Cerebral Cortex, Vol. 12, No. 6, 601-616,
June 2002
© 2002 Oxford University Press
Spatial Receptive Field Organization of Macaque V4 Neurons
1 Department of Neurology, University of Massachusetts Medical School, Worcester, MA 01655, , 2 Department of Cognitive and Neural Systems, Boston University, Boston, MA 02215 and , 3 Department of Psychiatry, Massachusetts General Hospital, Boston, MA 02114, USA
Daniel Pollen, Department of Neurology, University of Massachusetts Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA. Email: pollend{at}ummhc.org.
Subfield analysis of the receptive fields (RFs) of parafoveal V4 complex cells demonstrates directly that most RFs are tiled by overlapping second-order excitatory inputs that for any given V4 cell are predominantly selective to the same preferred values of spatial frequency and orientation. These results extend hierarchical principles of RF organization in the spatial, orientation and spatial frequency domains, first recognized in V1, to an intermediate extrastriate cortex. Spatial interaction studies across subfields demonstrate that the responses of V4 neurons to paired stimuli may either decrease or increase as a function of inter-stimulus distance across the width axis. These intra-RF suppressions and facilitations vary independently in magnitude and spatial extent from cell to cell. These results taken together with the relatively large RF sizes of V4 neurons — as compared with RF sizes of their afferent inputs — lead us to hypothesize a novel property, namely that classes of stimulus configurations that enhance areal summation while reducing suppressive interactions between excitatory inputs will evoke especially robust responses. We tested, and found support for, this hypothesis by presenting stimuli consisting of optimally tuned sine-wave gratings visible only within an annular region and found that such stimuli vigorously activate V4 neurons at firing rates far higher than those evoked by comparable stimuli to either the full-field or central core. On the basis of these results we propose a framework for a new class of neural network models for the spatial RF organizations of prototypic V4 neurons.
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