scholarly journals Recurrent computations for visual pattern completion

2018 ◽  
Vol 115 (35) ◽  
pp. 8835-8840 ◽  
Author(s):  
Hanlin Tang ◽  
Martin Schrimpf ◽  
William Lotter ◽  
Charlotte Moerman ◽  
Ana Paredes ◽  
...  
Keyword(s):  
Computational Models ◽  
Partial Information ◽  
Recognition Performance ◽  
Backward Masking ◽  
Visual Stream ◽  
Strong Argument ◽  
Pattern Completion ◽  
Occluded Objects ◽  
Ventral Visual Stream

Making inferences from partial information constitutes a critical aspect of cognition. During visual perception, pattern completion enables recognition of poorly visible or occluded objects. We combined psychophysics, physiology, and computational models to test the hypothesis that pattern completion is implemented by recurrent computations and present three pieces of evidence that are consistent with this hypothesis. First, subjects robustly recognized objects even when they were rendered <15% visible, but recognition was largely impaired when processing was interrupted by backward masking. Second, invasive physiological responses along the human ventral cortex exhibited visually selective responses to partially visible objects that were delayed compared with whole objects, suggesting the need for additional computations. These physiological delays were correlated with the effects of backward masking. Third, state-of-the-art feed-forward computational architectures were not robust to partial visibility. However, recognition performance was recovered when the model was augmented with attractor-based recurrent connectivity. The recurrent model was able to predict which images of heavily occluded objects were easier or harder for humans to recognize, could capture the effect of introducing a backward mask on recognition behavior, and was consistent with the physiological delays along the human ventral visual stream. These results provide a strong argument of plausibility for the role of recurrent computations in making visual inferences from partial information.

Activation Timecourse of Ventral Visual Stream Object-recognition Areas: High Density Electrical Mapping of Perceptual Closure Processes

2000 ◽  
Vol 12 (4) ◽  
pp. 615-621 ◽  
Author(s):  
Glen M. Doniger ◽  
John J. Foxe ◽  
Micah M. Murray ◽  
Beth A. Higgins ◽  
Joan Gay Snodgrass ◽  
...  
Keyword(s):  
Object Recognition ◽  
Partial Information ◽  
Brain Activity ◽  
Event Related Potentials ◽  
High Density ◽  
Visual Stream ◽  
Density Mapping ◽  
Related Potentials ◽  
Ventral Visual Stream ◽  
Perceptual Closure

Object recognition is achieved even in circumstances when only partial information is available to the observer. Perceptual closure processes are essential in enabling such recognitions to occur. We presented successively less fragmented images while recording high-density event-related potentials (ERPs), which permitted us to monitor brain activity during the perceptual closure processes leading up to object recognition. We reveal a bilateral ERP component (Ncl) that tracks these processes (onsets ∼ 230 msec, maximal at ∼290 msec). Scalp-current density mapping of the Ncl revealed bilateral occipito-temporal scalp foci, which are consistent with generators in the human ventral visual stream, and specifically the lateral-occipital or LO complex as defined by hemodynamic studies of object recognition.

scholarly journals Neural mechanisms underlying the income-achievement gap: the role of the ventral visual stream

2021 ◽  
pp. 101025
Author(s):  
Maya L. Rosen ◽  
Lucy A. Lurie ◽  
Kelly A. Sambrook ◽  
Andrew N. Meltzoff ◽  
Katie A. McLaughlin
Keyword(s):  
Achievement Gap ◽  
Neural Mechanisms ◽  
Visual Stream ◽  
Ventral Visual Stream ◽  
Income Achievement Gap

scholarly journals A Visual Encoding Model Based on Contrastive Self-Supervised Learning for Human Brain Activity along the Ventral Visual Stream

2021 ◽  
Vol 11 (8) ◽  
pp. 1004
Author(s):  
Jingwei Li ◽  
Chi Zhang ◽  
Linyuan Wang ◽  
Penghui Ding ◽  
Lulu Hu ◽  
...  
Keyword(s):  
Human Brain ◽  
Supervised Learning ◽  
Computational Models ◽  
Brain Activity ◽  
Learning Method ◽  
Learning Mechanisms ◽  
Visual Stream ◽  
Model Based ◽  
Visual Encoding ◽  
Ventral Visual Stream

Visual encoding models are important computational models for understanding how information is processed along the visual stream. Many improved visual encoding models have been developed from the perspective of the model architecture and the learning objective, but these are limited to the supervised learning method. From the view of unsupervised learning mechanisms, this paper utilized a pre-trained neural network to construct a visual encoding model based on contrastive self-supervised learning for the ventral visual stream measured by functional magnetic resonance imaging (fMRI). We first extracted features using the ResNet50 model pre-trained in contrastive self-supervised learning (ResNet50-CSL model), trained a linear regression model for each voxel, and finally calculated the prediction accuracy of different voxels. Compared with the ResNet50 model pre-trained in a supervised classification task, the ResNet50-CSL model achieved an equal or even relatively better encoding performance in multiple visual cortical areas. Moreover, the ResNet50-CSL model performs hierarchical representation of input visual stimuli, which is similar to the human visual cortex in its hierarchical information processing. Our experimental results suggest that the encoding model based on contrastive self-supervised learning is a strong computational model to compete with supervised models, and contrastive self-supervised learning proves an effective learning method to extract human brain-like representations.

