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Challenges in neuroscience : a special issue of the journal Science

Credit : AAAS Sciencemag.org



REVIEW : Big data and the industrialization of neuroscience :
A safe roadmap for understanding the brain ?

Yves Frégnac (Synaptic integration and functional plasticity in primary visual cortex, UNIC)

New technologies in neuroscience generate reams of data at an exponentially increasing rate, spurring the design of very-large-scale data-mining initiatives. Several supranational ventures are contemplating the possibility of achieving, within the next decade(s), full simulation of the human brain.

This review questions here the scientific and strategic underpinnings of the runaway enthusiasm for industrial-scale projects at the interface between “wet” (biology) and “hard” (physics, microelectronics and computer science) sciences.
It focuses on three major issues :
(i) Is the industrialization of neuroscience the soundest way to achieve substantial progress in knowledge about the brain ?
(ii) Do we have a safe “roadmap,” based on a scientific consensus ?
(iii) Do these large-scale approaches guarantee that we will reach a better understanding of the brain ?

Science. 2017 Oct 27 ; 358(6362):470-477. doi : 10.1126/science.aan8866
PubMed PMID : 29074766


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What is consciousness, and could machines have it ?

Stanislas Dehaene (Cognitive Neuroimaging Unit, NeuroSpin, CEA-Saclay),
Hakwan Lau, Sid Kouider

The controversial question of whether machines may ever be conscious must be based on a careful consideration of how consciousness arises in the only physical system that undoubtedly possesses it : the human brain.
We suggest that the word "consciousness" conflates two different types of information-processing computations in the brain : the selection of information for global broadcasting, thus making it flexibly available for computation and report (C1, consciousness in the first sense), and the self-monitoring of those computations, leading to a subjective sense of certainty or error (C2, consciousness in the second sense).
We argue that despite their recent successes, current machines are still mostly implementing computations that reflect unconscious processing (C0) in the human brain. We review the psychological and neural science of unconscious (C0) and conscious computations (C1 and C2) and outline how they may inspire novel machine architectures.

Science. 2017 Oct 27 ; 358(6362):486-492. doi : 10.1126/science.aan8871
PubMed PMID : 29074769
- science.sciencemag.org


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See also table of Contents - Science, October 27, 2017, 358 (6362)
- science.sciencemag.org/content/358/6362

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