Fast Readout of Object Identity from Macaque Inferior Temporal Cortex

By Chou P. Hung, Gabriel Kreiman, Tomaso A. Poggio, James J. DiCarlo. 2005

952 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience

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In Cognitive Neuroscience

Citations in the corpus

Deep learning in neural networks: An overview (2014, background): “, 2013) seem compatible with insights about the visual pathway in the primate cerebral cortex, which has been studied for many decades (e.g., Hubel and Wiesel, 1968; Perrett et al., 1982; Desimone et al., 1984; Felleman and Van Essen, 1991; Perrett et al., 1992; Kobatake and Tanaka, 1994; Logothetis”
View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation (2016, neutral): “This is too fast of a timescale for feedback to play a large role [23, 50, 27].”
How does the brain solve visual object recognition? (2012, positive): “…argue for a distributed representation of visual objects in IT, as suggested previously (e.g., Desimone et al., 1984; Kiani et al., 2007; Rolls and Tovee, 1995)—a view that motivates the population decoding approaches described above (Hung et al., 2005; Li et al., 2009; Rust and DiCarlo, 2010).”
Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream (2014, background): “That is, neurons in early visual areas have smaller receptive fields (RFs) and respond to simple features such as edge orientation (Hubel and Wiesel, 1962), whereas neurons further along the ventral pathway have larger RFs and are more invariant to transformations and can be selective for complex sh”
Unsupervised Learning of Invariant Representations in Hierarchical Architectures (2013, background): “3 : Empirical demonstration of the properties of invariance , stability and uniqueness of the hierarchical architecture ( see Theo rem 12 ) in a specific 2 layers implementation ( HMAX ) .”
STDP-based spiking deep neural networks for object recognition (2016, background): “forward wave of spikes in IT (∼ 100−150 ms poststimulus presentation) appears to be sufficient for crude object recognition [54, 19, 33].”
Matching Categorical Object Representations in Inferior Temporal Cortex of Man and Monkey (2008, background): “Monkey studies have reported IT responses that are correlated with categories (Vogels, 1999; Sigala and Logothetis, 2002; Baker et al., 2002; Tsao et al., 2003; Freedman et al., 2003; Kiani et al., 2005; Hung et al., 2005; Tsao et al., 2006; Afraz et al., 2006).”
The dynamics of invariant object recognition in the human visual system. (2014, methodology): “18 the timing for size and position invariant information is close to the latencies of size and position-invariant signals in macaque IT, which first occur around 100 ms (Hung et al., 2005).”
Optogenetic and pharmacological suppression of spatial clusters of face neurons reveal their causal role in face gender discrimination (2015, background): “In this study, we aimed to test this hypothesis for one facediscrimination task: Is face gender* discrimination impaired by temporary silencing of IT subregions enriched with face (detector) neurons? Because distributed IT cortical populations show the computational capacity to support a wide range ”
Representational Similarity Analysis – Connecting the Branches of Systems Neuroscience (2008, neutral): “…Polyn et al., 2005; Serences and Boynton, 2007; Spiridon and Kanwisher, 2002; Strother et al., 2002; Williams et al., 2007; for reviews see Haynes and Rees, 2006; Kriegeskorte and Bandettini, 2007; Norman et al., 2006) and also in monkey electrophysiology (Hung et al., 2005; Tsao et al., 2006).”
Why is Real-World Visual Object Recognition Hard? (2008, background): “Second, the development of appropriate recognition tests is critical to guiding the development of object recognition models and testing performance of neuronal populations that might support recognition [24].”