Fast Readout of Object Identity from Macaque Inferior Temporal Cortex
Open accessAbstract
Understanding the brain computations leading to object recognition requires quantitative characterization of the information represented in inferior temporal (IT) cortex. We used a biologically plausible, classifier-based readout technique to investigate the neural coding of selectivity and invariance at the IT population level. The activity of small neuronal populations (∼100 randomly selected cells) over very short time intervals (as small as 12.5 milliseconds) contained unexpectedly accurate and robust information about both object “identity” and “category.” This information generalized over a range of object positions and scales, even for novel objects. Coarse information about position and scale could also be read out from the same population.
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Activity of small neuronal populations in monkey IT, over very short time intervals (as small as 12.5 ms) and only about 100 ms after stimulus onset, contains surprisingly accurate and robust information supporting a variety of recognition tasks.
“Very recent data (9) show that the activity of small neuronal populations in monkey IT, over very short time intervals (as small as 12.5 ms) and only ≈ 100 ms after stimulus onset, contains surprisingly accurate and robust information supporting a variety of recognition tasks.”
From A feedforward architecture accounts for rapid categorization (2007) · cites this paper as [9]
Readout from C2b units in the model predicted recent readout experiments in IT, showing very similar selectivity and invariance for the same set of stimuli.
“Readout from C 2b units in the model described here predicted (23) recent readout experiments in IT (9), showing very similar selectivity and invariance for the same set of stimuli.”
From A feedforward architecture accounts for rapid categorization (2007) · cites this paper as [9]
A recent study demonstrated that a linear classifier can read out with high accuracy and over extremely short time intervals (a single bin as short as 12.5 ms) object identity, object category, and other information from the activity of ≈ 100 neurons in IT.
“A recent study (9) demonstrated that a linear classifier can indeed read out with high accuracy and over extremely short time intervals (a single bin as short as 12.5 ms) object identity, object category, and other information (such as the position and size of the object) from the activity of ≈ 100 neurons in IT.”
From A feedforward architecture accounts for rapid categorization (2007) · cites this paper as [9]
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- Authors
- Hung, C. P., Kreiman, G., Poggio, T., & DiCarlo, J. J.
- Affiliations
- McGovern Institute for Brain Research (US)
- Corresponding author
- Hung, Chou P.
- Kreiman, Gabriel
- Venue
- Science
- Published
- 2005-11-04
- Volume
- 310
- Issue
- 5749
- Pages
- 863-866
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2166130385
- Cited by
- 854
- References
- 31
- Citations by year
- 2026: 25 · 2025: 28 · 2024: 46 · 2023: 21 · 2022: 38 · 2021: 50 · 2020: 40 · 2019: 29 · 2018: 52 · 2017: 41 · 2016: 54 · 2015: 40 · 2014: 78 · 2013: 46 · 2012: 41
- FWCI
- 15.98
- Type
- journal article
- Language
- en
- Open access
- green
- nrs.harvard.edu
- Licence
- cc-by
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- inferior temporal cortex, object recognition, object identity, object categories, neural coding, population coding, novel object generalization, macaque
- MeSH terms
- Action Potentials, Animals, Brain Mapping, Macaca mulatta, Neurons, Psychomotor Performance, Temporal Lobe, Time Factors, Visual Perception, Recognition, Psychology
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