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 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.

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.

scholarly journals rTMS Induces Brain Functional and Structural Alternations in Schizophrenia Patient With Auditory Verbal Hallucination

2021 ◽  
Vol 15 ◽  
Author(s):  
Yuanjun Xie ◽  
Muzhen Guan ◽  
Zhongheng Wang ◽  
Zhujing Ma ◽  
Huaning Wang ◽  
...  
Keyword(s):  
Clinical Symptoms ◽  
Psychiatric Symptoms ◽  
Visual Learning ◽  
Verbal Learning ◽  
Temporal Cortex ◽  
Low Frequency ◽  
Inferior Temporal Cortex ◽  
Positive Symptoms ◽  
Neural Basis ◽  
Auditory Verbal Hallucination

BackgroundLow-frequency transcranial magnetic stimulation (rTMS) over the left temporoparietal cortex reduces the auditory verbal hallucination (AVH) in schizophrenia. However, the underlying neural basis of the rTMS treatment effect for schizophrenia remains not well understood. This study investigates the rTMS induced brain functional and structural alternations and their associations with clinical as well as neurocognitive profiles in schizophrenia patients with AVH.MethodsThirty schizophrenia patients with AVH and thirty-three matched healthy controls were enrolled. The patients were administered by 15 days of 1 Hz rTMS delivering to the left temporoparietal junction (TPJ) area. Clinical symptoms and neurocognitive measurements were assessed at pre- and post-rTMS treatment. The functional (amplitude of low-frequency fluctuation, ALFF) and structural (gray matter volume, GMV) alternations were compared, and they were then used to related to the clinical and neurocognitive measurements after rTMS treatment.ResultsThe results showed that the positive symptoms, including AVH, were relieved, and certain neurocognitive measurements, including visual learning (VisLearn) and verbal learning (VerbLearn), were improved after the rTMS treatment in the patient group. Furthermore, the rTMS treatment induced brain functional and structural alternations in patients, such as enhanced ALFF in the left superior frontal gyrus and larger GMV in the right inferior temporal cortex. The baseline ALFF and GMV values in certain brain areas (e.g., the inferior parietal lobule and superior temporal gyrus) could be associated with the clinical symptoms (e.g., positive symptoms) and neurocognitive performances (e.g., VerbLearn and VisLearn) after rTMS treatment in patients.ConclusionThe low-frequency rTMS over the left TPJ area is an efficacious treatment for schizophrenia patients with AVH and could selectively modulate the neural basis underlying psychiatric symptoms and neurocognitive domains in schizophrenia.

Subdivisions of inferior temporal cortex in squirrel monkeys make dissociable contributions to visual learning and memory.

2006 ◽  
Vol 120 (2) ◽  
pp. 423-446 ◽  
Author(s):  
Rosalyn E. Weller ◽  
Mark S. LeDoux ◽  
Lisa M. Toll ◽  
Michelle K. Gould ◽  
R. Alan Hicks ◽  
...  
Keyword(s):  
Learning And Memory ◽  
Visual Learning ◽  
Temporal Cortex ◽  
Squirrel Monkeys ◽  
Inferior Temporal Cortex

The amygdala – responsible for memories of reward as well as punishment?

2000 ◽  
Vol 23 (2) ◽  
pp. 213-214
Author(s):  
Amanda Parker
Keyword(s):  
Basal Forebrain ◽  
Temporal Cortex ◽  
Inferior Temporal Cortex ◽  
Association Learning ◽  
Visual Objects ◽  
Reward Value ◽  
Rapid Modulation ◽  
Pass Through

Rolls's proposal that the amygdala is critical for the association of visual objects with reward is not consistent with recent ablation evidence. Stimulus-reward association learning is more likely to depend on basal forebrain efferents to the inferior temporal cortex, some of which pass through the amygdala. It is more likely that the amygdala is involved in rapid modulation of stimulus reward value.

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 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.

Sign in / Sign up

Export Citation Format

Share Document