Object Category Structure in Response Patterns of Neuronal Population in Monkey Inferior Temporal Cortex

Kiani, Roozbeh; Esteky, Hossein; Mirpour, Koorosh; Tanaka, Keiji

doi:10.1152/jn.00024.2007

Cited by 451 publications

(565 citation statements)

References 63 publications

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“…In a recent study in which monkeys were shown images of objects from many natural categories, the population of IT neurons was highly selective for category membership, especially for animate objects like faces and bodies 48 . More indirect evidence for category selectivity is the observation that single IT neurons are more selective for shape features that are useful for object categorization ('non-accidental properties') than for other shape features ('metric properties') 49 .…”

Section: Monkey Studiesmentioning

confidence: 99%

“…Computational evidence has also revealed that image fragments of intermediate complexity are more informative for object categorization than fragments of low or high complexity 118,119 , suggesting that neural coding in object-selective areas might be based on such intermediate-complexity shape features. However, a computational description of cortical organization in the ventral pathway remains a distant goal, because current neurophysiologically plausible models cannot yet predict the strong category selectivity that exists in the object-vision pathway 48,120 : for example, the dominance of object category in the selectivities of monkey IT neurons is not predicted by 'standard' hierarchical models 48 . Similar discrepancies might exist in the human brain 100 .…”

Section: Object Category As a Basic Propertymentioning

confidence: 99%

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Interpreting fMRI data: maps, modules and dimensions

2008

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Neuroimaging research over the past decade has revealed a detailed picture of the functional organization of the human brain. Here we focus on two fundamental questions that are raised by the detailed mapping of sensory and cognitive functions and illustrate these questions with findings from the object-vision pathway. First, are functionally specific regions that are located close together best understood as distinct cortical modules or as parts of a larger-scale cortical map? Second, what functional properties define each cortical map or module? We propose a model in which overlapping continuous maps of simple features give rise to discrete modules that are selective for complex stimuli.Advances in brain imaging technology (especially functional MRI (fMRI)) have radically improved our understanding of the functional organization of the human brain (BOX 1). In this Review we describe the organization of the ventral visual pathway, which is characterized by strong selectivity for particular object categories (for example, faces and bodies) at the level of both individual neurons and larger cortical regions. We then consider two central questions: whether this organization reflects maps or modules, and what properties are mapped. In each case we derive clues from the literature on the primary sensory cortex, in which cortical maps have been studied extensively using electrophysiology in animals. We find that apparently modular cortical regions, such as orientation columns and face-selective regions, might be parts of larger maps, and show that it is a substantial challenge to determine the basic properties and dimensions that describe functional organization most parsimoniously. We then propose a new framework that reconciles the existence of graded cortical maps and distinct functional modules. In this framework, the strong category selectivity that exists for faces and other objects might arise from the nonlinear combination of multiple correlated maps for simpler stimulus properties. The ventral visual pathwayThe ventral visual pathway comprises a large cortical region that occupies the ventral and lateral surfaces of the occipital and temporal lobes (FIG. 1). A substantial proportion of fMRI voxels in this pathway are 'object-selective' -that is, they respond more strongly when people view images of objects than when people view scrambled versions of these objects or texture patterns. This object-selective region is often referred to as the lateral occipital complex (LOC) 1 . The LOC has little selectivity for particular stimulus categories 2-4 , but several regions of NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author Manuscript cortex near the LOC are selective for particular object categories: they respond at least twice as strongly to their 'preferred' stimuli than to other stimuli. For example, in essentially all humans cortical regions can be found that respond selectively to faces (the fusiform face area (FFA) 5,6 and, in many individuals, the occipital face area (OFA)) 7,8 , to pl...

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Section: Monkey Studiesmentioning

confidence: 99%

Section: Object Category As a Basic Propertymentioning

confidence: 99%

Interpreting fMRI data: maps, modules and dimensions

2008

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“…Tsunoda et al [54] presented another example where concept hierarchies may play a crucial role (cf., also [53,27]). In combined optical and single unit recordings from inferotemporal cortex of Macaques they found that visual objects evoke blobs of activity in the optical recordings that stay roughly constant in cortical location and size if complex stimuli get successively simplified.…”

Section: Discussionmentioning

confidence: 88%

“…Strong experimental evidence has been provided to support this view [27,43,53,54] up to the highest cognitive levels in human [44,42]. Similarly, many scientists considered feedback processes, often interpreted as top-down attention [26,47,49] or more recently the action of some generative models that aim to reconstruct or predict the input [12,46].…”

Section: Discussionmentioning

confidence: 99%

On the Natural Hierarchical Composition of Cliques in Cell Assemblies

Wennekers

2009

Cogn Comput

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Hebbian cell assemblies can be formalised as sets of tightly connected cells in auto-and hetero-associative memories. Direct evidence for such ''cliques'' has recently been obtained in multiple-unit recordings from rat hippocampal neurons. These experiments suggest a hierarchical organisation where cliques are embedded in each other such that larger cliques represent less specific stimulus conditions. We here suggest an interpretation stating that the firing patterns may not just reflect nested categories but a lattice of concepts about stimulus-response mappings in the sense of formal concept analysis, an applied branch of set theory. We present an implementation of formal concept lattices in bidirectional associative memories that in contrast to previous work satisfies Dale's principle and uses balanced excitation and inhibition. Inhibitory cells have fixed, non-plastic synapses even if the model learns new concepts. As an extreme case a single global inhibitory cell is enough that controls the total level of activation. The excitatory cells can further learn incrementally using a Hebbian coincidence learning rule. Implications of the model for retrieval in auto-associative memories are further outlined. Overall the model is well suited for representing hierarchical compositional relationships between entities in the form of correlated patterns in technical cognitive systems and potentially the brain.

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“…In detail, the "distance" between two stimuli is defined by a measure of dissimilarity between two elicited activity patterns, which can be assessed in a variety of ways, including correlation or rank-correlation analysis, Euclidean and Manhattan distance measure, etc. A widely used measure of dissimilarity is correlation distance, i.e., one minus the correlation between patterns [21].…”

Section: Representing Dissimilarity Structure Of Response Patterns Tomentioning

confidence: 99%

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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Object Category Structure in Response Patterns of Neuronal Population in Monkey Inferior Temporal Cortex

Cited by 451 publications

References 63 publications

Interpreting fMRI data: maps, modules and dimensions

Interpreting fMRI data: maps, modules and dimensions

On the Natural Hierarchical Composition of Cliques in Cell Assemblies

Similarity representation of pattern-information fMRI

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