Regular pulse train transformation by the monosynaptic connection-neurophysiological data and their treating with simple stochastic neuron model

1976 ◽  
Vol 24 (4) ◽  
pp. 219-226 ◽  
Author(s):  
A. I. Kostyukov ◽  
K. V. Bayev ◽  
D. A. Vasilenko
Keyword(s):  
Pulse Train ◽  
Neuron Model ◽  
Monosynaptic Connection ◽  
Regular Pulse ◽  
Neurophysiological Data ◽  
Stochastic Neuron

scholarly journals Stimulation Strategies for Tinnitus Suppression in a Neuron Model

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Alessandra Paffi ◽  
Francesca Camera ◽  
Chiara Carocci ◽  
Francesca Apollonio ◽  
Micaela Liberti
Keyword(s):  
Gaussian Noise ◽  
Neuron Model ◽  
Continuous Wave ◽  
Experimental Studies ◽  
Neuronal Firing ◽  
White Gaussian Noise ◽  
Cochlear Prosthesis ◽  
Continuous Waves ◽  
Stochastic Neuron ◽  
Underlying Mechanisms

Tinnitus is a debilitating perception of sound in the absence of external auditory stimuli. It may have either a central or a peripheral origin in the cochlea. Experimental studies evidenced that an electrical stimulation of peripheral auditory fibers may alleviate symptoms but the underlying mechanisms are still unknown. In this work, a stochastic neuron model is used, that mimics an auditory fiber affected by tinnitus, to check the effects, in terms of firing reduction, of different kinds of electric stimulations, i.e., continuous wave signals and white Gaussian noise. Results show that both white Gaussian noise and continuous waves at tens of kHz induce a neuronal firing reduction; however, for the same amplitude of fluctuations, Gaussian noise is more efficient than continuous waves. When contemporary applied, signal and noise exhibit a cooperative effect in retrieving neuronal firing to physiological values. These results are a proof of concept that a combination of signal and noise could be delivered through cochlear prosthesis for tinnitus suppression.

Linear and Nonlinear Electrical Models of Neurons for Hopfield Neural Network

2016 ◽  
Vol 71 (11) ◽  
pp. 995-1002
Author(s):  
Farah Sarwar ◽  
Shaukat Iqbal ◽  
Muhammad Waqar Hussain
Keyword(s):  
Neural Network ◽  
Neuron Model ◽  
Deterministic Model ◽  
Hopfield Neural Network ◽  
Biological Properties ◽  
Operational Amplifiers ◽  
Nonlinear Input ◽  
Stochastic Neuron ◽  
Output Characteristics ◽  
The Stability

AbstractA novel electrical model of neuron is proposed in this presentation. The suggested neural network model has linear/nonlinear input-output characteristics. This new deterministic model has joint biological properties in excellent agreement with the earlier deterministic neuron model of Hopfield and Tank and to the stochastic neuron model of McCulloch and Pitts. It is an accurate portrayal of differential equation presented by Hopfield and Tank to mimic neurons. Operational amplifiers, resistances, capacitor, and diodes are used to design this system. The presented biological model of neurons remains to be advantageous for simulations. Impulse response is studied and conferred to certify the stability and strength of this innovative model. A simple illustration is mapped to demonstrate the exactness of the intended system. Precisely mapped illustration exhibits 100 % accurate results.

Estimation and Verification of a Stochastic Neuron Model

1986 ◽  
Vol BME-33 (7) ◽  
pp. 654-666 ◽  
Author(s):  
William D. O'Neill ◽  
James C. Lin ◽  
Ying-Chang Ma
Keyword(s):  
Neuron Model ◽  
Stochastic Neuron

scholarly journals Features of K-Changes Observed in Sri Lanka in the Tropics

Atmosphere ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 141
Author(s):  
Sankha Nanayakkara ◽  
Mahendra Fernando ◽  
Vernon Cooray
Keyword(s):  
Fine Structure ◽  
Sri Lanka ◽  
Pulse Train ◽  
Geometric Mean ◽  
Time Interval ◽  
Small Step ◽  
Time Duration ◽  
Pulse Trains ◽  
Regular Pulse ◽  
The Tropics

General characteristics of K-changes, including their duration and probability of occurrence associated with ground flashes in Sri Lanka in the tropics, together with their fine structure, are presented. In 98 ground flashes where the small step changes associated with K-changes are clearly visible, there were about two K-changes per flash on average. The mean K-change time duration observed in this study is 0.38 ms. In 53 of the ground flashes, there were 120 consecutive K-changes. In these cases, the geometric mean of the time interval between K-changes was 12 ms. Analysis of the fine structure of the K-changes reveals the K-changes are always associated with either a chaotic pulse train or a combination of chaotic and regular pulse trains. The results suggest that the small step-like static electric fields identified in the literature as K-changes are the step-like static fields associated with the processes that generate chaotic or a combination of chaotic and regular pulse trains. Thus, at larger distances where the static fields are negligible, K-changes may appear as a chaotic pulse train or a combination of chaotic and regular pulse trains.

scholarly journals Restoring the encoding properties of a stochastic neuron model by an exogenous noise

2015 ◽  
Vol 9 ◽  
Author(s):  
Alessandra Paffi ◽  
Francesca Camera ◽  
Francesca Apollonio ◽  
Guglielmo d'Inzeo ◽  
Micaela Liberti
Keyword(s):  
Neuron Model ◽  
Stochastic Neuron ◽  
Exogenous Noise

Development of Reading Remediation for Dyslexic Individuals

2016 ◽  
Vol 224 (4) ◽  
pp. 240-246 ◽  
Author(s):  
Mélanie Bédard ◽  
Line Laplante ◽  
Julien Mercier
Keyword(s):  
20Th Century ◽  
Scientific Literature ◽  
Behavioral Data ◽  
Educational Neuroscience ◽  
Reading Remediation ◽  
Functional Brain ◽  
Neurophysiological Data ◽  
Brain Correlates ◽  
Research Questions ◽  
The Brain

Abstract. Dyslexia is a phenomenon for which the brain correlates have been studied since the beginning of the 20th century. Simultaneously, the field of education has also been studying dyslexia and its remediation, mainly through behavioral data. The last two decades have seen a growing interest in integrating neuroscience and education. This article provides a quick overview of pertinent scientific literature involving neurophysiological data on functional brain differences in dyslexia and discusses their very limited influence on the development of reading remediation for dyslexic individuals. Nevertheless, it appears that if certain conditions are met – related to the key elements of educational neuroscience and to the nature of the research questions – conceivable benefits can be expected from the integration of neurophysiological data with educational research. When neurophysiological data can be employed to overcome the limits of using behavioral data alone, researchers can both unravel phenomenon otherwise impossible to document and raise new questions.

Low Capacity Implementation of a Pulse-Type Chaotic Neuron Model

2016 ◽  
Vol 136 (10) ◽  
pp. 1424-1430 ◽  
Author(s):  
Yoshiki Sasaki ◽  
Katsutoshi Saeki ◽  
Yoshifumi Sekine
Keyword(s):  
Neuron Model ◽  
Pulse Type ◽  
Chaotic Neuron
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