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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.
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 .
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 ...
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 ...
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.
This architecture is an online machine learning model developed by Jeff Hawkins and Dileep George of Numenta, Inc. that models some of the structural and algorithmic properties of the neocortex. HTM is a biomimetic model based on the memory-prediction theory of brain function described by Jeff Hawkins in his book On Intelligence. HTM is a ...
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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.