Neuronal synchrony does not correlate with motion coherence in cortical area MT

Natural visual scenes are cluttered with multiple objects whose individual features must somehow be selectively linked (or ‘bound’) if perception is to coincide with reality. Recent neurophysiological evidence supports a ‘binding-by-synchrony’ hypothesis: neurons excited by features of the same object fire synchronously, while neurons excited by features of different objects do not. Moving plaid patterns offer a straightforward means to test this idea. By appropriate manipulations of apparent transparency, the component gratings of a plaid pattern can be seen as parts of a single coherently moving surface or as two non-coherently moving surfaces. We examined directional tuning and synchrony of area-MT neurons in awake, fixating primates in response to perceptually coherent and non-coherent plaid patterns. Here we show that directional tuning correlated highly with perceptual coherence, which is consistent with an earlier study. Although we found stimulus-dependent synchrony, coherent plaids elicited significantly less synchrony than did non-coherent plaids. Our data therefore do not support the binding-by-synchrony hypothesis as applied to this class of motion stimuli in area MT.

[1]  Robert M. Storm,et al.  Animal Orientation and Navigation , 1968 .

[2]  Klaus Schmidt-Koenig,et al.  Animal Orientation and Navigation , 1972 .

[3]  J. Rayner A vortex theory of animal flight. Part 2. The forward flight of birds , 1979, Journal of Fluid Mechanics.

[4]  Benjamin W. C. Rosser,et al.  The avian pectoralis : histochemical characterization and distribution of muscle fiber types , 1986 .

[5]  M K Habib,et al.  Dynamics of neuronal firing correlation: modulation of "effective connectivity". , 1989, Journal of neurophysiology.

[6]  W. Singer,et al.  Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties , 1989, Nature.

[7]  T. D. Albright,et al.  Transparency and coherence in human motion perception , 1990, Nature.

[8]  C. Ellington Limitations on Animal Flight Performance , 1991 .

[9]  Thomas D. Albright,et al.  Neural correlates of perceptual motion coherence , 1992, Nature.

[10]  T. Albright,et al.  Motion coherency rules are form-cue invariant , 1992, Vision Research.

[11]  K. Dial Activity patterns of the wing muscles of the pigeon (Columba livia) during different modes of flight , 1992 .

[12]  Adrian L. R. Thomas On the aerodynamics of birds’ tails , 1993 .

[13]  W Singer,et al.  Visual feature integration and the temporal correlation hypothesis. , 1995, Annual review of neuroscience.

[14]  E. Bandman,et al.  Heterogeneity of myosin heavy-chain expression in fast-twitch fiber types of mature avian pectoralis muscle. , 1996, Biochemistry and cell biology = Biochimie et biologie cellulaire.

[15]  C. Pennycuick,et al.  Wingbeat frequency and the body drag anomaly: wind-tunnel observations on a thrush nightingale (Luscinia luscinia) and a teal (Anas crecca) , 1996, The Journal of experimental biology.

[16]  Thomas D. Albright,et al.  The interpretation of visual motion: Evidence for surface segmentation mechanisms , 1996, Vision Research.

[17]  W. Singer,et al.  Stimulus-dependent synchronization of neuronal responses in the visual cortex of the awake macaque monkey , 1996, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[18]  J. Todd,et al.  On the relative contributions of motion energy and transparency to the perception of moving plaids , 1996, Vision Research.

[19]  A. Biewener,et al.  Mechanical power output of bird flight , 1997, Nature.

[20]  K. Hoffmann,et al.  Synchronization of Neuronal Activity during Stimulus Expectation in a Direction Discrimination Task , 1997, The Journal of Neuroscience.

[21]  C. Pennycuick,et al.  A new low-turbulence wind tunnel for bird flight experiments at Lund University, Sweden , 1997, The Journal of experimental biology.

[22]  T. Albright,et al.  Luminance contrast affects motion coherency in plaid patterns by acting as a depth-from-occlusion cue , 1998, Vision Research.

[23]  Wolf Singer,et al.  Neuronal Synchrony: A Versatile Code for the Definition of Relations? , 1999, Neuron.

[24]  J. Rayner Estimating power curves of flying vertebrates. , 1999, The Journal of experimental biology.

[25]  A Thiele,et al.  Decision‐related activity in the macaque dorsal visual pathway , 1999, The European journal of neuroscience.

[26]  Rainer Goebel,et al.  Neural synchrony correlates with surface segregation rules , 2000, Nature.

[27]  Bret W. Tobalske,et al.  Biomechanics and Physiology of Gait Selection in Flying Birds* , 2000, Physiological and Biochemical Zoology.

[28]  Christopher C. Pack,et al.  Dynamic properties of neurons in cortical area MT in alert and anaesthetized macaque monkeys , 2001, Nature.

[29]  R. Marsh,et al.  The mechanical power output of the flight muscles of blue-breasted quail (Coturnix chinensis) during take-off. , 2001, The Journal of experimental biology.

[30]  W. Bair,et al.  Correlated Firing in Macaque Visual Area MT: Time Scales and Relationship to Behavior , 2001, The Journal of Neuroscience.

[31]  A. Biewener,et al.  Estimates of circulation and gait change based on a three-dimensional kinematic analysis of flight in cockatiels (Nymphicus hollandicus) and ringed turtle-doves (Streptopelia risoria). , 2002, The Journal of experimental biology.

[32]  Katherine M. Armstrong,et al.  Selective gating of visual signals by microstimulation of frontal cortex , 2003, Nature.

[33]  Ch. von der Malsburg,et al.  A neural cocktail-party processor , 1986, Biological Cybernetics.

[34]  H. Rodman,et al.  Single-unit analysis of pattern-motion selective properties in the middle temporal visual area (MT) , 2004, Experimental Brain Research.

[35]  Jos J. Eggermont,et al.  Neural connectivity only accounts for a small part of neural correlation in auditory cortex , 1996, Experimental Brain Research.