Reconstructing Stimulus Velocity from Neuronal Responses in Area MT

We employed a white-noise velocity signal to study the dynamics of the response of single neurons in the cortical area MT to visual motion. Responses were quantified using reverse correlation, optimal linear reconstruction filters, and reconstruction signal-to-noise ratio (SNR). The SNR and lower bound estimates of information rate were lower than we expected. Ninety percent of the information was transmitted below 18 Hz, and the highest lower bound on bit rate was 12 bits/s. A simulated opponent motion energy subunit with Poisson spike statistics was able to out-perform the MT neurons. The temporal integration window, measured from the reverse correlation half-width, ranged from 30-90 ms. The window was narrower when a stimulus moved faster, but did not change when temporal frequency was held constant.

[1]  J. B. Levitt,et al.  Receptive fields and functional architecture of macaque V2. , 1994, Journal of neurophysiology.

[2]  R A Andersen,et al.  Transparent motion perception as detection of unbalanced motion signals. III. Modeling , 1994, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[3]  E. Adelson,et al.  Directionally selective complex cells and the computation of motion energy in cat visual cortex , 1992, Vision Research.

[4]  D J Heeger,et al.  Model for the extraction of image flow. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[5]  A. Yuille,et al.  A model for the estimate of local image velocity by cells in the visual cortex , 1990, Proceedings of the Royal Society of London. B. Biological Sciences.

[6]  Terrence J. Sejnowski,et al.  Filter selection model for motion segmentation and velocity integration , 1994 .

[7]  E H Adelson,et al.  Spatiotemporal energy models for the perception of motion. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[8]  William Bialek,et al.  Reading a Neural Code , 1991, NIPS.

[9]  Christof Koch,et al.  Temporal Precision of Spike Trains in Extrastriate Cortex of the Behaving Macaque Monkey , 1999, Neural Computation.

[10]  Christof Koch,et al.  Coding of Time-Varying Signals in Spike Trains of Integrate-and-Fire Neurons with Random Threshold , 1999, Neural Computation.