Matching Categorical Object Representations in Inferior Temporal Cortex of Man and Monkey

Kriegeskorte, Nikolaus; Mur, Marieke; Ruff, Douglas A.; Kiani, Roozbeh; Bodurka, Jerzy; Esteky, Hossein; Tanaka, Keiji; Bandettini, Peter A.

doi:10.1016/j.neuron.2008.10.043

Cited by 1,267 publications

(1,605 citation statements)

References 72 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: 81%

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: 81%

Deep learning in neural networks: An overview

2015

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“…conceptual vs perceptual) drove the observed discriminations (Simanova et al, 2014). A second approach, representational similarity analysis (RSA) (Kriegeskorte et al, 2008), compares the similarity between different stimuli and the one observed between the multivoxel activations patterns elicited by them (i.e. neural similarity).…”

Section: Multivariate Pattern Analysesmentioning

confidence: 99%

Word meaning in the ventral visual path: a perceptual to conceptual gradient of semantic coding

et al. 2016

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valentinaborghesani@gmail.comThe authors declare no competing financial interests. ABSTRACTThe meaning of words referring to concrete items is thought of as a multidimensional representation that includes both perceptual (e.g., average size, prototypical color) and conceptual (e.g., taxonomic class) dimensions. Are these different dimensions coded in different brain regions? In healthy human subjects, we tested the presence of a mapping between the implied real object size (a perceptual dimension) and the taxonomic categories at different levels of specificity (conceptual dimensions) of a series of words, and the patterns of brain activity recorded with functional magnetic resonance imaging in six areas along the ventral occipito-temporal cortical path. Combining multivariate pattern classification and representational similarity analysis, we found that the real object size implied by a word appears to be primarily encoded in early visual regions, while the taxonomic category and subcategorical cluster in more anterior temporal regions. This anteroposterior gradient of information content indicates that different areas along the ventral stream encode complementary dimensions of the semantic space.

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“…Direct visual inspection of RDMs may yield clustered structures in neural response patterns [22,23]. Nevertheless, the dissimilarity structures can be measured and analyzed by automated algorithms of multi-dimensional scaling (MDS) [24] and hierarchical clustering analysis [25].…”

Section: Representing Dissimilarity Structure Of Response Patterns Tomentioning

confidence: 99%

“…The authors found, contrary to previous studies [29,30], that representations in the ventral temporal parahippocampal place area (PPA) were characterized primarily by the spatial factor of expanse (open, closed) and in early visual cortex (EVC) primarily by distance (near, far), not by category or context. Kriegeskorte et al [22] applied hierarchical clustering analysis of response patterns in human brain evoked by 92 ungrouped-object stimuli photos, and found that object representation was inherently categorical in inferotemporal cortex (IT): animate and inanimate objects form the two major clusters; faces and bodies form subclusters within the animate cluster.…”

Section: Representing Dissimilarity Structure Of Response Patterns Tomentioning

confidence: 99%

“…One of the key questions is: do humans and monkeys see the world similarly? To this end, Kriegeskorte et al [22] have estimated blood-oxygen-level dependent responses patterns elicited in human IT and neural activities recorded from monkey IT neurons by the same 92 object images (different face, body, animal, plant and artifact stimuli photographs), and then used RSA to investigate their neural similarity structures. Despite different imaging modalities and species, the authors found a high degree of match between the similarity structures of human and monkey, which were characterized by major distinctions between animate and inanimate stimuli and, within the animate domain, between faces and bodies.…”

Section: Identifying Semantic Dissimilarity Structure In Human Brain mentioning

confidence: 99%

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Similarity representation of pattern-information fMRI

Xue

Weng

et al. 2013

Chin. Sci. Bull.

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Representational similarity analysis (RSA) is a rapidly developing multivariate platform to investigate the structure of neural activities. Similarity/dissimilarity is the core concept of RSA, realized by the construction of a representational dissimilarity matrix, that addresses the closeness/distance for each pair of research elements (e.g., one minus the correlation between the brain responses to 2 different stimuli) and in turn, constitutes a multivariate pattern as its analytic foundation. This approach is also welcome for its sensitivity in detecting subtle differences of distributed experimental effects in the brain. Importantly, RSA is not only an experimental tool but a promising data-analytical framework that can integrate cross-modal imaging signals, explore brain-behavior link, and verify computational models according to measured neural activities. RSA substantiates its integrative power by relating similarity structure in one domain (e.g., stimulus features) to that in another domain (e.g., neural activities). This review summarizes dissimilarity/similarity definition of RSA, introduces how to derive the dissimilarity structure in neural response pattern, and carry out connectivity analysis based on RSA platform. Several recent advances are highlighted, such as the extraction of across-subjects regularity, cross-validation of brain reactivity in human beings and monkeys, the incorporation of computational models and behavioral profiles into RSA. Voxel receptor field modeling, another promising multivariate tool of pattern elucidation, is presented and compared. The application of RSA is expected to surge and extend in many fields of neuroscience, computation, psychology and medicine. We also discuss the limitations of RSA and some critical questions that need to be addressed in future research.representational similarity analysis, pattern information, condition-rich experiments, decode Citation:Xue S W, Weng X C, He S, et al. Similarity representation of pattern-information fMRI.

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Matching Categorical Object Representations in Inferior Temporal Cortex of Man and Monkey

Cited by 1,267 publications

References 72 publications

Deep learning in neural networks: An overview

Deep learning in neural networks: An overview

Word meaning in the ventral visual path: a perceptual to conceptual gradient of semantic coding

Similarity representation of pattern-information fMRI

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