Artificial Neural Networks for Neuroscientists: A Primer

Artificial neural networks (ANNs) are essential tools in machine learning that have drawn increasing attention in neuroscience. Besides offering powerful techniques for data analysis, ANNs provide a new approach for neuroscientists to build models for complex behaviors, heterogeneous neural activity...

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Published inNeuron (Cambridge, Mass.) Vol. 107; no. 6; pp. 1048 - 1070
Main Authors Yang, Guangyu Robert, Wang, Xiao-Jing
Format Journal Article
LanguageEnglish
Published United States Elsevier Inc 23.09.2020
Elsevier Limited
Subjects
Algorithms
Animal cognition
Animals
Architecture
Artificial intelligence
Attention
Brain - physiology
Brain research
Deep learning
Humans
Ingredients
Learning algorithms
Machine learning
Mathematical models
Models, Neurological
Nervous system
Neural networks
Neural Networks, Computer
Neurophysiology
Neurosciences
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Abstract Artificial neural networks (ANNs) are essential tools in machine learning that have drawn increasing attention in neuroscience. Besides offering powerful techniques for data analysis, ANNs provide a new approach for neuroscientists to build models for complex behaviors, heterogeneous neural activity, and circuit connectivity, as well as to explore optimization in neural systems, in ways that traditional models are not designed for. In this pedagogical Primer, we introduce ANNs and demonstrate how they have been fruitfully deployed to study neuroscientific questions. We first discuss basic concepts and methods of ANNs. Then, with a focus on bringing this mathematical framework closer to neurobiology, we detail how to customize the analysis, structure, and learning of ANNs to better address a wide range of challenges in brain research. To help readers garner hands-on experience, this Primer is accompanied with tutorial-style code in PyTorch and Jupyter Notebook, covering major topics. Artificial neural networks (ANNs) are essential tools in modern machine learning. In this Primer, Yang and Wang introduce how new computational models based on ANNs can be built, analyzed, and customized to study a wide range of neuroscientific questions.
AbstractList Artificial neural networks (ANNs) are essential tools in machine learning that have drawn increasing attention in neuroscience. Besides offering powerful techniques for data analysis, ANNs provide a new approach for neuroscientists to build models for complex behaviors, heterogeneous neural activity, and circuit connectivity, as well as to explore optimization in neural systems, in ways that traditional models are not designed for. In this pedagogical Primer, we introduce ANNs and demonstrate how they have been fruitfully deployed to study neuroscientific questions. We first discuss basic concepts and methods of ANNs. Then, with a focus on bringing this mathematical framework closer to neurobiology, we detail how to customize the analysis, structure, and learning of ANNs to better address a wide range of challenges in brain research. To help readers garner hands-on experience, this Primer is accompanied with tutorial-style code in PyTorch and Jupyter Notebook, covering major topics.Artificial neural networks (ANNs) are essential tools in machine learning that have drawn increasing attention in neuroscience. Besides offering powerful techniques for data analysis, ANNs provide a new approach for neuroscientists to build models for complex behaviors, heterogeneous neural activity, and circuit connectivity, as well as to explore optimization in neural systems, in ways that traditional models are not designed for. In this pedagogical Primer, we introduce ANNs and demonstrate how they have been fruitfully deployed to study neuroscientific questions. We first discuss basic concepts and methods of ANNs. Then, with a focus on bringing this mathematical framework closer to neurobiology, we detail how to customize the analysis, structure, and learning of ANNs to better address a wide range of challenges in brain research. To help readers garner hands-on experience, this Primer is accompanied with tutorial-style code in PyTorch and Jupyter Notebook, covering major topics.
Artificial neural networks (ANNs) are essential tools in machine learning that have drawn increasing attention in neuroscience. Besides offering powerful techniques for data analysis, ANNs provide a new approach for neuroscientists to build models for complex behaviors, heterogeneous neural activity, and circuit connectivity, as well as to explore optimization in neural systems, in ways that traditional models are not designed for. In this pedagogical Primer, we introduce ANNs and demonstrate how they have been fruitfully deployed to study neuroscientific questions. We first discuss basic concepts and methods of ANNs. Then, with a focus on bringing this mathematical framework closer to neurobiology, we detail how to customize the analysis, structure, and learning of ANNs to better address a wide range of challenges in brain research. To help readers garner hands-on experience, this Primer is accompanied with tutorial-style code in PyTorch and Jupyter Notebook, covering major topics.
