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Stochastic Noise Application for the Assessment of Medial Vestibular Nucleus Neuron Sensitivity In Vitro
Published on: August 28, 2019
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A neuron model of stochastic resonance using rectangular pulse trains
Zachary Danziger1, Warren M Grill
1Department of Biomedical Engineering, Duke University, Campus box 90281, Durham, NC, 27708-0281, USA, zachary.danziger@duke.edu.
Journal of Computational Neuroscience
|September 5, 2014
Summary
Stochastic resonance (SR) can be achieved using novel pulse train perturbations, enhancing weak signal detection in a neuron model. This finding may improve neural prosthetics by mimicking natural neural stimulation.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Signal Processing
Background:
- Stochastic resonance (SR) enhances weak signal detection in nonlinear systems using noise.
- SR applications have expanded from periodic to aperiodic signals and various noise types.
- Traditional neural stimulation uses square pulses, differing from noise-based SR methods.
Purpose of the Study:
- To investigate a new class of perturbations for achieving SR.
- To demonstrate SR in a neuronal model using stochastically generated biphasic pulse trains.
- To explore the potential of this method for neural prosthetics.
Main Methods:
- Utilized a Hodgkin-Huxley model neuron.
- Employed series of stochastically generated biphasic pulse trains as perturbations.
- Assessed SR behavior in response to weak input signals.
Main Results:
- Stochastically generated biphasic pulse trains successfully induced SR in the neuron model.
- The neuron model exhibited enhanced weak input signal detection.
- SR was observed with pulse train perturbations, a novel approach.
Conclusions:
- Biphasic pulse trains can serve as an effective perturbation for inducing SR.
- This finding offers a new paradigm for neural stimulation.
- The results suggest potential applications for improving neural prosthetics and neurostimulation technologies.
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