Stimulus-selective properties of inferior temporal neurons in the macaque

By Robert Desimone, Thomas D. Albright, Charles G. Gross, C. Bruce. 1984

Previous studies have reported that some neurons in the inferior temporal (IT) cortex respond selectively to highly specific complex objects. In the present study, we conducted the first systematic survey of the responses of IT neurons to both simple stimuli, such as edges and bars, and highly complex stimuli, such as models of flowers, snakes, hands, and faces. If a neuron responded to any of these stimuli, we attempted to isolate the critical stimulus features underlying the response. We found that many of the responsive neurons responded well to virtually every stimulus tested. The remaining, stimulus-selective cells were often selective along the dimensions of shape, color, or texture of a stimulus, and this selectivity was maintained throughout a large receptive field. Although most IT neurons do not appear to be “detectors” for complex objects, we did find a separate population of cells that responded selectively to faces. The responses of these cells were dependent on the configuration of specific face features, and their selectivity was maintained over changes in stimulus size and position. A particularly high incidence of such cells was found deep in the superior temporal sulcus. These results indicate that there may be specialized mechanisms for the analysis of faces in IT cortex.

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Citations in the corpus

Theory of Mind: A Neural Prediction Problem (2013, neutral): “Because sensory input passes through many hierarchically organized levels of processing (DiCarlo et al., 2012; Felleman and Van Essen, 1991; Logothetis and Sheinberg, 1996; Desimone et al., 1984; Maunsell and Newsome, 1987), a predictive model of sensory processing requires an account of the interac”
How does the brain solve visual object recognition? (2012, neutral): “Such findings 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…”
A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information (1993, background): “In addition, neurophysiological studies (Petersen et al., 1985, 1987), lesion studies (Rafal and Posner, 1987; Bender, 1988; Desimone et al., 1990), and positron emission tomography studies (La-Berge and Buchsbaum, 1990; Corbetta et al., 199 1) of the pulvinar suggest that it plays a role in…”
Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream (2014, neutral): “This is consistent with neurophysiological findings in primates that some downstream neurons are tuned to relatively simple features and some upstream neurons are tuned to rel- atively complex features (Desimone et al., 1984; Hegdé and Van Essen, 2007).”
Neural mechanisms of form and motion processing in the primate visual system. (1994, background): “, 1992; Tanaka, 1993) and natural shapes such as faces or hands (Perrett et al., 1982; Desimone et al., 1984).”
Hierarchical Modular Optimization of Convolutional Networks Achieves Representations Similar to Macaque IT and Human Ventral Stream (2013, background): “A core, long-standing hypothesis is that the visual input captured by the retina is rapidly processed through the ventral stream into an effective, “invariant” representation of object shape and identity [11, 9, 8].”
Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition (2014, methodology): “contribution. We can estimate the total variance of the empirical response (˙2 signal+noise ) as, ˙2 signal+noise = 1 NM X ij " 1 T X k r ijk ! # 2 ; ˙2 signal+noise = ˙ 2 signal + ˙ 2 noise : (7) Note that we cannot observe ˙2 signal and we use the subscript \noise" to denote the standar”
From the neuron doctrine to neural networks (2015, result): “Consistent with this, ‘face cells’ that responded to images of specific individuals were found in the temporal cortex of monkeys and humans 29–31 .”
Distributed hierarchical processing in the primate cerebral cortex. (1991, background): “(3) Some cells in the inferotemporal cortex are selective for faces or other complex patterns (Desimone et al., 1984; Baylis et al., 1987; Perrett et al., 1987; Saito et al., 1987).”
Memory search and the neural representation of context. (2008, background): “Decades of work on non-human 92 primates clearly shows that features of visual stimuli are represented by the pattern of spiking ac93 tivity in various cortical regions [16, 17].”
Interactions of visual stimuli in the receptive fields of inferior temporal neurons in awake macaques (2004, unknown): “inputs to represent a complex stimulus (Sato et al. 1980; Desimone et al. 1984).”
The eyes have it: the neuroethology, function and evolution of social gaze. (2000, background): “[46] Desimone R, Albright TD, Gross CG, Bruce C.”
Building high-level features using large scale unsupervised learning (2011, background): “” The extent of class-specificity of neurons in the brain is an area of active investigation, but current experimental evidence suggests the possibility that some neurons in the temporal cortex are highly selective for object categories such as faces or hands [1], and perhaps even specific people [2”