Artificial neural networks (ANNs) are essential tools in machine learning that have drawn increasing attention in neuroscience. Besides offering powerful techniques for data analysis, ANNs provide a new approach for neuroscientists to build models for complex behaviors, heterogeneous neural activity, and circuit connectivity, as well as to explore optimization in neural systems, in ways that traditional models are not designed for. In this pedagogical Primer, we introduce ANNs and demonstrate how they have been fruitfully deployed to study neuroscientific questions. We first discuss basic concepts and methods of ANNs. Then, with a focus on bringing this mathematical framework closer to neurobiology, we detail how to customize the analysis, structure, and learning of ANNs to better address a wide range of challenges in brain research. To help readers garner hands-on experience, this Primer is accompanied with tutorial-style code in PyTorch and Jupyter Notebook, covering major topics. Artificial neural networks (ANNs) are essential tools in modern machine learning. In this Primer, Yang and Wang introduce how new computational models based on ANNs can be built, analyzed, and customized to study a wide range of neuroscientific questions.
SummaryArtificial neural networks (ANNs) are essential tools in machine learning that have drawn increasing attention in neuroscience. Besides offering powerful techniques for data analysis, ANNs provide a new approach for neuroscientists to build models for complex behaviors, heterogeneous neural activity, and circuit connectivity, as well as to explore optimization in neural systems, in ways that traditional models are not designed for. In this pedagogical Primer, we introduce ANNs and demonstrate how they have been fruitfully deployed to study neuroscientific questions. We first discuss basic concepts and methods of ANNs. Then, with a focus on bringing this mathematical framework closer to neurobiology, we detail how to customize the analysis, structure, and learning of ANNs to better address a wide range of challenges in brain research. To help readers garner hands-on experience, this Primer is accompanied with tutorial-style code in PyTorch and Jupyter Notebook, covering major topics.
Author Wang, Xiao-Jing
Yang, Guangyu Robert
AuthorAffiliation 2 Center for Neural Science, New York University, New York, NY, USA
1 Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
AuthorAffiliation_xml – name: 2 Center for Neural Science, New York University, New York, NY, USA
– name: 1 Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
Author_xml – sequence: 1
  givenname: Guangyu Robert
  surname: Yang
  fullname: Yang, Guangyu Robert
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  organization: Center for Theoretical Neuroscience, Columbia University, New York, NY, USA
– sequence: 2
  givenname: Xiao-Jing
  orcidid: 0000-0003-3124-8474
  surname: Wang
  fullname: Wang, Xiao-Jing
  email: xjwang@nyu.edu
  organization: Center for Neural Science, New York University, New York, NY, USA
BackLink https://www.ncbi.nlm.nih.gov/pubmed/32970997$$D View this record in MEDLINE/PubMed
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Snippet Artificial neural networks (ANNs) are essential tools in machine learning that have drawn increasing attention in neuroscience. Besides offering powerful...
SummaryArtificial neural networks (ANNs) are essential tools in machine learning that have drawn increasing attention in neuroscience. Besides offering...
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SubjectTerms Algorithms
Animal cognition
Animals
Architecture
Artificial intelligence
Attention
Brain - physiology
Brain research
Deep learning
Humans
Ingredients
Learning algorithms
Machine learning
Mathematical models
Models, Neurological
Nervous system
Neural networks
Neural Networks, Computer
Neurophysiology
Neurosciences
Title Artificial Neural Networks for Neuroscientists: A Primer
URI https://dx.doi.org/10.1016/j.neuron.2020.09.005
https://www.ncbi.nlm.nih.gov/pubmed/32970997
https://www.proquest.com/docview/2445353444
https://www.proquest.com/docview/2446662903
https://pubmed.ncbi.nlm.nih.gov/PMC11576090
Volume 107
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