidence: 99%

“…Every place cell responding to the current location with a firing power above a certain threshold modified their weights from sensors through a learning rule as reported in 63 . Weights were the basis vectors in the place cell model that computed the firing powers of these neurons 55 , 62 . Using the firing rate definition as in 64 and 65 , we considered the energy consumption of a place cell during an action potential (or spike) 62 .…”

Section: Resultsmentioning

confidence: 99%

Place cells dynamically refine grid cell activities to reduce error accumulation during path integration in a continuous attractor model

Fernández-León

Uysal

2022

Sci Rep

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Navigation is one of the most fundamental skills of animals. During spatial navigation, grid cells in the medial entorhinal cortex process speed and direction of the animal to map the environment. Hippocampal place cells, in turn, encode place using sensory signals and reduce the accumulated error of grid cells for path integration. Although both cell types are part of the path integration system, the dynamic relationship between place and grid cells and the error reduction mechanism is yet to be understood. We implemented a realistic model of grid cells based on a continuous attractor model. The grid cell model was coupled to a place cell model to address their dynamic relationship during a simulated animal’s exploration of a square arena. The grid cell model processed the animal’s velocity and place field information from place cells. Place cells incorporated salient visual features and proximity information with input from grid cells to define their place fields. Grid cells had similar spatial phases but a diversity of spacings and orientations. To determine the role of place cells in error reduction for path integration, the animal’s position estimates were decoded from grid cell activities with and without the place field input. We found that the accumulated error was reduced as place fields emerged during the exploration. Place fields closer to the animal’s current location contributed more to the error reduction than remote place fields. Place cells’ fields encoding space could function as spatial anchoring signals for precise path integration by grid cells.

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“…Neuron is the basic element of biological neural network, the study of electrical activity of single neuron (Zhu et al 2019) has been extended to the neural networks (Wang et al 2018b;Wen et al 2019a). For example, the feed-forward multilayer networks (Ge et al 2018a;Lu et al 2019c) were investigated.…”

Section: Introductionmentioning

confidence: 99%

Ionic channel blockage in stochastic Hodgkin–Huxley neuronal model driven by multiple oscillatory signals

Zhou

Wang

et al. 2020

Cogn Neurodyn

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Ionic channel blockage and multiple oscillatory signals play an important role in the dynamical response of pulse sequences. The effects of ionic channel blockage and ionic channel noise on the discharge behaviors are studied in Hodgkin-Huxley neuronal model with multiple oscillatory signals. It is found that bifurcation points of spontaneous discharge are altered through tuning the amplitude of multiple oscillatory signals, and the discharge cycle is changed by increasing the frequency of multiple oscillatory signals. The effects of ionic channel blockage on neural discharge behaviors indicate that the neural excitability can be suppressed by the sodium channel blockage, however, the neural excitability can be reversed by the potassium channel blockage. There is an optimal blockage ratio of potassium channel at which the electrical activity is the most regular, while the order of neural spike is disrupted by the sodium channel blockage. In addition, the frequency of spike discharge is accelerated by increasing the ionic channel noise, the firing of neuron becomes more stable if the ionic channel noise is appropriately reduced. Our results might provide new insights into the effects of ionic channel blockages, multiple oscillatory signals, and ionic channel noises on neural discharge behaviors.

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An Energy Model of Place Cell Network in Three Dimensional Space

Cited by 16 publications

References 67 publications

Biophysical mechanism of the interaction between default mode network and working memory network

Biophysical mechanism of the interaction between default mode network and working memory network

Place cells dynamically refine grid cell activities to reduce error accumulation during path integration in a continuous attractor model

Ionic channel blockage in stochastic Hodgkin–Huxley neuronal model driven by multiple oscillatory signals

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