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Brainwave entrainment is a colloquialism for 'neural entrainment', [25] which is a term used to denote the way in which the aggregate frequency of oscillations produced by the synchronous electrical activity in ensembles of cortical neurons can adjust to synchronize with the periodic vibration of external stimuli, such as a sustained acoustic ...
Auditory entrainment (AE) is the same concept as visual entrainment, with the exception that auditory signals are passed from the cochlea of the ears into the thalamus via the medial geniculate nucleus, whereas visual entrainment passes from the retina into the thalamus via the lateral geniculate nucleus. [4]
Central pattern generators (CPGs) are self-organizing biological neural circuits [1] [2] that produce rhythmic outputs in the absence of rhythmic input. [3] [4] [5] They are the source of the tightly-coupled patterns of neural activity that drive rhythmic and stereotyped motor behaviors like walking, swimming, breathing, or chewing.
Neurolinguistics is the study of neural mechanisms in the human brain that control the comprehension, production, and acquisition of language. As an interdisciplinary field, neurolinguistics draws methods and theories from fields such as neuroscience, linguistics, cognitive science, communication disorders and neuropsychology. Researchers are ...
Systems neuroscience encompasses a number of areas of study concerned with how nerve cells behave when connected together to form neural pathways, neural circuits, and larger brain networks. At this level of analysis, neuroscientists study how different neural circuits work together to analyze sensory information, form perceptions of the ...
In the original Chua-Yang CNN (CY-CNN) processor, the state of the cell was a weighted sum of the inputs and the output was a piecewise linear function.However, like the original perceptron-based neural networks, the functions it could perform were limited: specifically, it was incapable of modeling non-linear functions, such as XOR.
The activation function of a node in an artificial neural network is a function that calculates the output of the node based on its individual inputs and their weights. Nontrivial problems can be solved using only a few nodes if the activation function is nonlinear .
Matlab: The neural network toolbox has explicit functionality designed to produce a time delay neural network give the step size of time delays and an optional training function. The default training algorithm is a Supervised Learning back-propagation algorithm that updates filter weights based on the Levenberg-Marquardt optimizations.