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The free energy principle is a theoretical framework suggesting that the brain reduces surprise or uncertainty by making predictions based on internal models and updating them using sensory input. It highlights the brain's objective of aligning its internal model and the external world to enhance prediction accuracy .
This model is the Integrate-and-Fire (IF) model that was mentioned in Section 2.3. Closely related to IF model is a model called Spike Response Model (SRM) (Gerstner, W. (1995) [15] Pages 738-758) that is dependent on impulse function response convoluted with the input stimulus signal. This forms a base for a large number of models developed ...
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.
Stanislas Dehaene extended the global workspace with the "neuronal avalanche" showing how sensory information gets selected to be broadcast throughout the cortex. [12] Many brain regions, the prefrontal cortex, anterior temporal lobe, inferior parietal lobe, and the precuneus all send and receive numerous projections to and from a broad variety of distant brain regions, allowing the neurons ...
The atlas was created from the brain of an unidentified 65-year-old man (it was "65-year-old female", according to "BigBrain: An Ultrahigh-Resolution3D Human Brain Model", page 1472, Amunts K et al., SCIENCE, 21 JUNE 2013 VOL 340) who died with no known brain pathology.
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Approaches for integration are diverse. [10] Henry Kautz's taxonomy of neuro-symbolic architectures [11] follows, along with some examples: . Symbolic Neural symbolic is the current approach of many neural models in natural language processing, where words or subword tokens are the ultimate input and output of large language models.