The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Real-time classification and sensor fusion with a spiking deep belief network
Open accessAbstract
Deep Belief Networks (DBNs) have recently shown impressive performance on a broad range of classification problems. Their generative properties allow better understanding of the performance, and provide a simpler solution for sensor fusion tasks. However, because of their inherent need for feedback and parallel update of large numbers of units, DBNs are expensive to implement on serial computers. This paper proposes a method based on the Siegert approximation for Integrate-and-Fire neurons to map an offline-trained DBN onto an efficient event-driven spiking neural network suitable for hardware implementation. The method is demonstrated in simulation and by a real-time implementation of a 3-layer network with 2694 neurons used for visual classification of MNIST handwritten digits with input from a 128 × 128 Dynamic Vision Sensor (DVS) silicon retina, and sensory-fusion using additional input from a 64-channel AER-EAR silicon cochlea. The system is implemented through the open-source software in the jAER project and runs in real-time on a laptop computer. It is demonstrated that the system can recognize digits in the presence of distractions, noise, scaling, translation and rotation, and that the degradation of recognition performance by using an event-based approach is less than 1%. Recognition is achieved in an average of 5.8 ms after the onset of the presentation of a digit. By cue integration from both silicon retina and cochlea outputs we show that the system can be biased to select the correct digit from otherwise ambiguous input.
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Cites
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Cited by
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Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing
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Self-citationHATS: Histograms of Averaged Time Surfaces for Robust Event-Based Object Classification
Sironi, A., Brambilla, M., Bourdis, N., Lagorce, X., & Benosman, R. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00186 · Cited by 486
Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences
Neil, D., Pfeiffer, M., & Liu, S. (2016) · Zurich Open Repository and Archive (University of Zurich) · doi:10.48550/arxiv.1610.09513 · Cited by 319
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Metadata
- Authors
- O'Connor, P., Neil, D., Liu, S.-C., Delbruck, T., & Pfeiffer, M.
- Venue
- Frontiers in Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2013
- Volume
- 7
- Pages
- 178-178
- ISSN
- 1662-453X (ISSN-L)
- OpenAlex
- W2008008156
- Cited by
- 408
- References
- 18
- FWCI
- 15.58
- Type
- journal article
- Language
- en
- Open access
- gold
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