Abstract
The Hassenstein-Reichardt (HR) correlation model is commonly used to model elementary motion detection in the fly. Recently, a neuronally-based computational model was proposed which, unlike the HR model, is based on identified neurons. The response of both models increases as the square of contrast, although the response of insect neurons saturates at high contrasts. We introduce a saturating nonlinearity into the neuronally-based model in order to produce contrast saturation and discuss the neuronal implications of these elements. Furthermore, we show that features of the contrast sensitivity of movement-detecting neurons are predicted by the modified model.
Original language | English (US) |
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Pages (from-to) | 173-179 |
Number of pages | 7 |
Journal | Neurocomputing |
Volume | 65-66 |
Issue number | SPEC. ISS. |
DOIs | |
State | Published - Jun 2005 |
Keywords
- Contrast saturation
- Contrast sensitivity functions
- Elementary motion detection
- Fly
- Neuronally-based model
- Vision
ASJC Scopus subject areas
- Computer Science Applications
- Cognitive Neuroscience
- Artificial Intelligence