Weak electric fields detectability in a noisy neural network

Zhao, Jia; Deng, Bin; Qin, Yingmei; Men, Cong; Wang, Jiang; Wei, Xile; Sun, Jianbing

doi:10.1007/s11571-016-9409-x

Cited by 26 publications

(13 citation statements)

References 48 publications

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“…83,[85][86][87] Biological systems are capable of amplifying signals from weak electromagnetic fields. 88 The correct medicine and living system recipient require communication of an individually relevant "message" 25 or information pattern at their interface. Sensory detection, 53,56,65 endogenous non-linear cell-to-cell communication, 77,87,[89][90][91][92][93][94][95] and non-linear amplification processes 18,54 in the body engage the inter-connected cells and sub-systems that comprise the body.…”

Section: Summary and Discussionmentioning

confidence: 99%

The Complexity of the Homeopathic Healing Response Part 1: The Role of the Body as a Complex Adaptive System in Simillimum-Initiated Recovery from Disease

Bell

2019

Homeopathy

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Background The concepts of complex systems science enhance the understanding of how people develop and recover from disease. Living systems (human beings, animals, and plants) are self-organizing complex adaptive systems (CAS): that is, interconnected networks. CAS maintain life by initiating and carrying out non-linear dynamical changes to optimize survival fitness and function in the context of an ever-changing environment. Aims In Part 1 of this two-part paper, we relate concepts from complex systems science to homeopathic healing. The systemic changes of homeopathic healing involve adaptive patterns of responses to salient signals (similia) for reversing disease patterns and generating emergent multi-symptom healing over time. Methods and Results This narrative review relates homeopathic clinical practice theory to complex systems and network research. Homeopathic medicines communicate individually salient environmental information to the organism, with effects that are multi-system and indirect. The body's defense mechanisms recognize the self-similar information that the correctly chosen simillimum medicine at low dose conveys as a weak external/internal environmental stressor or danger signal (hormetin) to mobilize neural and cellular defenses. The body networks then use endogenous cell to cell signaling and amplify the small magnitude signal information. The results are disproportionately large: that is, non-linear, adaptive, modifications across the inter-connected self-organized biological networks/sub-systems of the body. CAS amplification mechanisms for small or weak signals include stochastic resonance, time-dependent sensitization, and hormesis. Conclusions The body as a complex system has the capacity for self-organization, emergence and self-similarity over global (overall health and wellbeing) and local (organ) levels of organization. These features are key for future research on the systemic healing that evolves over time during individualized homeopathic treatment.

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Section: Summary and Discussionmentioning

confidence: 99%

The Complexity of the Homeopathic Healing Response Part 1: The Role of the Body as a Complex Adaptive System in Simillimum-Initiated Recovery from Disease

Bell

2019

Homeopathy

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“…Some studies that have applied the Izhikevich neural model are represented in the following. Zhao et al investigated the probability of detecting a weak electric field in neural networks with the presence of noise (Zhao et al, 2017). Lv et al have implemented the simultaneous simulations of brain networks based on the Izhikevich spiking model (Lv et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

Improvement of the Izhikevich model based on the rat basolateral amygdala and hippocampus neurons, and recognition of their possible firing patterns

Hojjatinia

Shoorehdeli

Fatahi

et al. 2019

Basic Clin. Neurosci. J.

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Identifying the potential firing patterns following different brain regions under normal and abnormal conditions increases our understanding of events at the level of neural interactions in the brain. Furthermore, it is important to be capable of modeling the potential neural activities to build precise artificial neural networks. The Izhikevich model is one of the simplest biologically-plausible models, i.e. capable of capturing most recognized firing patterns of neurons. This property makes the model efficient in simulating the large-scale networks of neurons. Improving the Izhikevich model for adapting with the neuronal activity of rat brain with great accuracy would make the model effective for future neural network implementations. Methods: Data sampling from two brain regions, the HIP and BLA, was performed by the extracellular recordings of male Wistar rats, and spike sorting was conducted by Plexon offline sorter. Further analyses were performed through NeuroExplorer and MATLAB. To optimize the Izhikevich model parameters, a genetic algorithm was used. In this algorithm, optimization tools, like crossover and mutation, provide the basis for generating model parameters populations. The process of comparison in each iteration leads to the survival of better populations until achieving the optimum solution. Results: In the present study, the possible firing patterns of the real single neurons of the HIP and BLA were identified. Additionally, an improved Izhikevich model was achieved. Accordingly, the real neuronal spiking pattern of these regions' neurons and the corresponding cases of the Izhikevich neuron spiking pattern were adjusted with great accuracy. Conclusion: This study was conducted to elevate our knowledge of neural interactions in different structures of the brain and accelerate the quality of future large-scale neural networks simulations, as well as reducing the modeling complexity. This aim was achievable by performing the improved Izhikevich model, and inserting only the plausible firing patterns and eliminating unrealistic ones.

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“…Spike synchronization has also been shown in coupled neural network models, such as the small-world network reported by Kim and Lim (2017) and in their subsequent free-scale network model (Kim and Lim 2018). In a noisy neural network model based on Izhikevich neuron models, SR was induced by a white Gaussian current (Zhao et al 2017) and was supported by neural coupling following synchronization of neural spikes. Under these conditions, decoupling eliminated synchronization.…”

Section: Introductionmentioning

confidence: 90%

Random pulse induced synchronization and resonance in uncoupled non-identical neuron models

Nakamura

Tateno

2019

Cogn Neurodyn

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Random pulses contribute to stochastic resonance in neuron models, whereas common random pulses cause stochasticsynchronized excitation in uncoupled neuron models. We studied concurrent phenomena contributing to phase synchronization and stochastic resonance following induction by a weak common random pulse in uncoupled non-identical Hodgkin-Huxley type neuron models. The common random pulse was selected from a gamma distribution and the degree of synchronization depended on the corresponding shape parameter. Specifically, a low shape parameter of the weak random pulse induced well-synchronized spiking in uncoupled neuron models, whereas a high shape parameter of the weak random pulse or a weak periodic pulse caused low degrees of synchronization. These were improved by concurrent inputs of periodic and random pulses with high shape parameters. Finally, the output pulse was synchronized with the periodic pulse, and the common random pulse revealed periodic responses in the present neuron models.

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Weak electric fields detectability in a noisy neural network

Cited by 26 publications

References 48 publications

The Complexity of the Homeopathic Healing Response Part 1: The Role of the Body as a Complex Adaptive System in Simillimum-Initiated Recovery from Disease

The Complexity of the Homeopathic Healing Response Part 1: The Role of the Body as a Complex Adaptive System in Simillimum-Initiated Recovery from Disease

Improvement of the Izhikevich model based on the rat basolateral amygdala and hippocampus neurons, and recognition of their possible firing patterns

Random pulse induced synchronization and resonance in uncoupled non-identical neuron models

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