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 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. I. Stimulus-response relations

1990 ◽  
Vol 64 (2) ◽  
pp. 351-369 ◽  
Author(s):  
B. J. Richmond ◽  
L. M. Optican ◽  
H. Spitzer
Keyword(s):  
Visual Processing ◽  
Cortical Neurons ◽  
Two Dimensional ◽  
Temporal Modulation ◽  
Gray Background ◽  
Complex Cells ◽  
Stimulus Response ◽  
Black And White ◽  
Stimulus Set ◽  
Simple And Complex Cells

1. Previously we developed a new approach for investigating visual system neuronal activity in which single neurons are considered to be communication channels transmitting stimulus-dependent codes in their responses. Application of this approach to the stimulus-response relations of inferior temporal (IT) neurons showed that these carry stimulus-dependent information in the temporal modulation as well as in the strength of their responses. IT cortex is a late station in the visual processing stream. Presumably the neuronal properties arise from the properties of the inputs. However, the discovery that IT neuronal spike trains transmit information in stimulus-dependent temporally modulated codes could not be assumed to be true for those earlier stations, so the techniques used in the earlier study were applied to single-striate cortical neurons in the studies reported here. 2. Single-striate cortical neurons were recorded from three awake, fixating rhesus monkeys. The neurons were stimulated by two sets of patterns. The first set was made up of 128 black-and-white patterns based on a complete, orthogonal set of two-dimensional Walsh-Hadamard functions. These stimuli appear as combinations of black-and-white rectangles and squares, and they fully span the range of all possible black-and-white pictures that can be constructed in an 8 x 8 grid. Except for the stimulus that appeared as an all-white or all-black square, each stimulus had equal areas of white and black. The second stimulus set was made up of single bars constructed in the same 8 x 8 grid as the Walsh stimuli. These were presented both as black against a gray background and white against a gray background. The stimuli were centered on the receptive field, and each member of the stimulus set was presented once before any stimulus appeared again. 3. The responses of 21 striate cortical neurons were recorded and analyzed. Two were identified as simple cells and the other 19 as complex cells according to the criteria originally used by Hubel and Wiesel. The stimulus set elicited a wide variety of response strengths and patterns from each neuron. The responses from both the bars and the Walsh set could be used to differentiate and classify simple and complex cells. 4. The responses of both simple and complex cells showed striking stimulus-related strength and temporal modulation. For all of the complex cells there were instances where the responses to a stimulus and its contrast-reversed mate were substantially different in response strength or pattern, or both.(ABSTRACT TRUNCATED AT 400 WORDS)

Enhancement of inferior temporal neurons during visual discrimination

1987 ◽  
Vol 58 (6) ◽  
pp. 1292-1306 ◽  
Author(s):  
B. J. Richmond ◽  
T. Sato
Keyword(s):  
Spatial Attention ◽  
Trial Type ◽  
Discrimination Task ◽  
Temporal Cortex ◽  
Pattern Discrimination ◽  
Response Strength ◽  
The Other ◽  
Inferior Temporal Cortex ◽  
Attention Task ◽  
Type Pattern

1. Previous results have shown that spatially directed attention enhances the stimulus-elicited responses of neurons in some areas of the brain. In the inferior temporal (IT) cortex, however, directing attention toward a stimulus mildly inhibits the responses of the neurons. Inferior temporal cortex is involved in pattern discrimination, but not spatial localization. If enhancement signifies that a neuron is participating in the function for which that part of cortex is responsible, then pattern discrimination, not spatial attention, should enhance responses of IT neurons. The influence of pattern discrimination behavior on the responses of IT neurons was therefore compared with previously reported suppressive influences of both spatial attention and the fixation point. 2. Single IT neurons were recorded from two monkeys while they performed each of five tasks. One task required the monkey to make a pattern discrimination between a bar and a square of light. In the other four tasks the same bar of light appeared, but the focus of spatial attention could differ, and the fixation point could be present or absent. Either attention to (without discrimination of) the bar stimulus or the presence of the fixation point attenuated responses slightly. These two suppressive influences produced a greater attenuation when both were present. 3. The visual conditions and motor requirements when the bar stimulus appeared in the discrimination task were identical to those of the trials in the stimulus attention task. However, one-half of the responsive neurons showed significantly stronger responses to the bar stimulus when it appeared in the discrimination task than when it appeared in the stimulus attention task. For most of these neurons, discrimination just overcame the combined effect of the two suppressive influences. For six other neurons, the response strength was significantly greater during the discrimination task than during any other task. 4. The monkeys achieved an overall correct performance rate of 90% in both the discrimination and stimulus attention tasks. To achieve this performance in the discrimination task they adopted a strategy in which they performed one trial type, bar stimulus attention trials, perfectly (100%) and the other trial type, pattern trials, relatively poorly (84% correct).(ABSTRACT TRUNCATED AT 400 WORDS)

