Categorical representation of visual stimuli in the primate prefrontal cortex.
By David J. Freedman, Maximilian Riesenhuber, Tomaso A. Poggio, Earl K. Miller. 2001
1,102 citations recorded by the source corpus.
Subfields: Cognitive Neuroscience, Social Psychology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cognitive Neuroscience
- Nikolaus Kriegeskorte
- James J. DiCarlo
- Mark D'Esposito
- Tomaso A. Poggio
- Stanislas Dehaene
- Charles F. Cadieu
- Daniel L. K. Yamins
- Xiao-Jing Wang
In Social Psychology
- John S. Duncan
- Thomas Serre
- Tomaso A. Poggio
- Markus Kiefer
- James J. DiCarlo
- Moshe Bar
- Marc H. Bornstein
- Daniel L. K. Yamins
Citations in the corpus
- Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies (2018, background): “Such categorical perception is also characteristic of biological intelligence [12, 13, 31].”
- On the Role of Object-Specific Features for Real World Object Recognition in Biological Vision (2002, methodology): “It should be easy to replace with a more biologically plausible linear classifier the svm classifier we have used here, while accounting well for the sharp class boundary exhibited by some category-specific neurons in prefrontal cortex [7].”
- The Distributed Nature of Working Memory. (2017, background): “In one series of studies [109,110], for instance, macaques were trained to memorize morphed images of animals whose low-level visual features could vary either within category or across a category boundary between cats and dogs.”
- Experience-dependent sharpening of visual shape selectivity in inferior temporal cortex. (2005, neutral): “Keywords: categorization, inferior temporal cortex, learning, object recognition, vision”
- Maps and streams in the auditory cortex: nonhuman primates illuminate human speech processing (2009, background): “For example, it has been suggested that visual categories are formed in the lateral PF”
- Single neurons in prefrontal cortex encode abstract rules (2001, background): “The ability of PFC neurons to group cues into behavioural categories that are dependent on abstract rules is consistent with observations of a loss of flexibility after PFC damage and with the ability of PFC neurons to form perceptual categorie”
- Prefrontal cortex activity during flexible categorization (2010, positive): “Our monkeys flexibly switched between these category schemes while we recorded neural activity from multiple electrodes in the PFC, targeting the same ventrolateral region as our prior work (Freedman et al., 2001, 2002, 2003).”
- Prefrontal and medial temporal lobe interactions in long-term memory (2003, background): “, although it has been suggested that prefrontal regions might be commonly recruited together, adapting their function depending on the nature of the task being undertake”
- A Cortical Mechanism for Triggering Top-Down Facilitation in Visual Object Recognition (2003, background): “Finally, a recent report demonstrating the central role of the PFC in representing categories of visual objects (Freedman et al., 2001) is in direct agreement with the present proposal that a visually driven PFC activation is elicited by LFs.”
- Is the rostro-caudal axis of the frontal lobe hierarchical? (2009, background): “Likewise, single-unit recording experiments have revealed that frontal neurons are highly flexible, capable of changing their tuning properties as task demands chang”
- Neuroimaging studies of shifting attention: a meta-analysis (2004, background): “Evidence has accumulated demonstrating that the DLPFC represents task and abstract rule information (Wallis et al., 2001)—particularly when behaviorally relevant (Rainer et al., 1998)—and abstract categories (Freedman et al., 2001), among other types of representations.”
- Dynamic Coding for Cognitive Control in Prefrontal Cortex (2013, background): “Classification learning tasks demonstrate the basic principles of adaptive coding in PFC (Cromer et al., 2011; Freedman et al., 2001; Li et al., 2007; Roy et al., 2010).”
- Using goal-driven deep learning models to understand sensory cortex (2016, methodology): “While not required, it is useful to use simple decoders such as linear classifiers or linear regressors 1,32,63,64 , as these embody hypothetical downstream decoding circuits 65,66 .”