Thinking about the brain

Bialek, W.

doi:10.1007/3-540-45701-1_12

Cited by 16Neural dynamics and brain functionNeural Networks and ApplicationsCognitive Science and Education Research

Thinking about the brain

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Bialek, W.·2007·Les Houches - Ecole d’Ete de Physique Theorique

DOI 10.1007/3-540-45701-1_12

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Many top-down frameworks for understanding neural computation boil down to assuming that the brain simply optimizes a single, given cost function for a single computational architecture.
“Interestingly, many of the “top down” frameworks boil down to assuming that the brain simply optimizes a single, given cost function for a single computational architecture.”
From Toward an integration of deep learning and neuroscience (2016) · cites this paper as Bialek, 2002

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Information Capacity and Robustness of Stochastic Neuron Models
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Toward an integration of deep learning and neuroscience
Marblestone, A. H., Wayne, G., & Kording, K. P. (2016) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2016.00094 · Cited by 119 · see citing works

Authors: Bialek, W.
Affiliations: NEC (Japan) (JP)
Corresponding author: Bialek, W.
ORCID: Bialek, W.
Venue: Les Houches - Ecole d’Ete de Physique Theorique
Publisher: Springer Berlin Heidelberg
Published: 2007-08-15
Pages: 485-578
DOI: 10.1007/3-540-45701-1_12
OpenAlex: W1508923040
Cited by: 16
References: 132
Citations by year: 2024: 1 · 2016: 2 · 2015: 1 · 2013: 1 · 2012: 1
FWCI: 1.63
Type: book chapter
Language: en
Open access: green
arxiv.org
Locations: Springer eBooks (publishedVersion)
arXiv (Cornell University) (submittedVersion)
CERN Document Server (European Organization for Nuclear Research) (submittedVersion)
arXiv (Cornell University)
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
Life Sciences › Neuroscience › Cognitive Neuroscience › Cognitive Science and Education Research
Keywords: brain computation, neural circuits, optimization principle, statistical limits, coding strategies, learning complexity, model complexity, data complexity, intelligent behavior

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