Applicability of white-noise techniques to analyzing motion responses

J Neurophysiol. 2010 May;103(5):2642-51. doi: 10.1152/jn.00591.2009. Epub 2010 Jan 6.


Motion processing in visual neurons is often understood in terms of how they integrate light stimuli in space and time. These integrative properties, known as the spatiotemporal receptive fields (STRFs), are sometimes obtained using white-noise techniques where a continuous random contrast sequence is delivered to each spatial location within the cell's field of view. In contrast, motion stimuli such as moving bars are usually presented intermittently. Here we compare the STRF prediction of a neuron's response to a moving bar with the measured response in second-order interneurons (L-neurons) of dragonfly ocelli (simple eyes). These low-latency neurons transmit sudden changes in intensity and motion information to mediate flight and gaze stabilization reflexes. A white-noise analysis is made of the responses of L-neurons to random bar stimuli delivered either every frame (densely) or intermittently (sparsely) with a temporal sequence matched to the bar motion stimulus. Linear STRFs estimated using the sparse stimulus were significantly better at predicting the responses to moving bars than the STRFs estimated using a traditional dense white-noise stimulus, even when second-order nonlinear terms were added. Our results strongly suggest that visual adaptation significantly modifies the linear STRF properties of L-neurons in dragonfly ocelli during dense white-noise stimulation. We discuss the ability to predict the responses of visual neurons to arbitrary stimuli based on white-noise analysis. We also discuss the likely functional advantages that adaptive receptive field structures provide for stabilizing attitude during hover and forward flight in dragonflies.

MeSH terms

  • Adaptation, Physiological / physiology
  • Algorithms
  • Animals
  • Eye
  • Insecta
  • Interneurons / physiology*
  • Linear Models
  • Microelectrodes
  • Models, Neurological
  • Motion*
  • Nonlinear Dynamics
  • Photic Stimulation
  • Signal Processing, Computer-Assisted*
  • Time Factors
  • Vision, Ocular / physiology*