A deep learning framework for neuroscience.
Saved in:
| Title: | A deep learning framework for neuroscience. |
|---|---|
| Authors: | Richards, Blake A. (AUTHOR), Lillicrap, Timothy P. (AUTHOR), Beaudoin, Philippe (AUTHOR), Bengio, Yoshua (AUTHOR), Bogacz, Rafal (AUTHOR), Christensen, Amelia (AUTHOR), Clopath, Claudia (AUTHOR), Costa, Rui Ponte (AUTHOR), de Berker, Archy (AUTHOR), Ganguli, Surya (AUTHOR), Gillon, Colleen J. (AUTHOR), Hafner, Danijar (AUTHOR), Kepecs, Adam (AUTHOR), Kriegeskorte, Nikolaus (AUTHOR), Latham, Peter (AUTHOR), Lindsay, Grace W. (AUTHOR), Miller, Kenneth D. (AUTHOR), Naud, Richard (AUTHOR), Pack, Christopher C. (AUTHOR), Poirazi, Panayiota (AUTHOR) |
| Source: | Nature Neuroscience. Nov2019, Vol. 22 Issue 11, p1761-1770. 10p. 1 Color Photograph, 4 Diagrams. |
| Abstract: | Systems neuroscience seeks explanations for how the brain implements a wide variety of perceptual, cognitive and motor tasks. Conversely, artificial intelligence attempts to design computational systems based on the tasks they will have to solve. In artificial neural networks, the three components specified by design are the objective functions, the learning rules and the architectures. With the growing success of deep learning, which utilizes brain-inspired architectures, these three designed components have increasingly become central to how we model, engineer and optimize complex artificial learning systems. Here we argue that a greater focus on these components would also benefit systems neuroscience. We give examples of how this optimization-based framework can drive theoretical and experimental progress in neuroscience. We contend that this principled perspective on systems neuroscience will help to generate more rapid progress. A deep network is best understood in terms of components used to design it—objective functions, architecture and learning rules—rather than unit-by-unit computation. Richards et al. argue that this inspires fruitful approaches to systems neuroscience. [ABSTRACT FROM AUTHOR] |
| Copyright of Nature Neuroscience is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
| FullText | Text: Availability: 0 |
|---|---|
| Header | DbId: pbh DbLabel: Psychology and Behavioral Sciences Collection An: 139365505 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: A deep learning framework for neuroscience. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Richards%2C+Blake+A%2E%22">Richards, Blake A.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lillicrap%2C+Timothy+P%2E%22">Lillicrap, Timothy P.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Beaudoin%2C+Philippe%22">Beaudoin, Philippe</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bengio%2C+Yoshua%22">Bengio, Yoshua</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bogacz%2C+Rafal%22">Bogacz, Rafal</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Christensen%2C+Amelia%22">Christensen, Amelia</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Clopath%2C+Claudia%22">Clopath, Claudia</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Costa%2C+Rui+Ponte%22">Costa, Rui Ponte</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22de+Berker%2C+Archy%22">de Berker, Archy</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ganguli%2C+Surya%22">Ganguli, Surya</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gillon%2C+Colleen+J%2E%22">Gillon, Colleen J.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hafner%2C+Danijar%22">Hafner, Danijar</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kepecs%2C+Adam%22">Kepecs, Adam</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kriegeskorte%2C+Nikolaus%22">Kriegeskorte, Nikolaus</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Latham%2C+Peter%22">Latham, Peter</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lindsay%2C+Grace+W%2E%22">Lindsay, Grace W.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Miller%2C+Kenneth+D%2E%22">Miller, Kenneth D.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Naud%2C+Richard%22">Naud, Richard</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pack%2C+Christopher+C%2E%22">Pack, Christopher C.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Poirazi%2C+Panayiota%22">Poirazi, Panayiota</searchLink> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Nature+Neuroscience%22">Nature Neuroscience</searchLink>. Nov2019, Vol. 22 Issue 11, p1761-1770. 10p. 1 Color Photograph, 4 Diagrams. – Name: Abstract Label: Abstract Group: Ab Data: Systems neuroscience seeks explanations for how the brain implements a wide variety of perceptual, cognitive and motor tasks. Conversely, artificial intelligence attempts to design computational systems based on the tasks they will have to solve. In artificial neural networks, the three components specified by design are the objective functions, the learning rules and the architectures. With the growing success of deep learning, which utilizes brain-inspired architectures, these three designed components have increasingly become central to how we model, engineer and optimize complex artificial learning systems. Here we argue that a greater focus on these components would also benefit systems neuroscience. We give examples of how this optimization-based framework can drive theoretical and experimental progress in neuroscience. We contend that this principled perspective on systems neuroscience will help to generate more rapid progress. A deep network is best understood in terms of components used to design it—objective functions, architecture and learning rules—rather than unit-by-unit computation. Richards et al. argue that this inspires fruitful approaches to systems neuroscience. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Nature Neuroscience is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=139365505 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1038/s41593-019-0520-2 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 1761 Titles: – TitleFull: A deep learning framework for neuroscience. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Richards, Blake A. – PersonEntity: Name: NameFull: Lillicrap, Timothy P. – PersonEntity: Name: NameFull: Beaudoin, Philippe – PersonEntity: Name: NameFull: Bengio, Yoshua – PersonEntity: Name: NameFull: Bogacz, Rafal – PersonEntity: Name: NameFull: Christensen, Amelia – PersonEntity: Name: NameFull: Clopath, Claudia – PersonEntity: Name: NameFull: Costa, Rui Ponte – PersonEntity: Name: NameFull: de Berker, Archy – PersonEntity: Name: NameFull: Ganguli, Surya – PersonEntity: Name: NameFull: Gillon, Colleen J. – PersonEntity: Name: NameFull: Hafner, Danijar – PersonEntity: Name: NameFull: Kepecs, Adam – PersonEntity: Name: NameFull: Kriegeskorte, Nikolaus – PersonEntity: Name: NameFull: Latham, Peter – PersonEntity: Name: NameFull: Lindsay, Grace W. – PersonEntity: Name: NameFull: Miller, Kenneth D. – PersonEntity: Name: NameFull: Naud, Richard – PersonEntity: Name: NameFull: Pack, Christopher C. – PersonEntity: Name: NameFull: Poirazi, Panayiota IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2019 Type: published Y: 2019 Identifiers: – Type: issn-print Value: 10976256 Numbering: – Type: volume Value: 22 – Type: issue Value: 11 Titles: – TitleFull: Nature Neuroscience Type: main |
| ResultId | 1 |
