Statistical field theory of the transmission of nerve impulses

Balzo, Gianluigi Zangari del

doi:10.1186/s12976-020-00132-9

Cited by 4 publications

(2 citation statements)

References 28 publications

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“…Accordingly, ongoing efforts seek to either adapt graph-theoretical constructs or to replace them with more sound neural fields which better incorporate realistic biophysical properties and mechanisms, physical structure, and spatial geometry into brain connectomics parameters [ 152 , 155 , 156 , 157 , 158 , 159 , 160 ]. Newer connectomic parameters now employed to enhance graph-theoretic brain structure-function models include wave-particle-defined neural conduction and entropy-quantified activity patterns.…”

Section: Discussionmentioning

confidence: 99%

Neural Field Continuum Limits and the Structure–Function Partitioning of Cognitive–Emotional Brain Networks

Clark

2023

Biology

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In The cognitive-emotional brain, Pessoa overlooks continuum effects on nonlinear brain network connectivity by eschewing neural field theories and physiologically derived constructs representative of neuronal plasticity. The absence of this content, which is so very important for understanding the dynamic structure-function embedding and partitioning of brains, diminishes the rich competitive and cooperative nature of neural networks and trivializes Pessoa’s arguments, and similar arguments by other authors, on the phylogenetic and operational significance of an optimally integrated brain filled with variable-strength neural connections. Riemannian neuromanifolds, containing limit-imposing metaplastic Hebbian- and antiHebbian-type control variables, simulate scalable network behavior that is difficult to capture from the simpler graph-theoretic analysis preferred by Pessoa and other neuroscientists. Field theories suggest the partitioning and performance benefits of embedded cognitive-emotional networks that optimally evolve between exotic classical and quantum computational phases, where matrix singularities and condensations produce degenerate structure-function homogeneities unrealistic of healthy brains. Some network partitioning, as opposed to unconstrained embeddedness, is thus required for effective execution of cognitive-emotional network functions and, in our new era of neuroscience, should be considered a critical aspect of proper brain organization and operation.

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Section: Discussionmentioning

confidence: 99%

Neural Field Continuum Limits and the Structure–Function Partitioning of Cognitive–Emotional Brain Networks

Clark

2023

Biology

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“…Data encoding method is spike coding, which can be closer to the real biological neural network. The commonly used neural models include the Hodgkin–Huxley (H–H) model [ 16 ], the LIF model [ 17 ], the Izhikevich model [ 18 ], etc. Morita et al [ 19 ] used the pulsed neural network to extract common words from Japanese speech data.…”

Section: Introductionmentioning

confidence: 99%

A Novel Evaluation Strategy to Artificial Neural Network Model Based on Bionics

et al. 2021

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With the continuous deepening of Artificial Neural Network (ANN) research, ANN model structure and function are improving towards diversification and intelligence. However, the model is more evaluated from the pros and cons of the problem-solving results and the lack of evaluation from the biomimetic aspect of imitating neural networks is not inclusive enough. Hence, a new ANN models evaluation strategy is proposed from the perspective of bionics in response to this problem in the paper. Firstly, four classical neural network models are illustrated: Back Propagation (BP) network, Deep Belief Network (DBN), LeNet5 network, and olfactory bionic model (KIII model), and the neuron transmission mode and equation, network structure, and weight updating principle of the models are analyzed qualitatively. The analysis results show that the KIII model comes closer to the actual biological nervous system compared with other models, and the LeNet5 network simulates the nervous system in depth. Secondly, evaluation indexes of ANN are constructed from the perspective of bionics in this paper: small-world, synchronous, and chaotic characteristics. Finally, the network model is quantitatively analyzed by evaluation indexes from the perspective of bionics. The experimental results show that the DBN network, LeNet5 network, and BP network have synchronous characteristics. And the DBN network and LeNet5 network have certain chaotic characteristics, but there is still a certain distance between the three classical neural networks and actual biological neural networks. The KIII model has certain small-world characteristics in structure, and its network also exhibits synchronization characteristics and chaotic characteristics. Compared with the DBN network, LeNet5 network, and the BP network, the KIII model is closer to the real biological neural network.

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Overcoming bias in estimating epidemiological parameters with realistic history-dependent disease spread dynamics

Hong,

Eom,

Lee

et al. 2024

Nat Commun

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Epidemiological parameters such as the reproduction number, latent period, and infectious period provide crucial information about the spread of infectious diseases and directly inform intervention strategies. These parameters have generally been estimated by mathematical models that involve an unrealistic assumption of history-independent dynamics for simplicity. This assumes that the chance of becoming infectious during the latent period or recovering during the infectious period remains constant, whereas in reality, these chances vary over time. Here, we find that conventional approaches with this assumption cause serious bias in epidemiological parameter estimation. To address this bias, we developed a Bayesian inference method by adopting more realistic history-dependent disease dynamics. Our method more accurately and precisely estimates the reproduction number than the conventional approaches solely from confirmed cases data, which are easy to obtain through testing. It also revealed how the infectious period distribution changed throughout the COVID-19 pandemic during 2020 in South Korea. We also provide a user-friendly package, IONISE, that automates this method.

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Statistical field theory of the transmission of nerve impulses

Cited by 4 publications

References 28 publications

Neural Field Continuum Limits and the Structure–Function Partitioning of Cognitive–Emotional Brain Networks

Neural Field Continuum Limits and the Structure–Function Partitioning of Cognitive–Emotional Brain Networks

A Novel Evaluation Strategy to Artificial Neural Network Model Based on Bionics

Overcoming bias in estimating epidemiological parameters with realistic history-dependent disease spread dynamics

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