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Cerebral Cortex Advance Access published online on July 31, 2008
Cerebral Cortex, doi:10.1093/cercor/bhn125
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© 2008 The Authors
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Converging Neuronal Activity in Inferior Temporal Cortex during the Classification of Morphed Stimuli

Athena Akrami1, Yan Liu2, Alessandro Treves1 and Bharathi Jagadeesh2

1 Cognitive Neuroscience Sector, SISSA International School for Advanced Studies, Trieste, Italy, 2 Department of Physiology & Biophysics, University of Washington, Seattle, WA 98115, USA

Address correspondence to Bharathi Jagadeesh, Box 357330, University of Washington, Seattle, WA 98195, USA. Email: bjag{at}u.washington.edu.

How does the brain dynamically convert incoming sensory data into a representation useful for classification? Neurons in inferior temporal (IT) cortex are selective for complex visual stimuli, but their response dynamics during perceptual classification is not well understood. We studied IT dynamics in monkeys performing a classification task. The monkeys were shown visual stimuli that were morphed (interpolated) between pairs of familiar images. Their ability to classify the morphed images depended systematically on the degree of morph. IT neurons were selected that responded more strongly to one of the 2 familiar images (the effective image). The responses tended to peak ~120 ms following stimulus onset with an amplitude that depended almost linearly on the degree of morph. The responses then declined, but remained above baseline for several hundred ms. This sustained component remained linearly dependent on morph level for stimuli more similar to the ineffective image but progressively converged to a single response profile, independent of morph level, for stimuli more similar to the effective image. Thus, these neurons represented the dynamic conversion of graded sensory information into a task-relevant classification. Computational models suggest that these dynamics could be produced by attractor states and firing rate adaptation within the population of IT neurons.

Key Words: attractor neural network • firing rate adaptation • inferior temporal cortex • monkey behavior • vision • visual classification


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