Multiple Object Response Normalization in Monkey Inferotemporal Cortex

By Davide Zoccolan, David D. Cox, James J. DiCarlo. 2005

230 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience

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Citations in the corpus

Normalization as a canonical neural computation (2011, background): “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”
How does the brain solve visual object recognition? (2012, neutral): “This contemporary view, that neuronal tolerance is the required and observed single unit phenomenology, has also been shown for less intuitive identity-preserving transformations such as the addition of clutter (Li et al., 2009; Zoccolan et al., 2005).”
A Theory of Object Recognition: Computations and Circuits in the Feedforward Path of the Ventral Stream in Primate Visual Cortex (2005, methodology): “Possible explanations for the “average” behavior For those particular neurons showing an “average” response to the clutter condition, what are the underlying mechanisms for such behavior? There are several potential explanations, many of which were already listed in [Zoccolan et al., 2005].”
Trade-Off between Object Selectivity and Tolerance in Monkey Inferotemporal Cortex (2007, methodology): “…al., 2007), most of these studies found that some fraction of neurons were much more tolerant to clutter (Chelazzi et al., 1998; Reynolds et al., 1999; Zoccolan et al., 2005), sometimes a large fraction (Sato, 1989; Sheinberg and Logothetis, 2001) or a majority (Gawne and Martin, 2002) of neurons.”
Selectivity and tolerance (“invariance”) both increase as visual information propagates from cortical area V4 to IT (2010, methodology): “The activity of well-isolated V4 and IT neurons was monitored serially using standard single microelectrode methods (Zoccolan et al., 2005).”
Divisive normalization in olfactory population codes (2010, methodology): “Versions of this model describe neural activity in several visual cortical areas (Carandini et al., 1997; Cavanaugh et al., 2002; Zoccolan et al., 2005), and this model has also been extended to describe the effects of attention (Lee and Maunsell, 2009; Reynolds and Heeger, 2009).”
The Normalization Model of Attention (2009, background): “…stream areas V4 and IT (Miller et al., 1993; Missal et al., 1997; Reynolds et al., 1999; Richmond et al., 1983; Rolls and Tovee, 1995; Sato, 1989; Zoccolan et al., 2005), and in dorsal stream visual cortical areas MT and MST (Heuer and Britten, 2002; Recanzone et al., 1997; Treue et al., 2000).”
Value-based gain control: divisive normalization in parietal cortex (2011, background): “This divisive normalization model explains a number of nonlinear phenomena in striate and extrastriate cortex (Carandini et al., 1997; Britten and Heuer, 1999; Cavanaugh et al., 2002; Heuer and Britten, 2002) as well as responses in the ventral visual stream (Zoccolan et al., 2005).”