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Universal principles justify the existence of concept cells.
Calvo Tapia, Carlos; Tyukin, Ivan; Makarov, Valeri A.
Afiliación
  • Calvo Tapia C; Instituto de Matemática Interdisciplinar, Faculty of Mathematics, Universidad Complutense de Madrid, Plaza de Ciencias 3, Madrid, 28040, Spain.
  • Tyukin I; University of Leicester, Department of Mathematics, University Road, LE1 7RH, United Kingdom.
  • Makarov VA; Instituto de Matemática Interdisciplinar, Faculty of Mathematics, Universidad Complutense de Madrid, Plaza de Ciencias 3, Madrid, 28040, Spain. vmakarov@ucm.es.
Sci Rep ; 10(1): 7889, 2020 05 12.
Article en En | MEDLINE | ID: mdl-32398873
The widespread consensus argues that the emergence of abstract concepts in the human brain, such as a "table", requires complex, perfectly orchestrated interaction of myriads of neurons. However, this is not what converging experimental evidence suggests. Single neurons, the so-called concept cells (CCs), may be responsible for complex tasks performed by humans. This finding, with deep implications for neuroscience and theory of neural networks, has no solid theoretical grounds so far. Our recent advances in stochastic separability of highdimensional data have provided the basis to validate the existence of CCs. Here, starting from a few first principles, we layout biophysical foundations showing that CCs are not only possible but highly likely in brain structures such as the hippocampus. Three fundamental conditions, fulfilled by the human brain, ensure high cognitive functionality of single cells: a hierarchical feedforward organization of large laminar neuronal strata, a suprathreshold number of synaptic entries to principal neurons in the strata, and a magnitude of synaptic plasticity adequate for each neuronal stratum. We illustrate the approach on a simple example of acquiring "musical memory" and show how the concept of musical notes can emerge.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Modelos Neurológicos / Plasticidad Neuronal / Neuronas Tipo de estudio: Prognostic_studies Límite: Animals / Humans Idioma: En Revista: Sci Rep Año: 2020 Tipo del documento: Article País de afiliación: España Pais de publicación: Reino Unido

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Modelos Neurológicos / Plasticidad Neuronal / Neuronas Tipo de estudio: Prognostic_studies Límite: Animals / Humans Idioma: En Revista: Sci Rep Año: 2020 Tipo del documento: Article País de afiliación: España Pais de publicación: Reino Unido