Neurons in the inferior temporal cortex (IT) of the macaque fire more strongly to some shapes than others, but little is known about how to characterize this shape tuning more generally, because most previous studies have used somewhat arbitrary variations in the stimuli with unspecified magnitudes of the changes. The present investigation studied the modulation of IT cells to nonaccidental property (NAP, i.e., invariant to orientations in depth) and metric property (MP, i.e., depth dependent) variations of dimensions of generalized cones (a general formalism for characterizing shapes hypothesized to mediate object recognition). Changes in an NAP resulted in greater neuronal modulation than equally large pixel-wise changes in an MP (including those consisting of a rotation in depth). There was also precise and highly systematic neuronal tuning to the quantitative variations of MPs along specific dimensions to which a neuron was sensitive. The NAP advantage was independent of whether the object was composed of only a single part or had two parts. These findings indicate that qualitative shape changes such as NAPs help explain the surplus amount of IT shape sensitivity that cannot be accounted for on the basis of metric or pixel-based changes alone. This NAP advantage may provide the neural basis for the greater detectability of NAP compared with MP changes in human psychophysics.
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http://dx.doi.org/10.1523/JNEUROSCI.23-07-03016.2003 | DOI Listing |
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Physical Chemistry, TU Dresden, Zellescher Weg 19, 01069 Dresden, Germany.
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Department of General Psychology and Padova Neuroscience Center, University of Padova, Padova, Italy.
Hierarchical generative models can produce data samples based on the statistical structure of their training distribution. This capability can be linked to current theories in computational neuroscience, which propose that spontaneous brain activity at rest is the manifestation of top-down dynamics of generative models detached from action-perception cycles. A popular class of hierarchical generative models is that of Deep Belief Networks (DBNs), which are energy-based deep learning architectures that can learn multiple levels of representations in a completely unsupervised way exploiting Hebbian-like learning mechanisms.
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Department of Molecular, Cellular, and Developmental Biology; University of Michigan; Ann Arbor, Michigan, 48109; USA.
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