scholarly journals Incorporating intrinsic suppression in deep neural networks captures dynamics of adaptation in neurophysiology and perception

2020 ◽  
Vol 6 (42) ◽  
pp. eabd4205 ◽  
Author(s):  
K. Vinken ◽  
X. Boix ◽  
G. Kreiman
Keyword(s):  
Neural Network ◽  
Visual Processing ◽  
Tuning Curve ◽  
Recognition Performance ◽  
Novelty Detection ◽  
Fundamental Property ◽  
Human Perception ◽  
Subjective Experiences ◽  
Intrinsic Mechanism ◽  
Detection Enhancement

Adaptation is a fundamental property of sensory systems that can change subjective experiences in the context of recent information. Adaptation has been postulated to arise from recurrent circuit mechanisms or as a consequence of neuronally intrinsic suppression. However, it is unclear whether intrinsic suppression by itself can account for effects beyond reduced responses. Here, we test the hypothesis that complex adaptation phenomena can emerge from intrinsic suppression cascading through a feedforward model of visual processing. A deep convolutional neural network with intrinsic suppression captured neural signatures of adaptation including novelty detection, enhancement, and tuning curve shifts, while producing aftereffects consistent with human perception. When adaptation was trained in a task where repeated input affects recognition performance, an intrinsic mechanism generalized better than a recurrent neural network. Our results demonstrate that feedforward propagation of intrinsic suppression changes the functional state of the network, reproducing key neurophysiological and perceptual properties of adaptation.

scholarly journals Incorporating neuronal fatigue in deep neural networks captures dynamics of adaptation in neurophysiology and perception

2019 ◽  
Author(s):  
Kasper Vinken ◽  
Xavier Boix ◽  
Gabriel Kreiman
Keyword(s):  
Visual Processing ◽  
Computational Models ◽  
Recognition Performance ◽  
Novelty Detection ◽  
Fundamental Property ◽  
Temporal Context ◽  
Repetition Suppression ◽  
Proposed Model ◽  
Biophysical Processes ◽  
Fatigue Mechanism

AbstractAdaptation is a fundamental property of the visual system that molds how an object is processed and perceived in its temporal context. It is unknown whether adaptation requires a circuit level implementation or whether it emerges from neuronally intrinsic biophysical processes. Here we combined neurophysiological recordings, psychophysics, and deep convolutional neural network computational models to test the hypothesis that a neuronally intrinsic, biophysically plausible, fatigue mechanism is sufficient to account for the hallmark properties of adaptation. The proposed model captured neural signatures of adaptation including repetition suppression and novelty detection. At the behavioral level, the proposed model was consistent with perceptual aftereffects. Furthermore, adapting to prevailing but irrelevant inputs improves object recognition and the adaptation computations can be trained in a network trained to maximize recognition performance. These results show that an intrinsic fatigue mechanism can account for key neurophysiological and perceptual properties and enhance visual processing by incorporating temporal context.

scholarly journals Shape-invariant encoding of dynamic primate facial expressions in human perception

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Nick Taubert ◽  
Michael Stettler ◽  
Ramona Siebert ◽  
Silvia Spadacenta ◽  
Louisa Sting ◽  
...  
Keyword(s):  
Neural Network ◽  
Facial Expressions ◽  
Visual Processing ◽  
Human Perception ◽  
Network Models ◽  
Neuromuscular Control ◽  
Expression Recognition ◽  
Neural Network Models ◽  
Facial Shape ◽  
Alternative Hypotheses

Dynamic facial expressions are crucial for communication in primates. Due to the difficulty to control shape and dynamics of facial expressions across species, it is unknown how species-specific facial expressions are perceptually encoded and interact with the representation of facial shape. While popular neural network models predict a joint encoding of facial shape and dynamics, the neuromuscular control of faces evolved more slowly than facial shape, suggesting a separate encoding. To investigate these alternative hypotheses, we developed photo-realistic human and monkey heads that were animated with motion capture data from monkeys and humans. Exact control of expression dynamics was accomplished by a Bayesian machine-learning technique. Consistent with our hypothesis, we found that human observers learned cross-species expressions very quickly, where face dynamics was represented largely independently of facial shape. This result supports the co-evolution of the visual processing and motor control of facial expressions, while it challenges appearance-based neural network theories of dynamic expression recognition.

Repetition Suppression of Induced Gamma Activity Predicts Enhanced Orienting toward a Novel Stimulus in 6-month-old Infants

2008 ◽  
Vol 20 (12) ◽  
pp. 2137-2152 ◽  
Author(s):  
Kelly A. Snyder ◽  
Andreas Keil
Keyword(s):  
Visual Processing ◽  
Novelty Detection ◽  
Neural Mechanisms ◽  
Human Infant ◽  
Gamma Activity ◽  
Repeated Stimulus ◽  
Repetition Suppression ◽  
The Face ◽  
Over 40 Years

