Sparse coding with memristor networks
Sparse representation of information provides a powerful means to perform feature extraction on high-dimensional data and is of broad interest for applications in signal processing, computer vision, object recognition and neurobiology. Sparse coding is also believed to be a key mechanism by which bi...
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| Veröffentlicht in: | Nature nanotechnology Jg. 12; H. 8; S. 784 - 789 |
|---|---|
| Hauptverfasser: | , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
London
Nature Publishing Group UK
01.08.2017
Nature Publishing Group |
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| ISSN: | 1748-3387, 1748-3395, 1748-3395 |
| Online-Zugang: | Volltext |
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| Abstract | Sparse representation of information provides a powerful means to perform feature extraction on high-dimensional data and is of broad interest for applications in signal processing, computer vision, object recognition and neurobiology. Sparse coding is also believed to be a key mechanism by which biological neural systems can efficiently process a large amount of complex sensory data while consuming very little power. Here, we report the experimental implementation of sparse coding algorithms in a bio-inspired approach using a 32 × 32 crossbar array of analog memristors. This network enables efficient implementation of pattern matching and lateral neuron inhibition and allows input data to be sparsely encoded using neuron activities and stored dictionary elements. Different dictionary sets can be trained and stored in the same system, depending on the nature of the input signals. Using the sparse coding algorithm, we also perform natural image processing based on a learned dictionary.
The implementation of bio-inspired sparse coding algorithms aimed at image processing is demonstrated by exploiting 32 × 32 crossbar arrays of analogue memristors. |
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| AbstractList | Sparse representation of information provides a powerful means to perform feature extraction on high-dimensional data and is of broad interest for applications in signal processing, computer vision, object recognition and neurobiology. Sparse coding is also believed to be a key mechanism by which biological neural systems can efficiently process a large amount of complex sensory data while consuming very little power. Here, we report the experimental implementation of sparse coding algorithms in a bio-inspired approach using a 32 × 32 crossbar array of analog memristors. This network enables efficient implementation of pattern matching and lateral neuron inhibition and allows input data to be sparsely encoded using neuron activities and stored dictionary elements. Different dictionary sets can be trained and stored in the same system, depending on the nature of the input signals. Using the sparse coding algorithm, we also perform natural image processing based on a learned dictionary. Sparse representation of information provides a powerful means to perform feature extraction on high-dimensional data and is of broad interest for applications in signal processing, computer vision, object recognition and neurobiology. Sparse coding is also believed to be a key mechanism by which biological neural systems can efficiently process a large amount of complex sensory data while consuming very little power. Here, we report the experimental implementation of sparse coding algorithms in a bio-inspired approach using a 32 × 32 crossbar array of analog memristors. This network enables efficient implementation of pattern matching and lateral neuron inhibition and allows input data to be sparsely encoded using neuron activities and stored dictionary elements. Different dictionary sets can be trained and stored in the same system, depending on the nature of the input signals. Using the sparse coding algorithm, we also perform natural image processing based on a learned dictionary. The implementation of bio-inspired sparse coding algorithms aimed at image processing is demonstrated by exploiting 32 × 32 crossbar arrays of analogue memristors. Sparse representation of information provides a powerful means to perform feature extraction on high-dimensional data and is of broad interest for applications in signal processing, computer vision, object recognition and neurobiology. Sparse coding is also believed to be a key mechanism by which biological neural systems can efficiently process a large amount of complex sensory data while consuming very little power. Here, we report the experimental implementation of sparse coding algorithms in a bio-inspired approach using a 32 × 32 crossbar array of analog memristors. This network enables efficient implementation of pattern matching and lateral neuron inhibition and allows input data to be sparsely encoded using neuron activities and stored dictionary elements. Different dictionary sets can be trained and stored in the same system, depending on the nature of the input signals. Using the sparse coding algorithm, we also perform natural image processing based on a learned dictionary.Sparse representation of information provides a powerful means to perform feature extraction on high-dimensional data and is of broad interest for applications in signal processing, computer vision, object recognition and neurobiology. Sparse coding is also believed to be a key mechanism by which biological neural systems can efficiently process a large amount of complex sensory data while consuming very little power. Here, we report the experimental implementation of sparse coding algorithms in a bio-inspired approach using a 32 × 32 crossbar array of analog memristors. This network enables efficient implementation of pattern matching and lateral neuron inhibition and allows input data to be sparsely encoded using neuron activities and stored dictionary elements. Different dictionary sets can be trained and stored in the same system, depending on the nature of the input signals. Using the sparse coding algorithm, we also perform natural image processing based on a learned dictionary. |
| Author | Zhang, Zhengya Lu, Wei D. Du, Chao Ma, Wen Cai, Fuxi Sheridan, Patrick M. |
| Author_xml | – sequence: 1 givenname: Patrick M. surname: Sheridan fullname: Sheridan, Patrick M. organization: Department of Electrical Engineering and Computer Science, University of Michigan – sequence: 2 givenname: Fuxi surname: Cai fullname: Cai, Fuxi organization: Department of Electrical Engineering and Computer Science, University of Michigan – sequence: 3 givenname: Chao surname: Du fullname: Du, Chao organization: Department of Electrical Engineering and Computer Science, University of Michigan – sequence: 4 givenname: Wen surname: Ma fullname: Ma, Wen organization: Department of Electrical Engineering and Computer Science, University of Michigan – sequence: 5 givenname: Zhengya surname: Zhang fullname: Zhang, Zhengya organization: Department of Electrical Engineering and Computer Science, University of Michigan – sequence: 6 givenname: Wei D. orcidid: 0000-0003-4731-1976 surname: Lu fullname: Lu, Wei D. email: wluee@eecs.umich.edu organization: Department of Electrical Engineering and Computer Science, University of Michigan |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28530717$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | 142/126 639/166/987 639/925/927/1007 Algorithms Coding Computer vision Data processing Dictionaries Feature extraction Image processing Materials Science Memristors Nanotechnology Nanotechnology and Microengineering Nervous system Neural coding Neurosciences Object recognition Pattern matching Pattern recognition Power consumption Signal processing |
| Title | Sparse coding with memristor networks |
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