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The focus of this article is a comprehensive view of modeling a neural network (technically neuronal network based on neuron model). Once an approach based on the perspective and connectivity is chosen, the models are developed at microscopic (ion and neuron), mesoscopic (functional or population), or macroscopic (system) levels.
With few exceptions, each neuron in the brain releases the same chemical neurotransmitter, or combination of neurotransmitters, at all the synaptic connections it makes with other neurons; this rule is known as Dale's principle. [8] Thus, a neuron can be characterized by the neurotransmitters that it releases.
A biological neural network is composed of a group of chemically connected or functionally associated neurons. [2] A single neuron may be connected to many other neurons and the total number of neurons and connections in a network may be extensive.
In the early 2000s, the field of neuroimaging reached the stage where limited practical applications of functional brain imaging have become feasible. The main application area is crude forms of brain–computer interface. The world record for the spatial resolution of a whole-brain MRI image was a 100-micrometer volume (image) achieved in 2019.
It is suggested that aesthetic experience is a function of the interaction between top-down, intentional orientation of attention and the bottom-up perceptual facilitation of image construction. [23] In other words, because untrained persons automatically apply the object-identification habit to viewing artworks, top-down control to reduce this ...
Biological neuron models, also known as spiking neuron models, [1] are mathematical descriptions of the conduction of electrical signals in neurons. Neurons (or nerve cells) are electrically excitable cells within the nervous system , able to fire electric signals, called action potentials , across a neural network.