scholarly journals Category-specific representational patterns in left inferior frontal and temporal cortex reflect similarities and differences in the sensorimotor and distributional properties of concepts

2021 ◽  
Author(s):  
Francesca Carota ◽  
Nikolaus Kriegeskorte ◽  
Hamed Nili ◽  
Friedemann Pulvermüller
Keyword(s):  
Activity Patterns ◽  
Temporal Cortex ◽  
Inferior Temporal Cortex ◽  
Angular Gyrus ◽  
Activation Patterns ◽  
Neuronal Populations ◽  
Distributional Information ◽  
Motor Information ◽  
Category Discrimination ◽  
Sensorimotor Knowledge

AbstractNeuronal populations code similar concepts by similar activity patterns across the human brain’s networks supporting language comprehension. However, it is unclear to what extent such meaning-to-symbol mapping reflects statistical distributions of symbol meanings in language use, as quantified by word co-occurrence frequencies, or, rather, experiential information thought to be necessary for grounding symbols in sensorimotor knowledge. Here we asked whether integrating distributional semantics with human judgments of grounded sensorimotor semantics better approximates the representational similarity of conceptual categories in the brain, as compared with each of these methods used separately. We examined the similarity structure of activation patterns elicited by action- and object-related concepts using multivariate representational similarity analysis (RSA) of fMRI data. The results suggested that a semantic vector integrating both sensorimotor and distributional information yields best category discrimination on the cognitive-linguistic level, and explains the corresponding activation patterns in left posterior inferior temporal cortex. In turn, semantic vectors based on detailed visual and motor information uncovered category-specific similarity patterns in fusiform and angular gyrus for object-related concepts, and in motor cortex, left inferior frontal cortex (BA 44), and supramarginal gyrus for action-related concepts.

scholarly journals Dynamic Population Coding of Category Information in Inferior Temporal and Prefrontal Cortex

2008 ◽  
Vol 100 (3) ◽  
pp. 1407-1419 ◽  
Author(s):  
Ethan M. Meyers ◽  
David J. Freedman ◽  
Gabriel Kreiman ◽  
Earl K. Miller ◽  
Tomaso Poggio
Keyword(s):  
Prefrontal Cortex ◽  
Visual Information ◽  
Activity Patterns ◽  
Temporal Cortex ◽  
Relevant Information ◽  
Population Coding ◽  
Inferior Temporal Cortex ◽  
Category Information ◽  
Almost All ◽  
Dynamic Population

Most electrophysiology studies analyze the activity of each neuron separately. While such studies have given much insight into properties of the visual system, they have also potentially overlooked important aspects of information coded in changing patterns of activity that are distributed over larger populations of neurons. In this work, we apply a population decoding method to better estimate what information is available in neuronal ensembles and how this information is coded in dynamic patterns of neural activity in data recorded from inferior temporal cortex (ITC) and prefrontal cortex (PFC) as macaque monkeys engaged in a delayed match-to-category task. Analyses of activity patterns in ITC and PFC revealed that both areas contain “abstract” category information (i.e., category information that is not directly correlated with properties of the stimuli); however, in general, PFC has more task-relevant information, and ITC has more detailed visual information. Analyses examining how information coded in these areas show that almost all category information is available in a small fraction of the neurons in the population. Most remarkably, our results also show that category information is coded by a nonstationary pattern of activity that changes over the course of a trial with individual neurons containing information on much shorter time scales than the population as a whole.

scholarly journals Fast Readout of Object Identity from Macaque Inferior Temporal Cortex

Science ◽  
2005 ◽  
Vol 310 (5749) ◽  
pp. 863-866 ◽  
Author(s):  
Chou P. Hung ◽  
Gabriel Kreiman ◽  
Tomaso Poggio ◽  
James J. DiCarlo
Keyword(s):  
Neural Coding ◽  
Temporal Cortex ◽  
Population Level ◽  
Inferior Temporal Cortex ◽  
Object Identity ◽  
Neuronal Populations ◽  
Novel Objects ◽  
Coarse Information ◽  
Short Time

Understanding the brain computations leading to object recognition requires quantitative characterization of the information represented in inferior temporal (IT) cortex. We used a biologically plausible, classifier-based readout technique to investigate the neural coding of selectivity and invariance at the IT population level. The activity of small neuronal populations (∼100 randomly selected cells) over very short time intervals (as small as 12.5 milliseconds) contained unexpectedly accurate and robust information about both object “identity” and “category.” This information generalized over a range of object positions and scales, even for novel objects. Coarse information about position and scale could also be read out from the same population.

scholarly journals Learning to Recognize Visual Objects With Microstimulation in Inferior Temporal Cortex

