neurodudes

In an impressive integrative effort, a new article in this month’s issue of Neuron describes a robust object and face classification model that is consistent with both behavioral and fMRI experiments.

From a preview of the article:

“A central theme that has emerged in research on face perception therefore is whether or not faces are “special” such that the cognitive and neural mechanisms that underlie their processing are different from those underlying the processing of other visual objects. [...] In this issue of Neuron, Jiang et al. (2006) provide a compelling array of evidence supporting the idea that the processing of faces and objects do not rely on qualitatively different mechanisms. In a series of experiments, Jiang et al. present and integrate findings from neural modeling, behavior, and fMRI, showing that face classification, similarly to object classification, can be achieved by a simple-to-complex architecture, based on hierarchical shape detectors. Furthermore, variations of this model can account for both configural and feature-based processing without qualitative modification of the model’s structure.”

The Riesenhuber lab, from which this work comes, has been working on object recognition in an integrative way. The lab is particularly “at the intersection of neuroscience and AI”.

This entry was posted on Tuesday, April 11th, 2006 at 12:02 am by Stephen Larson and is filed under At the scale of systems and functions, Computational neuroscience, Imaging, Neural network models, Vision. You can follow any responses to this entry through the RSS 2.0 feed. You can leave a response, or trackback from your own site.