scholarly journals CORnet: Modeling the Neural Mechanisms of Core Object Recognition

2018 ◽  
Author(s):  
Jonas Kubilius ◽  
Martin Schrimpf ◽  
Aran Nayebi ◽  
Daniel Bear ◽  
Daniel L. K. Yamins ◽  
...  
Keyword(s):  
Object Recognition ◽  
Visual Processing ◽  
State Of The Art ◽  
Recognition Performance ◽  
Explanatory Power ◽  
Large Body ◽  
Response Dynamics ◽  
Visual Stream ◽  
Current State ◽  
Ventral Visual Stream

AbstractDeep artificial neural networks with spatially repeated processing (a.k.a., deep convolutional ANNs) have been established as the best class of candidate models of visual processing in primate ventral visual processing stream. Over the past five years, these ANNs have evolved from a simple feedforward eight-layer architecture in AlexNet to extremely deep and branching NAS-Net architectures, demonstrating increasingly better object categorization performance and increasingly better explanatory power of both neural and behavioral responses. However, from the neuroscientist’s point of view, the relationship between such very deep architectures and the ventral visual pathway is incomplete in at least two ways. On the one hand, current state-of-the-art ANNs appear to be too complex (e.g., now over 100 levels) compared with the relatively shallow cortical hierarchy (4-8 levels), which makes it difficult to map their elements to those in the ventral visual stream and to understand what they are doing. On the other hand, current state-of-the-art ANNs appear to be not complex enough in that they lack recurrent connections and the resulting neural response dynamics that are commonplace in the ventral visual stream. Here we describe our ongoing efforts to resolve both of these issues by developing a “CORnet” family of deep neural network architectures. Rather than just seeking high object recognition performance (as the state-of-the-art ANNs above), we instead try to reduce the model family to its most important elements and then gradually build new ANNs with recurrent and skip connections while monitoring both performance and the match between each new CORnet model and a large body of primate brain and behavioral data. We report here that our current best ANN model derived from this approach (CORnet-S) is among the top models on Brain-Score, a composite benchmark for comparing models to the brain, but is simpler than other deep ANNs in terms of the number of convolutions performed along the longest path of information processing in the model. All CORnet models are available at github.com/dicarlolab/CORnet, and we plan to up-date this manuscript and the available models in this family as they are produced.

scholarly journals The functional neuroanatomy of face perception: from brain measurements to deep neural networks

2018 ◽  
Vol 8 (4) ◽  
pp. 20180013 ◽  
Author(s):  
Kalanit Grill-Spector ◽  
Kevin S. Weiner ◽  
Jesse Gomez ◽  
Anthony Stigliani ◽  
Vaidehi S. Natu
Keyword(s):  
Neural Networks ◽  
Computational Models ◽  
Human Performance ◽  
Deep Neural Networks ◽  
Functional Neuroanatomy ◽  
Functional Architecture ◽  
Visual Stream ◽  
Ventral Visual Stream ◽  
The Brain ◽  
New Framework

A central goal in neuroscience is to understand how processing within the ventral visual stream enables rapid and robust perception and recognition. Recent neuroscientific discoveries have significantly advanced understanding of the function, structure and computations along the ventral visual stream that serve as the infrastructure supporting this behaviour. In parallel, significant advances in computational models, such as hierarchical deep neural networks (DNNs), have brought machine performance to a level that is commensurate with human performance. Here, we propose a new framework using the ventral face network as a model system to illustrate how increasing the neural accuracy of present DNNs may allow researchers to test the computational benefits of the functional architecture of the human brain. Thus, the review (i) considers specific neural implementational features of the ventral face network, (ii) describes similarities and differences between the functional architecture of the brain and DNNs, and (iii) provides a hypothesis for the computational value of implementational features within the brain that may improve DNN performance. Importantly, this new framework promotes the incorporation of neuroscientific findings into DNNs in order to test the computational benefits of fundamental organizational features of the visual system.

scholarly journals Neural coding of fine-grained object knowledge in perirhinal cortex

2017 ◽  
Author(s):  
Amy Rose Price ◽  
Michael F. Bonner ◽  
Jonathan E. Peelle ◽  
Murray Grossman
Keyword(s):  
Neural Coding ◽  
Perirhinal Cortex ◽  
Semantic Knowledge ◽  
Perceptual Content ◽  
Perceptual Similarity ◽  
Visual Stream ◽  
Shape Information ◽  
Perceptual Features ◽  
Ventral Visual Stream

