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  Orientation-selective aVLSI spiking neurons.

Liu, S. C., Kramer, J., Indiveri, G., Delbrück, T., Burg, T. P., & Douglas, R. (2001). Orientation-selective aVLSI spiking neurons. Neural Networks, 14(6-7), 629-643. doi:10.1016/S0893-6080(01)00054-5.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0014-9D06-0 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0027-C4F2-4
Genre: Journal Article

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1851324.pdf (Publisher version), 512KB
 
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 Creators:
Liu, S. C., Author
Kramer, J., Author
Indiveri, G., Author
Delbrück, T., Author
Burg, T. P.1, Author              
Douglas, R., Author
Affiliations:
1Research Group of Biological Micro- and Nanotechnology, MPI for biophysical chemistry, Max Planck Society, ou_578602              

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 Abstract: We describe a programmable multi-chip VLSI neuronal system that can be used for exploring spike-based information processing models. The system consists of a silicon retina, a PIC microcontroller, and a transceiver chip whose integrate-and-fire neurons are connected in a soft winner-take-all architecture. The circuit on this multi-neuron chip approximates a cortical microcircuit. The neurons can be configured for different computational properties by the virtual connections of a selected set of pixels on the silicon retina. The virtual wiring between the different chips is effected by an event-driven communication protocol that uses asynchronous digital pulses, similar to spikes in a neuronal system. We used the multi-chip spike-based system to synthesize orientation-tuned neurons using both a feedforward model and a feedback model. The performance of our analog hardware spiking model matched the experimental observations and digital simulations of continuous-valued neurons. The multi-chip VLSI system has advantages over computer neuronal models in that it is real-time, and the computational time does not scale with the size of the neuronal network.

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Language(s): eng - English
 Dates: 2001-07-09
 Publication Status: Published in print
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 Rev. Method: Peer
 Identifiers: DOI: 10.1016/S0893-6080(01)00054-5
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Title: Neural Networks
Source Genre: Journal
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Pages: 15 Volume / Issue: 14 (6-7) Sequence Number: - Start / End Page: 629 - 643 Identifier: -