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

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

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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