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Shape Selectivity of Middle Superior Temporal Sulcus Body Patch Neurons
Ioannis Kalfas1, Satwant Kumar1, Rufin Vogels1
1Laboratorium voor Neuro- en Psychofysiologie, Department of Neurosciences, KU Leuven, Leuven, Belgium.
Eneuro
|June 30, 2017
Summary
Researchers studied how neurons in the macaque brain process visual body shapes. Deep convolutional neural networks (CNNs) effectively modeled the shape selectivity of these neurons, suggesting shape, not just category, is key.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Primate Vision
Background:
- Functional MRI studies reveal specific brain regions in primates activated by visual body images.
- The middle superior temporal sulcus body (MSB) patch in macaques contains neurons selective for body stimuli.
- Previous research indicates shape is a significant factor in MSB neuron responses.
Purpose of the Study:
- To investigate and model the precise shape selectivity of single MSB neurons.
- To determine the extent to which shape, rather than semantic meaning, drives MSB neuron responses.
Main Methods:
- Recorded responses of single MSB neurons to diverse shapes.
- Employed an adaptive stimulus sampling procedure to refine shape stimuli.
- Fitted neuron responses using models based on shape parameters, pixel data, and convolutional neural networks (CNNs).
Main Results:
- Deep convolutional layers of CNNs provided the best fit for MSB neuron shape selectivity, explaining a median of 77% of the variance.
- Goodness-of-fit improved with deeper CNN layers but decreased in fully connected layers.
- Human ratings indicated that while many top shapes were animal-like, they didn't always resemble a body.
Conclusions:
- Deep CNNs successfully model single-unit shape selectivity in MSB neurons.
- Shape, particularly contour features, is a primary driver of MSB neuron responses.
- Semantic or category knowledge plays a minor role in the shape selectivity of individual MSB neurons.
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