Abstract:
:In this work, we propose a two-layered descriptive model for motion processing from retina to the cortex, with an event-based input from the asynchronous time-based image sensor (ATIS) camera. Spatial and spatiotemporal filtering of visual scenes by motion energy detectors has been implemented in two steps in a simple layer of a lateral geniculate nucleus model and a set of three-dimensional Gabor kernels, eventually forming a probabilistic population response. The high temporal resolution of independent and asynchronous local sensory pixels from the ATIS provides a realistic stimulation to study biological motion processing, as well as developing bio-inspired motion processors for computer vision applications. Our study combines two significant theories in neuroscience: event-based stimulation and probabilistic sensory representation. We have modeled how this might be done at the vision level, as well as suggesting this framework as a generic computational principle among different sensory modalities.
journal_name
Neural Computjournal_title
Neural computationauthors
Khoei MA,Ieng SH,Benosman Rdoi
10.1162/neco_a_01191subject
Has Abstractpub_date
2019-06-01 00:00:00pages
1114-1138issue
6eissn
0899-7667issn
1530-888Xjournal_volume
31pub_type
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