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The reiterative nature of the cerebral cortex, in the sense that it is a vast array of repeating functional circuits, led to the idea that cortical evolution is governed by mechanisms regulating the addition of cortical columns, enabling additional functional areas to become specialized and incorporated into the brain.
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.
The basic idea at the core of the soliton model is the balancing of intrinsic dispersion of the two dimensional sound waves in the membrane by nonlinear elastic properties near a phase transition. The initial impulse can acquire a stable shape under such circumstances, in general known as a solitary wave. [12]
Computational neuroscience is an interdisciplinary field that combines engineering, biology, control systems, brain functions, physical sciences, and computer science. It has fundamental development models done at the lower levels of ions, neurons, and synapses, as well as information propagation between neurons.
Computational models provide a base form of brain-activity level, which is typically represented by the firing of a single neuron. This is essential for understanding systems neuroscience as it shows the physical changes that occur during functional changes in an organism.
The dynamical systems approach to neuroscience is a branch of mathematical biology that utilizes nonlinear dynamics to understand and model the nervous system and its functions. In a dynamical system, all possible states are expressed by a phase space . [ 1 ]
The theta model, or Ermentrout–Kopell canonical Type I model, is mathematically equivalent to the quadratic integrate-and-fire model which in turn is an approximation to the exponential integrate-and-fire model and the Hodgkin-Huxley model. It is called a canonical model because it is one of the generic models for constant input close to the ...
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.