The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Multiple Object Response Normalization in Monkey Inferotemporal Cortex
Open accessAbstract
The highest stages of the visual ventral pathway are commonly assumed to provide robust representation of object identity by disregarding confounding factors such as object position, size, illumination, and the presence of other objects (clutter). However, whereas neuronal responses in monkey inferotemporal cortex (IT) can show robust tolerance to position and size changes, previous work shows that responses to preferred objects are usually reduced by the presence of nonpreferred objects. More broadly, we do not yet understand multiple object representation in IT. In this study, we systematically examined IT responses to pairs and triplets of objects in three passively viewing monkeys across a broad range of object effectiveness. We found that, at least under these limited clutter conditions, a large fraction of the response of each IT neuron to multiple objects is reliably predicted as the average of its responses to the constituent objects in isolation. That is, multiple object responses depend primarily on the relative effectiveness of the constituent objects, regardless of object identity. This average effect becomes virtually perfect when populations of IT neurons are pooled. Furthermore, the average effect cannot simply be explained by attentional shifts but behaves as a primarily feedforward response property. Together, our observations are most consistent with mechanistic models in which IT neuronal outputs are normalized by summed synaptic drive into IT or spiking activity within IT and suggest that normalization mechanisms previously revealed at earlier visual areas are operating throughout the ventral visual stream.
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Cites
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“However, mechanisms that produce averaging behavior may not be the only ones mediating stimulus interactions, because some populations of cat V1 neurons (Lampl et al., 2004) and monkey V4 neurons (Gawne and Martin, 2002) have responses that are similar to the response of the best constituent stimulus of each pair (CCI…”
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Cited by
How does the brain solve visual object recognition?
DiCarlo, J. J., Zoccolan, D., & Rust, N. C. (2012) · Neuron · doi:10.1016/j.neuron.2012.01.010 · Cited by 1,790
Self-citationNormalization as a canonical neural computation
Carandini, M., & Heeger, D. J. (2012) · Nature Reviews Neuroscience · doi:10.1038/nrn3136 · Cited by 1,906
“As predicted by such a model, there is evidence for suppression in ventral stream area V4 and the inferotemporal cortex ( IT ) when a non-preferred stimulus (or object) is presented with a preferred stimulu”
BackgroundThe Normalization Model of Attention
Reynolds, J. H., & Heeger, D. J. (2009) · Neuron · doi:10.1016/j.neuron.2009.01.002 · Cited by 1,457
Spectral fingerprints of large-scale neuronal interactions
Siegel, M., Donner, T. H., & Engel, A. K. (2012) · Nature Reviews Neuroscience · doi:10.1038/nrn3137 · Cited by 1,295
Untangling invariant object recognition
DiCarlo, J. J., & Cox, D. D. (2007) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2007.06.010 · Cited by 959
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Metadata
- Authors
- Zoccolan, D., Cox, D. D., & DiCarlo, J. J.
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2005-09-07
- Volume
- 25
- Issue
- 36
- Pages
- 8150-8164
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- OpenAlex
- W2062906394
- Cited by
- 218
- References
- 11
- FWCI
- 3.74
- Type
- journal article
- Language
- en
- Open access
- hybrid
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