scholarly journals Neural representations of haptic object size in the human brain revealed by multivoxel fMRI patterns

2020 ◽  
Vol 124 (1) ◽  
pp. 218-231 ◽  
Author(s):  
Francesca Perini ◽  
Thomas Powell ◽  
Simon J. Watt ◽  
Paul E. Downing
Keyword(s):  
Human Brain ◽  
Functional Mri ◽  
Object Size ◽  
Motor Tasks ◽  
Neural Basis ◽  
Neural Representations ◽  
The World ◽  
Haptic Object ◽  
Size Estimates

Our understanding of the neural basis of haptics (perceiving the world through touch) remains incomplete. We used functional MRI to study human haptic judgments of object size, which require integrating multiple afferent signals. Multivoxel pattern analyses identified intraparietal and prefrontal regions that encode size haptically in a metric and hand-invariant fashion. Effector-independent haptic size estimates are useful on their own and in combination with other sensory estimates for a variety of perceptual and motor tasks.

Functional MRI of the human brain

Neuroreport ◽  
1994 ◽  
Vol 5 (7) ◽  
pp. 813-816 ◽  
Author(s):  
Christoph Segebarth ◽  
Valérie Belle ◽  
Chantai Delon ◽  
Raphaël Massarelli ◽  
Jean Decety ◽  
...  
Keyword(s):  
Human Brain ◽  
Functional Mri

scholarly journals Single units and conscious vision

1998 ◽  
Vol 353 (1377) ◽  
pp. 1801-1818 ◽  
Author(s):  
◽  
N. K. Logothetis
Keyword(s):  
Binocular Rivalry ◽  
Neural Activity ◽  
Visual Awareness ◽  
Physiological Data ◽  
Neural Basis ◽  
Binocular Fusion ◽  
Neural Representations ◽  
Multistable Perception ◽  
Visual Areas ◽  
Psychophysical Studies

Figures that can be seen in more than one way are invaluable tools for the study of the neural basis of visual awareness, because such stimuli permit the dissociation of the neural responses that underlie what we perceive at any given time from those forming the sensory representation of a visual pattern. To study the former type of responses, monkeys were subjected to binocular rivalry, and the response of neurons in a number of different visual areas was studied while the animals reported their alternating percepts by pulling levers. Perception–related modulations of neural activity were found to occur to different extents in different cortical visual areas. The cells that were affected by suppression were almost exclusively binocular, and their proportion was found to increase in the higher processing stages of the visual system. The strongest correlations between neural activity and perception were observed in the visual areas of the temporal lobe. A strikingly large number of neurons in the early visual areas remained active during the perceptual suppression of the stimulus, a finding suggesting that conscious visual perception might be mediated by only a subset of the cells exhibiting stimulus selective responses. These physiological findings, together with a number of recent psychophysical studies, offer a new explanation of the phenomenon of binocular rivalry. Indeed, rivalry has long been considered to be closely linked with binocular fusion and stereopsis, and the sequences of dominance and suppression have been viewed as the result of competition between the two monocular channels. The physiological data presented here are incompatible with this interpretation. Rather than reflecting interocular competition, the rivalry is most probably between the two different central neural representations generated by the dichoptically presented stimuli. The mechanisms of rivalry are probably the same as, or very similar to, those underlying multistable perception in general, and further physiological studies might reveal a much about the neural mechanisms of our perceptual organization.

scholarly journals The Theme of Death and Eternity in Emily Dickenson’s Poetry

2008 ◽  
Vol 4 (1-2 (5)) ◽  
pp. 112-119
Author(s):  
Gayane Petrosyan
Keyword(s):  
Human Brain ◽  
19Th Century ◽  
American Poetry ◽  
Dark Side ◽  
Surrounding World ◽  
The People ◽  
The World ◽  
The 19Th Century ◽  
The Universe

The poetry of the world-renowned poetess Emily Dickenson received general acclaim in the fifties of the previous century, 70 years after her death. This country-dwelling lady who had locked herself from the surrounding world, created one of the most precious examples of the 19th century American poetry and became one of the most celebrated poets of all time without leaving her own garden.Her soul was her universe and the mission of Dickenson’s sole was to open the universe to let the people see it. Interestingly, most of her poems lack a title, are short and symbolic. The poetess managed to disclose the dark side of the human brain which symbolizes death and eternity.

scholarly journals Humans use stereo and haptic distance cues to improve physical object size estimates

2010 ◽  
Vol 8 (6) ◽  
pp. 1090-1090
Author(s):  
P. Battaglia ◽  
M. Ernst ◽  
P. Schrater ◽  
M. Di Luca ◽  
T. Machulla ◽  
...  
Keyword(s):  
Physical Object ◽  
Object Size ◽  
Size Estimates

scholarly journals Stimulus-Driven and Spontaneous Dynamics in Excitatory-Inhibitory Recurrent Neural Networks for Sequence Representation

