The Macaque Face Patch System: A Window into Object Representation

Tsao, Doris Y.

doi:10.1101/sqb.2014.79.024950

Cited by 20 publications

(11 citation statements)

References 26 publications

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“…The preferential responses to human faces compared to non-human faces is in line with a recent human functional magnetic resonance imaging (fMRI) study, in which multivariate patterns of activity throughout the ventral TC were found to correlate with behavioral judgments of biological similarity of the same stimuli (i.e., monkeys and mammals versus insects and birds) 59 . Moreover, two electrophysiological studies in the macaque brain have shown faster responses of neurons to primate faces compared to nonprimate animal faces 60 or a change in species selectivity across different patches of face-selective areas 61 . Our findings along with these studies may explain the behavioral observations of conspecific advantage in face recognition; that is, humans and other primates recognize members of their own species more readily than faces of other species 62,63 .…”

Section: Discussionmentioning

confidence: 99%

Fast temporal dynamics and causal relevance of face processing in the human temporal cortex

et al. 2020

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We measured the fast temporal dynamics of face processing simultaneously across the human temporal cortex (TC) using intracranial recordings in eight participants. We found sites with selective responses to faces clustered in the ventral TC, which responded increasingly strongly to marine animal, bird, mammal, and human faces. Both face-selective and face-active but non-selective sites showed a posterior to anterior gradient in response time and selectivity. A sparse model focusing on information from the human face-selective sites performed as well as, or better than, anatomically distributed models when discriminating faces from non-faces stimuli. Additionally, we identified the posterior fusiform site (pFUS) as causally the most relevant node for inducing distortion of conscious face processing by direct electrical stimulation. These findings support anatomically discrete but temporally distributed response profiles in the human brain and provide a new common ground for unifying the seemingly contradictory modular and distributed modes of face processing.

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Section: Discussionmentioning

confidence: 99%

Fast temporal dynamics and causal relevance of face processing in the human temporal cortex

et al. 2020

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“…This permutation logic-based coverage of all possible connectivity-patterns can mechanistically account for why researchers reported all sorts of interesting cells in the brain that corresponded to some kind of specific stimulus or multiple stimuli (e.g., syringes, peanuts, faces, hands, the actress Halle Berry, or a nest) or a category of items (e.g., dogs vs. cats, or people vs. other objects; Rolls et al, 1979; Logothetis and Sheinberg, 1996; Fried et al, 1997; Freedman et al, 2003; Hampson et al, 2004; Gross, 2005; Quiroga et al, 2008; Bowers, 2009; Tsao, 2014). Although combining simple stimulus-features from sensory organs for higher cognition were often postulated and reported (Buck and Axel, 1991; Yeshurun and Sobel, 2010; Fu et al, 2015), findings of some cells in a given site which responded to multiple stimuli (as the literature intermittently described in bits and pieces) have reinforced the popular but undue impression that somehow convergence and combination occurred but in a stochastic and random fashion.…”

Section: Discussionmentioning

confidence: 99%

Brain Computation Is Organized via Power-of-Two-Based Permutation Logic

Xie

Fox

Liu

et al. 2016

Front. Syst. Neurosci.

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There is considerable scientific interest in understanding how cell assemblies—the long-presumed computational motif—are organized so that the brain can generate intelligent cognition and flexible behavior. The Theory of Connectivity proposes that the origin of intelligence is rooted in a power-of-two-based permutation logic (N = 2i–1), producing specific-to-general cell-assembly architecture capable of generating specific perceptions and memories, as well as generalized knowledge and flexible actions. We show that this power-of-two-based permutation logic is widely used in cortical and subcortical circuits across animal species and is conserved for the processing of a variety of cognitive modalities including appetitive, emotional and social information. However, modulatory neurons, such as dopaminergic (DA) neurons, use a simpler logic despite their distinct subtypes. Interestingly, this specific-to-general permutation logic remained largely intact although NMDA receptors—the synaptic switch for learning and memory—were deleted throughout adulthood, suggesting that the logic is developmentally pre-configured. Moreover, this computational logic is implemented in the cortex via combining a random-connectivity strategy in superficial layers 2/3 with nonrandom organizations in deep layers 5/6. This randomness of layers 2/3 cliques—which preferentially encode specific and low-combinatorial features and project inter-cortically—is ideal for maximizing cross-modality novel pattern-extraction, pattern-discrimination and pattern-categorization using sparse code, consequently explaining why it requires hippocampal offline-consolidation. In contrast, the nonrandomness in layers 5/6—which consists of few specific cliques but a higher portion of more general cliques projecting mostly to subcortical systems—is ideal for feedback-control of motivation, emotion, consciousness and behaviors. These observations suggest that the brain’s basic computational algorithm is indeed organized by the power-of-two-based permutation logic. This simple mathematical logic can account for brain computation across the entire evolutionary spectrum, ranging from the simplest neural networks to the most complex.

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“…For example, the fusiform face area FFA, initially conceptualized as one area, decomposes into multiple areas (FFA1 and FFA2) when scanning at a finer spatial resolution [50,51]. Data from human imaging and monkey electrophysiology support a hierarchical model with an increase of the complexity, invariance, and perceptual subjectivity of the represented features [49,[53][54][55][56][57][58][59][60][61]. Areas higher up in the computational hierarchy, corresponding to more anterior regions in ventral OTC in human cortex, represent stimuli such as faces and words in a more holistic, less part-based manner and with higher levels of invariance for image transformations.…”

Section: Computational Hierarchymentioning

confidence: 99%

Factors Determining Where Category-Selective Areas Emerge in Visual Cortex

Beeck

Pillet

Ritchie

2019

Trends in Cognitive Sciences

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A hallmark of functional localization in the human brain is the presence of areas in visual cortex specialized for representing particular categories such as faces and words. Why do these areas appear where they do during development? Recent findings highlight several general factors to consider when answering this question. Experience-driven category selectivity arises in regions that have: (i) preexisting selectivity for properties of the stimulus; (ii) are appropriately placed in the computational hierarchy of the visual system; and (iii) exhibit domain-specific patterns of connectivity to non-visual regions. In other words, cortical location of category selectivity is constrained by what category will be represented, how it will be represented, and why the representation will be used.

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The Macaque Face Patch System: A Window into Object Representation

Cited by 20 publications

References 26 publications

Fast temporal dynamics and causal relevance of face processing in the human temporal cortex

Fast temporal dynamics and causal relevance of face processing in the human temporal cortex

Brain Computation Is Organized via Power-of-Two-Based Permutation Logic

Factors Determining Where Category-Selective Areas Emerge in Visual Cortex

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