2008 ◽  
Vol 100 (1) ◽  
pp. 197-211 ◽  
Author(s):  
Keisuke Kawasaki ◽  
David L. Sheinberg
Keyword(s):  
Activity Patterns ◽  
Visual Learning ◽  
Temporal Cortex ◽  
Inferior Temporal Cortex ◽  
Classification Learning ◽  
Critical Question ◽  
Direct Stimulation ◽  
Visual Classification ◽  
Association Learning ◽  
New Associations

The malleability of object representations by experience is essential for adaptive behavior. It has been hypothesized that neurons in inferior temporal cortex (IT) in monkeys are pivotal in visual association learning, evidenced by experiments revealing changes in neural selectivity following visual learning, as well as by lesion studies, wherein functional inactivation of IT impairs learning. A critical question remaining to be answered is whether IT neuronal activity is sufficient for learning. To address this question directly, we conducted experiments combining visual classification learning with microstimulation in IT. We assessed the effects of IT microstimulation during learning in cases where the stimulation was exclusively informative, conditionally informative, and informative but not necessary for the classification task. The results show that localized microstimulation in IT can be used to establish visual classification learning, and the same stimulation applied during learning can predictably bias judgments on subsequent recognition. The effect of induced activity can be explained neither by direct stimulation-motor association nor by simple detection of cortical stimulation. We also found that the learning effects are specific to IT stimulation as they are not observed by microstimulation in an adjacent auditory area. Our results add the evidence that the differential activity in IT during visual association learning is sufficient for establishing new associations. The results suggest that experimentally manipulated activity patterns within IT can be effectively combined with ongoing visually induced activity during the formation of new associations.

scholarly journals Effect of Adaptation on Object Representation Accuracy in Macaque Inferior Temporal Cortex

2013 ◽  
Vol 25 (5) ◽  
pp. 777-789 ◽  
Author(s):  
Dzmitry A. Kaliukhovich ◽  
Wouter De Baene ◽  
Rufin Vogels
Keyword(s):  
Object Representation ◽  
Temporal Cortex ◽  
Inferior Temporal Cortex ◽  
Efficient Coding ◽  
Neuronal Populations ◽  
Cross Adaptation ◽  
Functional Consequences ◽  
The Difference ◽  
Induced Changes ◽  
Familiar Stimuli

Stimulus repetition produces a decrease of the response in many cortical areas and different modalities. This adaptation is highly prominent in macaque inferior temporal (IT) neurons. Here we ask how these repetition-induced changes in IT responses affect the accuracy by which IT neurons encode objects. This question bears on the functional consequences of adaptation, which are still unclear. We recorded the responses of single IT neurons to sequences of familiar shapes, each shown for 300 msec with an ISI of the same duration. The difference in shape between the two successively presented stimuli,that is, adapter and test, varied parametrically. The discriminability of the test stimuli was reduced for repeated compared with nonrepeated stimuli. In some conditions for which adapter and test shapes differed, the cross-adaptation resulted in an enhanced discriminability. These single cell results were confirmed in a second experiment in which we recorded multiunit spiking activity using a laminar microelectrode in macaque IT. Two familiar stimuli were presented successively for 500 msec each and separated with an ISI of the same duration. Trials consisted either of a repetition of the same stimulus or of their alternation. Small neuronal populations showed decreased classification accuracy for repeated compared with nonrepeated test stimuli, but classification was enhanced for the test compared with adapter stimuli when the test stimulus differed from recently seen stimuli. These findings suggest that short-term, stimulus-specific adaptation in IT supports efficient coding of stimuli that differ from recently seen ones while impairing the coding of repeated stimuli.

Temporal encoding of two-dimensional patterns by single units in primate primary visual cortex. II. Information transmission

1990 ◽  
Vol 64 (2) ◽  
pp. 370-380 ◽  
Author(s):  
B. J. Richmond ◽  
L. M. Optican
Keyword(s):  
Cortical Neurons ◽  
Activity Patterns ◽  
Temporal Cortex ◽  
Principal Component ◽  
Response Strength ◽  
Inferior Temporal Cortex ◽  
Temporal Code ◽  
Response Code ◽  
Stimulus Response ◽  
Black And White