Regional and Laminar Differences in In Vivo Firing Patterns of Primate Cortical Neurons

2005 ◽  
Vol 94 (1) ◽  
pp. 567-575 ◽  
Author(s):  
Shigeru Shinomoto ◽  
Youichi Miyazaki ◽  
Hiroshi Tamura ◽  
Ichiro Fujita
Keyword(s):  
Cortical Neurons ◽  
Temporal Cortex ◽  
Local Variation ◽  
Inferior Temporal Cortex ◽  
Regular Type ◽  
Cortical Areas ◽  
Different Types ◽  
Area Te ◽  
Firing Characteristics

The firing rates of cortical neurons change in time; yet, some aspects of their in vivo firing characteristics remain unchanged and are specific to individual neurons. A recent study has shown that neurons in the monkey medial motor areas can be grouped into 2 firing types, “likely random” and “quasi-regular,” according to a measure of local variation of interspike intervals. In the present study, we extended this analysis to area TE of the inferior temporal cortex and addressed whether this classification applies generally to different cortical areas and whether different types of neurons show different laminar distribution. We found that area TE did consist of 2 groups of neurons with different firing characteristics, one similar to the “likely random” type in the medial motor cortical areas, and the other exhibiting a “clumpy-bursty” firing pattern unique to TE. The quasi-regular type was rarely observed in area TE. The likely random firing type of neuron was more frequently found in layers V–VI than in layers II–III, whereas the opposite was true for the clumpy-bursty firing type. These results show that neocortical areas consist of heterogeneous neurons that differ from one area to another in their basic firing characteristics. Moreover, we show that spike trains obtained from a single cortical neuron can provide a clue that helps to identify its layer localization.

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.

Temporal encoding of two-dimensional patterns by single units in primate inferior temporal cortex. II. Quantification of response waveform

1987 ◽  
Vol 57 (1) ◽  
pp. 147-161 ◽  
Author(s):  
B. J. Richmond ◽  
L. M. Optican
Keyword(s):  
Principal Components ◽  
Neuronal Response ◽  
Temporal Cortex ◽  
Preceding Paper ◽  
Principal Component ◽  
Spike Count ◽  
Inferior Temporal Cortex ◽  
Large Set ◽  
Two Dimensional ◽  
Temporal Modulation

The purpose of this study was to describe how the responses of neurons in inferior temporal (IT) cortex represent visual stimuli. In the preceding paper we described the responses of IT neurons to a large set of two-dimensional black and white patterns. The responses to different stimuli showed temporal modulation of the spike trains. This paper develops a method for quantifying temporal modulation and shows that the stimulus determines the distribution over time, as well as the number, of spikes in a response. The responses were quantified using an orthogonal set of temporal waveforms called principal components. The principal components related to each neuron were extracted from all the responses of that neuron to all of the stimuli, regardless of which stimulus elicited which response. Each response was then projected onto the set of principal components to obtain a set of coefficients that quantified its temporal modulation. This decomposition produces coefficients that are uncorrelated with each other. Thus each coefficient could be tested individually, with univariate statistics, to determine whether its relation to the stimulus was nonrandom. The waveforms of the principal components are unconstrained and depend only on the responses from which they are derived; hence, they can assume any shape. Nonetheless, the 21 neurons we analyzed all had principal components that belonged to only one of two sets. The two sets could be characterized by their first principal component, which was either phasic or tonic. This suggests that these neurons may use as few as two different mechanisms in generating responses. The first principal component was highly correlated with spike count, and both were driven by the stimulus. Higher principal components were uncorrelated with spike count, yet some of them were also driven by the stimulus. Thus the principal components form a richer description of the stimulus-dependent aspects of a neuronal response than does spike count. Bootstrap tests showed that several principal components (usually 3 or 4) were determined by the stimulus. Since higher principal components were not correlated with the spike count, the stimulus must have determined the distribution of spikes in the response as well as their number. However, it is possible that the number and distribution of spikes are both determined by the same characteristics of the stimulus. In this case, the temporal modulation would be redundant, and a simple univariate measure would be sufficient to characterize the stimulus-response relationship.(ABSTRACT TRUNCATED AT 400 WORDS)

scholarly journals Adaptation to Binocular Anticorrelation Results in Increased Neural Excitability

