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In the field of computational neuroscience, brain simulation is the concept of creating a functioning computer model of a brain or part of a brain. [1] Brain simulation projects intend to contribute to a complete understanding of the brain, and eventually also assist the process of treating and diagnosing brain diseases .
As early as the 1860s, with the work of Hermann Helmholtz in experimental psychology, the brain's ability to extract perceptual information from sensory data was modeled in terms of probabilistic estimation. [5] [6] The basic idea is that the nervous system needs to organize sensory data into an accurate internal model of the outside world.
Computational neuroscience (also known as theoretical neuroscience or mathematical neuroscience) is a branch of neuroscience which employs mathematics, computer science, theoretical analysis and abstractions of the brain to understand the principles that govern the development, structure, physiology and cognitive abilities of the nervous system.
Adaptive resonance theory (ART) is a theory developed by Stephen Grossberg and Gail Carpenter on aspects of how the brain processes information.It describes a number of artificial neural network models which use supervised and unsupervised learning methods, and address problems such as pattern recognition and prediction.
Voxel-based morphometry is a computational approach to neuroanatomy that measures differences in local concentrations of brain tissue, through a voxel-wise comparison of multiple brain images. [ 1 ] [ 2 ] In traditional morphometry , volume of the whole brain or its subparts is measured by drawing regions of interest (ROIs) on images from brain ...
One of the core architectures in brain network models is the "small-world" architecture. It interprets models to be regular networks, while they occasionally experience random activity. In small-world networks, the clustering coefficient (i.e., transitivity) is high, and the average path distance is short.
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This model was simplified with the FitzHugh–Nagumo model in 1962. [9] By 1981, the Morris–Lecar model had been developed for the barnacle muscle. These mathematical models proved useful and are still used by the field of biophysics today, but a late 20th century development propelled the dynamical study of neurons even further: computer ...