Stimulus-selective properties of inferior temporal neurons in the macaque

Desimone, Robert; Albright, T. D.; Gross, C. G.; Bruce, Charles J.

doi:10.1523/jneurosci.04-08-02051.1984

Cited by 1,320 publications

(933 citation statements)

References 22 publications

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“…While the visual cortex of BNNs may use quite different learning algorithms, its objective function to be minimised may be quite similar to the one of visual ANNs. In fact, results obtained with relatively deep artificial DBNs (Lee et al, 2007b) and CNNs (Yamins et al, 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 et al, 1995;Bichot et al, 2005;Hung et al, 2005;Lennie and Movshon, 2005;Connor et al, 2007;Kriegeskorte et al, 2008;DiCarlo et al, 2012); compare a computer vision-oriented survey (Kruger et al, 2013).…”

Section: Consequences For Neurosciencementioning

confidence: 80%

Deep learning in neural networks: An overview

2015

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In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarises relevant work, much of it from the previous millennium. Shallow and deep learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.LATEX source: http://www.idsia.ch/˜juergen/DeepLearning8Oct2014.tex Complete BIBTEX file (888 kB): http://www.idsia.ch/˜juergen/deep.bib Preface This is the preprint of an invited Deep Learning (DL) overview. One of its goals is to assign credit to those who contributed to the present state of the art. I acknowledge the limitations of attempting to achieve this goal. The DL research community itself may be viewed as a continually evolving, deep network of scientists who have influenced each other in complex ways. Starting from recent DL results, I tried to trace back the origins of relevant ideas through the past half century and beyond, sometimes using "local search" to follow citations of citations backwards in time. Since not all DL publications properly acknowledge earlier relevant work, additional global search strategies were employed, aided by consulting numerous neural network experts. As a result, the present preprint mostly consists of references. Nevertheless, through an expert selection bias I may have missed important work. A related bias was surely introduced by my special familiarity with the work of my own DL research group in the past quarter-century. For these reasons, this work should be viewed as merely a snapshot of an ongoing credit assignment process. To help improve it, please do not hesitate to send corrections and suggestions to juergen@idsia.ch.

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Section: Consequences For Neurosciencementioning

confidence: 80%

Deep learning in neural networks: An overview

2015

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“…Micro-electrode studies in macaque have used simple luminance-defined, geometrical constructs and found neurons in IT that fired preferentially for related shapes (Desimone et al, 1984;Gallant et al, 1996;Gross et al, 1972;Tanaka et al, 1991) in a cue-invariant fashion (Sary et al, 1993). Neurons that are selective for object shape have also been found in V4 (Gallant et al, 1996;Pasupathy and Connor, 2002).…”

Section: Tuning For Shapementioning

confidence: 99%

The time course of shape discrimination in the human brain

et al. 2013

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The lateral occipital cortex (LOC) activates selectively to images of intact objects versus scrambled controls, is selective for the figure-ground relationship of a scene, and exhibits at least some degree of invariance for size and position. Because of these attributes, it is considered to be a crucial part of the object recognition pathway. Here we show that human LOC is critically involved in perceptual decisions about object shape. High-density EEG was recorded while subjects performed a threshold-level shape discrimination task on texture-defined figures segmented by either phase or orientation cues. The appearance or disappearance of a figure region from a uniform background generated robust visual evoked potentials throughout retinotopic cortex as determined by inverse modeling of the scalp voltage distribution. Contrasting responses from trials containing shape changes that were correctly detected (hits) with trials in which no change occurred (correct rejects) revealed stimulus-locked, target-selective activity in the occipital visual areas LOC and V4 preceding the subject's response. Activity that was locked to the subjects' reaction time was present in the LOC. Response-locked activity in the LOC was determined to be related to shape discrimination for several reasons: shape-selective responses were silenced when subjects viewed identical stimuli but their attention was directed away from the shapes to a demanding letter discrimination task; shape-selectivity was present across four different stimulus configurations used to define the figure; LOC responses correlated with participants' reaction times. These results indicate that decision-related activity is present in the LOC when subjects are engaged in threshold-level shape discriminations.

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“…Since in the monkey temporal lobe, not only facebut also hand-responsive cells have been reported (31,32), pictures of hands, faces, and objects were presented to the same patients with subdural electrodes (16). Over the sites where face-specific responses were expected, hands did not induce significant differences.…”

Section: Perception Of Human Faces As Compared With Face Parts and Otmentioning

confidence: 99%

Intracranial Neurophysiological Correlates Related to the Processing of Faces

Seeck¹,

Michel²,

Blanke³

et al. 2001

Epilepsy & Behavior

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Face perception and recognition is an intriguing ability, already present in neonates. Numerous studies in patients with brain lesions identified the temporo-occipital cortex as the crucial structure for this capacity. Analysis of electrical signals (EEG) inside the brain of patients implanted with intracranial electrodes for diagnostic purposes allows researchers to describe the temporal and spatial organization of responses to various aspects of face processing in human subjects. Several findings have emerged and appear relevant for cerebral organization in general: (1) Selective face responses were obtained from the basal temporo-occipital cortex at around 200 ms (N200); however, other structures such as the lateral temporal lobe and frontal cortex also participate in face recognition and perception tasks. (2) Each structure has a distinct "response profile"; that is, with respect to a given task certain structures respond strongly, others less or not at all. This profile might change with a different task, although the physical parameters of the stimuli remain the same. (3) The right hemispheric predominance of face processing, as suggested by patient data and studies in healthy volunteers, seemed to be restricted to its early stages (i.e., before 100 -150 ms). (4) Recognition of faces might be associated with differential intracranial responses, despite an incorrect overt response, reflecting neurophysiological correlates of implicit memory. (5) The more the stimulus resembled a complete human face, the earlier and larger the N200 response was found, in particular over the basal temporobasal cortex. Analysis of electrical signals from intracranial electrodes might help to improve our understanding of the underlying physiological and anatomical constraints of cognitive processes.

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Stimulus-selective properties of inferior temporal neurons in the macaque

Cited by 1,320 publications

References 22 publications

Deep learning in neural networks: An overview

Deep learning in neural networks: An overview

The time course of shape discrimination in the human brain

Intracranial Neurophysiological Correlates Related to the Processing of Faces

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