A neuromorphic depth-from-motion vision model with STDP adaptation

Article


Yang, Z., Murray, A., Worgotter, F., Cameron, K. and Boonsobhak, V. 2006. A neuromorphic depth-from-motion vision model with STDP adaptation. IEEE Transactions on Neural Networks. 17 (2), pp. 482-495. https://doi.org/10.1109/TNN.2006.871711
TypeArticle
TitleA neuromorphic depth-from-motion vision model with STDP adaptation
AuthorsYang, Z., Murray, A., Worgotter, F., Cameron, K. and Boonsobhak, V.
Abstract

We propose a simplified depth-from-motion vision model based on leaky integrate-and-fire (LIF) neurons for edge detection and two-dimensional depth recovery. In the model, every LIF neuron is able to detect the irradiance edges passing through its receptive field in an optical flow field, and respond to the detection by firing a spike when the neuron's firing criterion is satisfied. If a neuron fires a spike, the time-of-travel of the spike-associated edge is transferred as the prediction information to the next synapse-linked neuron to determine its state. Correlations between input spikes and their timing thus encode depth in the visual field. The adaptation of synapses mediated by spike-timing-dependent plasticity is used to improve the algorithm's robustness against inaccuracy caused by spurious edge propagation. The algorithm is characterized on both artificial and real image sequences. The implementation of the algorithm in analog very large scale integrated (aVLSI) circuitry is also discussed.

PublisherInstitute of Electrical and Electronics Engineers
JournalIEEE Transactions on Neural Networks
ISSN1045-9227
Publication dates
PrintMar 2006
Publication process dates
Deposited28 Jan 2013
Output statusPublished
Digital Object Identifier (DOI)https://doi.org/10.1109/TNN.2006.871711
LanguageEnglish
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