Integrating model development across computational neuroscience, cognitive science, and machine learning

被引：2

作者：

Gleeson, Padraig ^{[1
]}

Crook, Sharon ^{[2
]}

Turner, David ^{[3
]}

Mantel, Katherine ^{[4
]}

Raunak, Mayank ^{[5
]}

Willke, Ted ^{[5
]}

Cohen, Jonathan D. ^{[4
]}

机构：

[1] UCL, Dept Neurosci, Physiol & Pharmacol, London, England

[2] Arizona State Univ, Sch Math & Stat Sci, Tempe, AZ USA

[3] Princeton Univ, Princeton Inst Computat Sci & Engn, Princeton, NJ USA

[4] Princeton Univ, Princeton Neurosci Inst, Princeton, NJ 08544 USA

[5] Intel Corp, Intel Labs, Hillsboro, OR USA

来源：

NEURON | 2023年 / 111卷 / 10期

关键词：

FORMAT;

D O I：

10.1016/j.neuron.2023.03.037

中图分类号：

Q189 [神经科学];

学科分类号：

071006 ;

摘要：

Neuroscience, cognitive science, and computer science are increasingly benefiting through their interac-tions. This could be accelerated by direct sharing of computational models across disparate modeling soft-ware used in each. We describe a Model Description Format designed to meet this challenge.

引用

页码：1526 / 1530

页数：5

共 50 条

[41] Theory of Mind and Preference Learning at the Interface of Cognitive Science, Neuroscience, and AI: A Review
Langley, Christelle
Cirstea, Bogdan Ionut
Cuzzolin, Fabio
Sahakian, Barbara J.
FRONTIERS IN ARTIFICIAL INTELLIGENCE, 2022, 5
[42] Model-Based Cognitive Neuroscience Approaches to Computational Psychiatry: Clustering and Classification
Wiecki, Thomas V.
Poland, Jeffrey
Frank, Michael J.
CLINICAL PSYCHOLOGICAL SCIENCE, 2015, 3 (03) : 378 - 399
[43] Towards Replication in Computational Cognitive Modeling: a Machine Learning Perspective
Emmery C.
Kádár Á.
Wiltshire T.J.
Hendrickson A.T.
Computational Brain & Behavior, 2019, 2 (3-4) : 242 - 246
[44] Provenance Data in the Machine Learning Lifecycle in Computational Science and Engineering
Souza, Renan
Azevedo, Leonardo
Lourenco, Vitor
Soares, Elton
Thiago, Raphael
Brandao, Rafael
Civitarese, Daniel
Brazil, Emilio Vital
Moreno, Marcio
Valduriez, Patrick
Mattoso, Marta
Cerqueira, Renato
Netto, Marco A. S.
PROCEEDINGS OF WORKS19: THE 2019 14TH IEEE/ACM WORKFLOWS IN SUPPORT OF LARGE-SCALE SCIENCE (WORKS), 2019, : 1 - 10
[45] Integrating Reinforcement Learning, Equilibrium Points, and Minimum Variance to Understand the Development of Reaching: A Computational Model
Caligiore, Daniele
Parisi, Domenico
Baldassarre, Gianluca
PSYCHOLOGICAL REVIEW, 2014, 121 (03) : 389 - 421
[46] Beyond the "Conceptual Nervous System": Can computational cognitive neuroscience transform learning theory?
Soto, Fabian A.
BEHAVIOURAL PROCESSES, 2019, 167
[47] Early Brain Development for Social Work Practice: Integrating Neuroscience with Piaget's Theory of Cognitive Development
Lefmann, Tess
Combs-Orme, Terri
JOURNAL OF HUMAN BEHAVIOR IN THE SOCIAL ENVIRONMENT, 2013, 23 (05) : 640 - 647
[48] IMPACT OF COGNITIVE NEUROSCIENCE IN CORPORATE LEARNING AND DEVELOPMENT FOR SUSTAINABLE COMPETITIVE ADVANTAGE
Jose, Julie
Durai, F. R. Alexander Pravin
INTERNATIONAL JOURNAL OF EARLY CHILDHOOD SPECIAL EDUCATION, 2022, 14 (02) : 2308 - 2311
[49] Bridging across cognitive training and brain plasticity: a neurally inspired computational model of interactive skill learning
Fu, Wai-Tat
Lee, Hyunkyu
Boot, Walter R.
Kramer, Arthur F.
WILEY INTERDISCIPLINARY REVIEWS-COGNITIVE SCIENCE, 2013, 4 (02) : 225 - 236
[50] Advancing social behavioral neuroscience by integrating ethology and comparative psychology methods through machine learning
Bordes, Joeri
Miranda, Lucas
Mueller-Myhsok, Bertram
Schmidt, Mathias V.
NEUROSCIENCE AND BIOBEHAVIORAL REVIEWS, 2023, 151

← 1 2 3 4 5 →