SummaryOver 40 years of research has examined the role of the ventral visual stream in transforming retinal inputs into high-level representations of object identity [1–6]. However, there remains an ongoing debate over the role of the ventral stream in coding abstract semantic content, which relies on stored knowledge, versus perceptual content that relies only on retinal inputs [7–12]. A major difficulty in adjudicating between these mechanisms is that the semantic similarity of objects is often highly confounded with their perceptual similarity (e.g., animate things are more perceptually similar to other animate things than to inanimate things). To address this problem, we developed a paradigm that exploits the statistical regularities of object colors while perfectly controlling for perceptual shape information, allowing us to dissociate lower-level perceptual features (i.e., color perception) from higher-level semantic knowledge (i.e., color meaning). Using multivoxel-pattern analyses of fMRI data, we observed a striking double dissociation between the processing of color information at a perceptual and at a semantic level along the posterior to anterior axis of the ventral visual pathway. Specifically, we found that the visual association region V4 assigned similar representations to objects with similar colors, regardless of object category. In contrast, perirhinal cortex, at the apex of the ventral visual stream, assigned similar representations to semantically similar objects, even when this was in opposition to their perceptual similarity. These findings suggest that perirhinal cortex untangles the representational space of lower-level perceptual features and organizes visual objects according to their semantic interpretations.

Lung Computational Models and the Role of the Small Airways in Asthma

PEDIATRICS ◽  
2020 ◽  
Vol 146 (Supplement 4) ◽  
pp. S359.2-S360
Author(s):  
Jennilee Eppley ◽  
Todd Mahr
Keyword(s):  
Computational Models ◽  
Small Airways

Using Convolutional Neural Networks to measure the contribution of visual features to the representation of object animacy in the brain

2019 ◽  
Author(s):  
Sushrut Thorat
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Temporal Cortex ◽  
Large Degree ◽  
Visual Features ◽  
Visual Feature ◽  
Visual Stream ◽  
Feature Information ◽  
Ventral Visual Stream ◽  
Animal Images

A mediolateral gradation in neural responses for images spanning animals to artificial objects is observed in the ventral temporal cortex (VTC). Which information streams drive this organisation is an ongoing debate. Recently, in Proklova et al. (2016), the visual shape and category (“animacy”) dimensions in a set of stimuli were dissociated using a behavioural measure of visual feature information. fMRI responses revealed a neural cluster (extra-visual animacy cluster - xVAC) which encoded category information unexplained by visual feature information, suggesting extra-visual contributions to the organisation in the ventral visual stream. We reassess these findings using Convolutional Neural Networks (CNNs) as models for the ventral visual stream. The visual features developed in the CNN layers can categorise the shape-matched stimuli from Proklova et al. (2016) in contrast to the behavioural measures used in the study. The category organisations in xVAC and VTC are explained to a large degree by the CNN visual feature differences, casting doubt over the suggestion that visual feature differences cannot account for the animacy organisation. To inform the debate further, we designed a set of stimuli with animal images to dissociate the animacy organisation driven by the CNN visual features from the degree of familiarity and agency (thoughtfulness and feelings). Preliminary results from a new fMRI experiment designed to understand the contribution of these non-visual features are presented.

LANDSLIDE RISK MANAGEMENT IN THE COASTAL ZONE OF THE KUIBYSHEV RESERVOIR DUE THE DESIGN OF HIGH-SPEED LINE "MOSCOW-KAZAN"

2017 ◽  
Author(s):  
Nikolai Petrov ◽  
Nikolai Petrov ◽  
Inna Nikonorova ◽  
Inna Nikonorova ◽  
Vladimir Mashin ◽  
...  
Keyword(s):  
High Speed ◽  
Computational Models ◽  
Stable State ◽  
Landslide Risk ◽  
Kuibyshev Reservoir ◽  
Stepped Surface ◽  
The Stability ◽  
Landslide Slope ◽  
The Village

High-speed railway "Moscow-Kazan" by the draft crosses the Volga (Kuibyshev reservoir) in Chuvashia region 500 m below the village of New Kushnikovo. The crossing plot is a right-bank landslide slope with a stepped surface. Its height is 80 m; the slope steepness -15-16o. The authors should assess the risk of landslides and recommend anti-landslide measures to ensure the safety of the future bridge. For this landslide factors have been analyzed, slope stability assessment has been performed and recommendations have been suggested. The role of the following factors have been analyzed: 1) hydrologic - erosion and abrasion reservoir and runoff role; 2) lithologyc (the presence of Urzhum and Northern Dvina horizons of plastically deformable rocks, displacement areas); 3) hydrogeological (the role of perched, ground and interstratal water); 4) geomorphological (presence of the elemental composition of sliding systems and their structure in the relief); 5) exogeodynamic (cycles and stages of landslide systems development, mechanisms and relationship between landslide tiers of different generations and blocks contained in tiers). As a result 6-7 computational models at each of the three engineering-geological sections were made. The stability was evaluated by the method “of the leaning slope”. It is proved that the slope is in a very stable state and requires the following measures: 1) unloading (truncation) of active heads blocks of landslide tiers) and the edge of the plateau, 2) regulation of the surface and groundwater flow, 3) concrete dam, if necessary.

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