2020 ◽  
Vol 32 (1) ◽  
pp. 100-110
Author(s):  
Reuben Rideaux ◽  
Elizabeth Michael ◽  
Andrew E. Welchman
Keyword(s):  
Steady State ◽  
Evoked Potentials ◽  
Cortical Neurons ◽  
Temporal Cortex ◽  
Theoretical Work ◽  
Inferior Temporal Cortex ◽  
Visually Evoked Potentials ◽  
Sources Of Information ◽  
Neural Excitability ◽  
The Brain

Throughout the brain, information from individual sources converges onto higher order neurons. For example, information from the two eyes first converges in binocular neurons in area V1. Some neurons are tuned to similarities between sources of information, which makes intuitive sense in a system striving to match multiple sensory signals to a single external cause—that is, establish causal inference. However, there are also neurons that are tuned to dissimilar information. In particular, some binocular neurons respond maximally to a dark feature in one eye and a light feature in the other. Despite compelling neurophysiological and behavioral evidence supporting the existence of these neurons [Katyal, S., Vergeer, M., He, S., He, B., & Engel, S. A. Conflict-sensitive neurons gate interocular suppression in human visual cortex. Scientific Reports, 8, 1239, 2018; Kingdom, F. A. A., Jennings, B. J., & Georgeson, M. A. Adaptation to interocular difference. Journal of Vision, 18, 9, 2018; Janssen, P., Vogels, R., Liu, Y., & Orban, G. A. At least at the level of inferior temporal cortex, the stereo correspondence problem is solved. Neuron, 37, 693–701, 2003; Tsao, D. Y., Conway, B. R., & Livingstone, M. S. Receptive fields of disparity-tuned simple cells in macaque V1. Neuron, 38, 103–114, 2003; Cumming, B. G., & Parker, A. J. Responses of primary visual cortical neurons to binocular disparity without depth perception. Nature, 389, 280–283, 1997], their function has remained opaque. To determine how neural mechanisms tuned to dissimilarities support perception, here we use electroencephalography to measure human observers' steady-state visually evoked potentials in response to change in depth after prolonged viewing of anticorrelated and correlated random-dot stereograms (RDS). We find that adaptation to anticorrelated RDS results in larger steady-state visually evoked potentials, whereas adaptation to correlated RDS has no effect. These results are consistent with recent theoretical work suggesting “what not” neurons play a suppressive role in supporting stereopsis [Goncalves, N. R., & Welchman, A. E. “What not” detectors help the brain see in depth. Current Biology, 27, 1403–1412, 2017]; that is, selective adaptation of neurons tuned to binocular mismatches reduces suppression resulting in increased neural excitability.

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.

Digital Processing of STEM Images

1981 ◽  
Vol 39 ◽  
pp. 236-237
Author(s):  
A. V. Crewe ◽  
M. Ohtsuki
Keyword(s):  
High Resolution ◽  
Low Dose ◽  
Assembly Language ◽  
Processing System ◽  
Principal Component ◽  
Digital Processing ◽  
Image Analysis System ◽  
Black And White ◽  
Analysis System ◽  
Dose Imaging

We have assembled an image processing system for use with our high resolution STEM for the particular purpose of working with low dose images of biological specimens. The system is quite flexible, however, and can be used for a wide variety of images.The original images are stored on magnetic tape at the microscope using the digitized signals from the detectors. For low dose imaging, these are “first scan” exposures using an automatic montage system. One Nova minicomputer and one tape drive are dedicated to this task.The principal component of the image analysis system is a Lexidata 3400 frame store memory. This memory is arranged in a 640 x 512 x 16 bit configuration. Images are displayed simultaneously on two high resolution monitors, one color and one black and white. Interaction with the memory is obtained using a Nova 4 (32K) computer and a trackball and switch unit provided by Lexidata.The language used is BASIC and uses a variety of assembly language Calls, some provided by Lexidata, but the majority written by students (D. Kopf and N. Townes).

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