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Universal principles justify the existence of concept cells.


ABSTRACT: 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.

SUBMITTER: Calvo Tapia C 

PROVIDER: S-EPMC7217959 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Universal principles justify the existence of concept cells.

Calvo Tapia Carlos C   Tyukin Ivan I   Makarov Valeri A VA  

Scientific reports 20200512 1


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 recen  ...[more]

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