2021 ◽  
pp. 1-43
Author(s):  
Alfred Rajakumar ◽  
John Rinzel ◽  
Zhe S. Chen
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Connectivity Matrix ◽  
Motor Tasks ◽  
Neural Representations ◽  
Input Signals ◽  
Sequence Representation ◽  
The Stability ◽  
Dynamic Attractors ◽  
Neural Sequences

Abstract Recurrent neural networks (RNNs) have been widely used to model sequential neural dynamics (“neural sequences”) of cortical circuits in cognitive and motor tasks. Efforts to incorporate biological constraints and Dale's principle will help elucidate the neural representations and mechanisms of underlying circuits. We trained an excitatory-inhibitory RNN to learn neural sequences in a supervised manner and studied the representations and dynamic attractors of the trained network. The trained RNN was robust to trigger the sequence in response to various input signals and interpolated a time-warped input for sequence representation. Interestingly, a learned sequence can repeat periodically when the RNN evolved beyond the duration of a single sequence. The eigenspectrum of the learned recurrent connectivity matrix with growing or damping modes, together with the RNN's nonlinearity, were adequate to generate a limit cycle attractor. We further examined the stability of dynamic attractors while training the RNN to learn two sequences. Together, our results provide a general framework for understanding neural sequence representation in the excitatory-inhibitory RNN.

Invariant Recognition Shapes Neural Representations of Visual Input

2018 ◽  
Vol 4 (1) ◽  
pp. 403-422 ◽  
Author(s):  
Andrea Tacchetti ◽  
Leyla Isik ◽  
Tomaso A. Poggio
Keyword(s):  
Computational Neuroscience ◽  
Sensory Input ◽  
Human Perception ◽  
Visual Input ◽  
Crucial Aspect ◽  
Human Visual Perception ◽  
Semantic Categories ◽  
Neural Representations ◽  
The People ◽  
The World

Recognizing the people, objects, and actions in the world around us is a crucial aspect of human perception that allows us to plan and act in our environment. Remarkably, our proficiency in recognizing semantic categories from visual input is unhindered by transformations that substantially alter their appearance (e.g., changes in lighting or position). The ability to generalize across these complex transformations is a hallmark of human visual intelligence, which has been the focus of wide-ranging investigation in systems and computational neuroscience. However, while the neural machinery of human visual perception has been thoroughly described, the computational principles dictating its functioning remain unknown. Here, we review recent results in brain imaging, neurophysiology, and computational neuroscience in support of the hypothesis that the ability to support the invariant recognition of semantic entities in the visual world shapes which neural representations of sensory input are computed by human visual cortex.

PRESTO, a Rapid 3D Approach for Functional MRI of Human Brain

1996 ◽  
pp. 105-110 ◽  
Author(s):  
C. T. W. Moonen ◽  
P. van Gelderen ◽  
N. Ramsey ◽  
G. Liu ◽  
J. H. Duyn ◽  
...  
Keyword(s):  
Human Brain ◽  
Functional Mri

scholarly journals Human frontoparietal cortex represents behaviorally relevant target status based on abstract object features

2019 ◽  
Vol 121 (4) ◽  
pp. 1410-1427 ◽  
Author(s):  
Margaret Henderson ◽  
John T. Serences
Keyword(s):  
Frontal Cortex ◽  
Functional Mri ◽  
Irrelevant Dimension ◽  
Target Recognition ◽  
Human Subjects ◽  
Object Identity ◽  
Neural Basis ◽  
Related Information ◽  
Parking Lot ◽  
High Level

Searching for items that are useful given current goals, or “target” recognition, requires observers to flexibly attend to certain object properties at the expense of others. This could involve focusing on the identity of an object while ignoring identity-preserving transformations such as changes in viewpoint or focusing on its current viewpoint while ignoring its identity. To effectively filter out variation due to the irrelevant dimension, performing either type of task is likely to require high-level, abstract search templates. Past work has found target recognition signals in areas of ventral visual cortex and in subregions of parietal and frontal cortex. However, target status in these tasks is typically associated with the identity of an object, rather than identity-orthogonal properties such as object viewpoint. In this study, we used a task that required subjects to identify novel object stimuli as targets according to either identity or viewpoint, each of which was not predictable from low-level properties such as shape. We performed functional MRI in human subjects of both sexes and measured the strength of target-match signals in areas of visual, parietal, and frontal cortex. Our multivariate analyses suggest that the multiple-demand (MD) network, including subregions of parietal and frontal cortex, encodes information about an object’s status as a target in the relevant dimension only, across changes in the irrelevant dimension. Furthermore, there was more target-related information in MD regions on correct compared with incorrect trials, suggesting a strong link between MD target signals and behavior. NEW & NOTEWORTHY Real-world target detection tasks, such as searching for a car in a crowded parking lot, require both flexibility and abstraction. We investigated the neural basis of these abilities using a task that required invariant representations of either object identity or viewpoint. Multivariate decoding analyses of our whole brain functional MRI data reveal that invariant target representations are most pronounced in frontal and parietal regions, and the strength of these representations is associated with behavioral performance.

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