Stimulus features coded by single neurons of a macaque body category selective patch

Significance A fundamental and understudied question is that of the higher-level features that actually drive neuronal responses to complex images. Although previous single-unit studies developed our knowledge of the features driving face-selective regions, little is known about the feature selectivity of neurons in body-selective regions. Using a reverse correlation technique, we reveal for the first time to our knowledge the image fragments coded by single neurons of a body-selective region defined by functional imaging. We found that the neurons respond to local fragments, such as extremities (e.g. leg fragments) and curved boundaries (e.g. shoulder), tolerating position and scale changes, with evidence of opponent body coding in a few neurons. Thus, our data offer new insights on how the brain codes higher-level features. Body category-selective regions of the primate temporal cortex respond to images of bodies, but it is unclear which fragments of such images drive single neurons’ responses in these regions. Here we applied the Bubbles technique to the responses of single macaque middle superior temporal sulcus (midSTS) body patch neurons to reveal the image fragments the neurons respond to. We found that local image fragments such as extremities (limbs), curved boundaries, and parts of the torso drove the large majority of neurons. Bubbles revealed the whole body in only a few neurons. Neurons coded the features in a manner that was tolerant to translation and scale changes. Most image fragments were excitatory but for a few neurons both inhibitory and excitatory fragments (opponent coding) were present in the same image. The fragments we reveal here in the body patch with Bubbles differ from those suggested in previous studies of face-selective neurons in face patches. Together, our data indicate that the majority of body patch neurons respond to local image fragments that occur frequently, but not exclusively, in bodies, with a coding that is tolerant to translation and scale. Overall, the data suggest that the body category selectivity of the midSTS body patch depends more on the feature statistics of bodies (e.g., extensions occur more frequently in bodies) than on semantics (bodies as an abstract category).

[1]  Davide Zoccolan,et al.  Multifeatural Shape Processing in Rats Engaged in Invariant Visual Object Recognition , 2013, The Journal of Neuroscience.

[2]  Philippe G Schyns,et al.  Perceptual moments of conscious visual experience inferred from oscillatory brain activity. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[3]  Wim Vanduffel,et al.  Stimulus representations in body-selective regions of the macaque cortex assessed with event-related fMRI , 2012, NeuroImage.

[4]  Leslie G. Ungerleider,et al.  Object representations in the temporal cortex of monkeys and humans as revealed by functional magnetic resonance imaging. , 2009, Journal of neurophysiology.

[5]  Elias B. Issa,et al.  Precedence of the Eye Region in Neural Processing of Faces , 2012, The Journal of Neuroscience.

[6]  Michael J. Morgan.  A bias-free measure of retinotopic tilt adaptation , 2014 .

[7]  C. Gross,et al.  Neural representations of faces and body parts in macaque and human cortex: a comparative FMRI study. , 2009, Journal of neurophysiology.

[8]  Doris Y. Tsao,et al.  A face feature space in the macaque temporal lobe , 2009, Nature Neuroscience.

[9]  P. Schyns,et al.  Show Me the Features! Understanding Recognition From the Use of Visual Information , 2002, Psychological science.

[10]  G. Orban,et al.  Selectivity of Neuronal Adaptation Does Not Match Response Selectivity: A Single-Cell Study of the fMRI Adaptation Paradigm , 2006, Neuron.

[11]  S. W. Kuffler.  Discharge patterns and functional organization of mammalian retina. , 1953, Journal of neurophysiology.

[12]  R. Adolphs,et al.  Single-Unit Responses Selective for Whole Faces in the Human Amygdala , 2011, Current Biology.

[13]  James J DiCarlo,et al.  Multiple Object Response Normalization in Monkey Inferotemporal Cortex , 2005, The Journal of Neuroscience.

[14]  P. Schyns,et al.  Cracking the Code of Oscillatory Activity , 2011, PLoS biology.

[15]  Alison J. Wiggett,et al.  Functional MRI analysis of body and body part representations in the extrastriate and fusiform body areas. , 2007, Journal of neurophysiology.

[16]  Joseph R. Madsen,et al.  Spatiotemporal Dynamics Underlying Object Completion in Human Ventral Visual Cortex , 2014, Neuron.

[17]  Charles E Connor,et al.  Underlying principles of visual shape selectivity in posterior inferotemporal cortex , 2004, Nature Neuroscience.

[18]  Michel Vidal-Naquet,et al.  A Fragment-Based Approach to Object Representation and Classification , 2001, IWVF.

[19]  Frédéric Gosselin,et al.  Bubbles: a technique to reveal the use of information in recognition tasks , 2001, Vision Research.

[20]  Lucy S. Petro,et al.  Dynamics of Visual Information Integration in the Brain for Categorizing Facial Expressions , 2007, Current Biology.

[21]  P. Downing,et al.  The neural basis of visual body perception , 2007, Nature Reviews Neuroscience.

[22]  P. Schyns,et al.  Categorization creates functional features , 1997 .

[23]  RussLL L. Ds Vnlos,et al.  SPATIAL FREQUENCY SELECTIVITY OF CELLS IN MACAQUE VISUAL CORTEX , 2022 .

[24]  P. Schyns,et al.  Receptive Fields for Flexible Face Categorizations , 2004, Psychological science.

[25]  Doris Y. Tsao,et al.  What Makes a Cell Face Selective? The Importance of Contrast , 2012, Neuron.

[26]  P. Schyns,et al.  A principled method for determining the functionality of brain responses , 2003, Neuroreport.

[27]  Doris Y. Tsao,et al.  Faces and objects in macaque cerebral cortex , 2003, Nature Neuroscience.

[28]  D. Hubel,et al.  Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.

[29]  Galit Yovel,et al.  Bodies are Represented as Wholes Rather Than Their Sum of Parts in the Occipital-Temporal Cortex. , 2014, Cerebral cortex.

[30]  Guy A. Orban,et al.  Fine-grained stimulus representations in body selective areas of human occipito-temporal cortex , 2014, NeuroImage.

[31]  Ha Hong,et al.  Performance-optimized hierarchical models predict neural responses in higher visual cortex , 2014, Proceedings of the National Academy of Sciences.

[32]  R Goebel,et al.  Inverse mapping the neuronal substrates of face categorizations. , 2009, Cerebral cortex.

[33]  C. Gross,et al.  Representations of faces and body parts in macaque temporal cortex: a functional MRI study. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[34]  N. Kanwisher,et al.  The Human Body , 2001 .

[35]  Denise A. Soria Bauser,et al.  Is the whole the sum of its parts? Configural processing of headless bodies in the right fusiform gyrus , 2015, Behavioural Brain Research.

[36]  Robin A A Ince,et al.  Eye coding mechanisms in early human face event-related potentials. , 2014, Journal of vision.

[37]  Rufin Vogels,et al.  Tolerance of Macaque Middle STS Body Patch Neurons to Shape-preserving Stimulus Transformations , 2015, Journal of Cognitive Neuroscience.