Artificial neural network - Wikipedia

Here we analyze data from two different groups of human subjects: intracranial electrocorticography from 15 participants over a 38 year age range (15-53 years) and scalp EEG data from healthy younger (20-30 years) and older (60-70 years) adults to test the neural noise hypothesis from a 1/f noise perspective.

Feature Visualization: How neural nets build up ..

How neural networks build up their understanding of images.

Wavelet neural networks: A practical guide - ScienceDirect

N2 - It has been shown that oscillations can be generated by additive Gaussian white noise in a recurrent Hodgkin-Huxley neuron model. Type 1 oscillation was induced with Stochastic Resonance (SR) by additive Gaussian noise at lower amplitudes, while Type 2 oscillation was observed at higher amplitudes. However, the mechanism of Type 2 oscillation is not clear. In this article, we test the hypothesis through computer simulations that the period of the Type 2 oscillation can be affected by temperature in a recurrent neural network in which the recurrent model is constructed by four Hodgkin-Huxley (HH) neuron models. Each HH neuron model is driven by Gaussian noise and sub-threshold excitatory synaptic currents with an alpha function from another HH neuron model, and the action potentials (spike firings) of each HH neuron model are transferred to the other HH neuron model via sub-threshold synaptic currents. From spike firing times recorded, the inter spike interval (ISI) histogram was generated, and the periodicity of spike firings was detected from the ISI histogram at each HH neuron model. The results show that the probability of spike firings in the Type1 oscillation is maximized at a specific standard deviation (S.D.) of the Gaussian white noise with SR at 6.3, 15.0 and 25.0 °C, while the period of the Type 2 oscillation depends on temperature. It is concluded that the Type1 oscillation can be induced by additive Gaussian white noise on the basis of a synaptic delay in the recurrent HH neuron model, whereas ISIs of the Type 2 oscillation may be determined by refractory periods of HH neuron models.

Neural networks and deep learning

AB - It has been shown that oscillations can be generated by additive Gaussian white noise in a recurrent Hodgkin-Huxley neuron model. Type 1 oscillation was induced with Stochastic Resonance (SR) by additive Gaussian noise at lower amplitudes, while Type 2 oscillation was observed at higher amplitudes. However, the mechanism of Type 2 oscillation is not clear. In this article, we test the hypothesis through computer simulations that the period of the Type 2 oscillation can be affected by temperature in a recurrent neural network in which the recurrent model is constructed by four Hodgkin-Huxley (HH) neuron models. Each HH neuron model is driven by Gaussian noise and sub-threshold excitatory synaptic currents with an alpha function from another HH neuron model, and the action potentials (spike firings) of each HH neuron model are transferred to the other HH neuron model via sub-threshold synaptic currents. From spike firing times recorded, the inter spike interval (ISI) histogram was generated, and the periodicity of spike firings was detected from the ISI histogram at each HH neuron model. The results show that the probability of spike firings in the Type1 oscillation is maximized at a specific standard deviation (S.D.) of the Gaussian white noise with SR at 6.3, 15.0 and 25.0 °C, while the period of the Type 2 oscillation depends on temperature. It is concluded that the Type1 oscillation can be induced by additive Gaussian white noise on the basis of a synaptic delay in the recurrent HH neuron model, whereas ISIs of the Type 2 oscillation may be determined by refractory periods of HH neuron models.

Convolutional Neural Networks vs

Here we analyze data from two different groups of human subjects: intracranial electrocorticography from 15 participants over a 38 year age range (15–53 years) and scalp EEG data from healthy younger (20 –30 years) and older (60 –70 years) adults to test the neural noise hypothesis from a 1/f noise perspective.

An Overview of Data Mining Techniques - Thearling

Results suggest higherneural noise and impaired local and distant neural coordination in thepatients and support the neural noise hypothesis to explain dyscognitionin FM.

Applied Mathematics Department - Brown University

The neural noise hypothesis, a dominant view of the basis of this decline, posits that aging is accompanied by an increase in spontaneous, noisy baseline neural activity.