1. Previously, we studied how picture information was processed by neurons in inferior temporal cortex. We found that responses varying in both response strength and temporal waveform carried information about briefly flashed stationary black-and-white patterns. Now, we have applied that same paradigm to the study of striate cortical neurons. 2. In this approach the responses to a set of basic black and white pictures were quantified through use of a set of basic waveforms, the principal components (extracted from all the responses of each neuron). We found that the first principal component, which corresponds to the response strength, and others, which correspond to different basic temporal activity patterns, were significantly related to the stimuli, i.e., the stimulus drove both the response strength and its temporal pattern. 3. Our previous study had shown that, when information theory was used to quantify the stimulus-response relation, inferior temporal neurons convey over twice as much information in a response code that includes temporal modulation as in a response code that includes only the response strength. This study shows that striate cortical neurons also carry twice as much information in a temporal code as in a response strength code. Thus single visual neurons at both ends of a cortical processing chain for visual pattern use a multidimensional temporal code to carry stimulus-related information. 4. These results support our multiplex-filter hypothesis, which states that single visual system neurons can be regarded as several simultaneously active parallel channels, each of which conveys independent information about the stimulus.

Where the Mass Counts: Common Cortical Activation for Different Kinds of Nonsingularity

2012 ◽  
Vol 24 (4) ◽  
pp. 915-932 ◽  
Author(s):  
Frank Domahs ◽  
Arne Nagels ◽  
Ulrike Domahs ◽  
Carin Whitney ◽  
Richard Wiese ◽  
...  
Keyword(s):  
Numerical Cognition ◽  
Temporal Cortex ◽  
Cortical Activation ◽  
Semantic Interpretation ◽  
Angular Gyrus ◽  
Activation Patterns ◽  
Mass Nouns ◽  
Count Nouns ◽  
Narrative Processing ◽  
Left Angular Gyrus

Typically, plural nouns are morphosyntactically marked for the number feature, whereas mass nouns are morphosyntactically singular. However, both plural count nouns and mass nouns can be semantically interpreted as nonsingular. In this study, we investigated the hypothesis that their commonality in semantic interpretation may lead to common cortical activation for these different kinds of nonsingularity. To this end, we examined brain activation patterns related to three types of nouns while participants were listening to a narrative. Processing of plural compared with singular nouns was related to increased activation in the left angular gyrus. Processing of mass nouns compared with singular count nouns was related to increased activity bilaterally in the superior temporal cortex and also in the left angular gyrus. No significant activation was observed in the direct comparison between plural and mass nouns. We conclude that the left angular gyrus, also known to be relevant for numerical cognition, is involved in the semantic interpretation of different kinds of nonsingularity.

scholarly journals Analysing linear multivariate pattern transformations in neuroimaging data

2018 ◽  
Author(s):  
Alessio Basti ◽  
Marieke Mur ◽  
Nikolaus Kriegeskorte ◽  
Vittorio Pizzella ◽  
Laura Marzetti ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Activity Patterns ◽  
Temporal Cortex ◽  
Inferior Temporal Cortex ◽  
Linear Transformations ◽  
Data Set ◽  
Face Area ◽  
Neuroimaging Data ◽  
Early Visual Cortex ◽  
One To One

AbstractMost connectivity metrics in neuroimaging research reduce multivariate activity patterns in regions-of-interests (ROIs) to one dimension, which leads to a loss of information. Importantly, it prevents us from investigating the transformations between patterns in different ROIs. Here, we applied linear estimation theory in order to robustly estimate the linear transformations between multivariate fMRI patterns with a cross-validated Tikhonov regularisation approach. We derived three novel metrics that describe different features of these voxel-by-voxel mappings: goodness-of-fit, sparsity and pattern deformation. The goodness-of-fit describes the degree to which the patterns in an input region can be described as a linear transformation of patterns in an output region. The sparsity metric, which relies on a Monte Carlo procedure, was introduced in order to test whether the transformation mostly consists of one-to-one mappings between voxels in different regions. Furthermore, we defined a metric for pattern deformation, i.e. the degree to which the transformation rotates or rescales the input patterns. As a proof of concept, we applied these metrics to an event-related fMRI data set consisting of four subjects that has been used in previous studies. We focused on the transformations from early visual cortex (EVC) to inferior temporal cortex (ITC), fusiform face area (FFA) and parahippocampal place area (PPA). Our results suggest that the estimated linear mappings are able to explain a significant amount of variance of the three output ROIs. The transformation from EVC to ITC shows the highest goodness-of-fit, and those from EVC to FFA and PPA show the expected preference for faces and places as well as animate and inanimate objects, respectively. The pattern transformations are sparse, but sparsity is lower than would have been expected for one-to-one mappings, thus suggesting the presence of one-to-few voxel mappings. ITC, FFA and PPA patterns are not simple rotations of an EVC pattern, indicating that the corresponding transformations amplify or dampen certain dimensions of the input patterns. While our results are only based on a small number of subjects, they show that our pattern transformation metrics can describe novel aspects of multivariate functional connectivity in neuroimaging data.

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