Habituation refers to a decline in orienting or responding to a repeated stimulus, and can be inferred to reflect learning about the properties of the repeated stimulus when followed by increased orienting to a novel stimulus (i.e., novelty detection). Habituation and novelty detection paradigms have been used for over 40 years to study perceptual and mnemonic processes in the human infant, yet important questions remain about the nature of these processes in infants. The aim of the present study was to examine the neural mechanisms underlying habituation and novelty detection in infants. Specifically, we investigated changes in induced alpha, beta, and gamma activity in 6-month-old infants during repeated presentations of either a face or an object, and examined whether these changes predicted behavioral responses to novelty at test. We found that induced gamma activity over occipital scalp regions decreased with stimulus repetition in the face condition but not in the toy condition, and that greater decreases in the gamma band were associated with enhanced orienting to a novel face at test. The pattern and topography of these findings are consistent with observations of repetition suppression in the occipital–temporal visual processing pathway, and suggest that encoding in infant habituation paradigms may reflect a form of perceptual learning. Implications for the role of repetition suppression in infant habituation and novelty detection are discussed with respect to a biased competition model of visual attention.

scholarly journals A Study of Defect Detection Techniques for Metallographic Images

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5593 ◽  
Author(s):  
Wei-Hung Wu ◽  
Jen-Chun Lee ◽  
Yi-Ming Wang
Keyword(s):  
Neural Network ◽  
Defect Detection ◽  
Network Architecture ◽  
Recognition Performance ◽  
Metallographic Analysis ◽  
Automatic Identification ◽  
Detection Techniques ◽  
Multi Scale ◽  
Modified Method ◽  
Metallographic Images

Metallography is the study of the structure of metals and alloys. Metallographic analysis can be regarded as a detection tool to assist in identifying a metal or alloy, to evaluate whether an alloy is processed correctly, to inspect multiple phases within a material, to locate and characterize imperfections such as voids or impurities, or to find the damaged areas of metallographic images. However, the defect detection of metallography is evaluated by human experts, and its automatic identification is still a challenge in almost every real solution. Deep learning has been applied to different problems in computer vision since the proposal of AlexNet in 2012. In this study, we propose a novel convolutional neural network architecture for metallographic analysis based on a modified residual neural network (ResNet). Multi-scale ResNet (M-ResNet), the modified method, improves efficiency by utilizing multi-scale operations for the accurate detection of objects of various sizes, especially small objects. The experimental results show that the proposed method yields an accuracy of 85.7% (mAP) in recognition performance, which is higher than existing methods. As a consequence, we propose a novel system for automatic defect detection as an application for metallographic analysis.

scholarly journals Neutrosophy for physiological data compression: in particular by neural nets using deep learning

2020 ◽  
pp. 74-80
Author(s):  
Philippe Schweizer ◽  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Boundary Surface ◽  
Human Perception ◽  
Neural Nets ◽  
Physiological Data ◽  
Compression Process ◽  
The Neural Network ◽  
Algorithmic Solution ◽  
Algorithmic Techniques

We would like to show the small distance in neutropsophy applications in sciences and humanities, has both finally consider as a terminal user a human. The pace of data production continues to grow, leading to increased needs for efficient storage and transmission. Indeed, the consumption of this information is preferably made on mobile terminals using connections invoiced to the user and having only reduced storage capacities. Deep learning neural networks have recently exceeded the compression rates of algorithmic techniques for text. We believe that they can also significantly challenge classical methods for both audio and visual data (images and videos). To obtain the best physiological compression, i.e. the highest compression ratio because it comes closest to the specificity of human perception, we propose using a neutrosophical representation of the information for the entire compression-decompression cycle. Such a representation consists for each elementary information to add to it a simple neutrosophical number which informs the neural network about its characteristics relative to compression during this treatment. Such a neutrosophical number is in fact a triplet (t,i,f) representing here the belonging of the element to the three constituent components of information in compression; 1° t = the true significant part to be preserved, 2° i = the inderterminated redundant part or noise to be eliminated in compression and 3° f = the false artifacts being produced in the compression process (to be compensated). The complexity of human perception and the subtle niches of its defects that one seeks to exploit requires a detailed and complex mapping that a neural network can produce better than any other algorithmic solution, and networks with deep learning have proven their ability to produce a detailed boundary surface in classifiers.

scholarly journals Idiosyncratic Perception: A Link Between Acuity, Perceived Position, and Apparent Size

2020 ◽  
Author(s):  
Zixuan Wang ◽  
Yuki Murai ◽  
David Whitney
Keyword(s):  
Visual Field ◽  
Visual Processing ◽  
Human Perception ◽  
Visual Space ◽  
Apparent Size ◽  
Matching Task ◽  
Object Size ◽  
Vernier Acuity ◽  
Visual Hierarchy ◽  
Different Levels

AbstractPerceiving the positions of objects is a prerequisite for most other visual and visuomotor functions, but human perception of object position varies from one individual to the next. The source of these individual differences in perceived position and their perceptual consequences are unknown. Here, we tested whether idiosyncratic biases in the underlying representation of visual space propagate across different levels of visual processing. In Experiment 1, using a position matching task, we found stable, observer-specific compressions and expansions within local regions throughout the visual field. We then measured Vernier acuity (Experiment 2) and perceived size of objects (Experiment 3) across the visual field and found that individualized spatial distortions were closely associated with variations in both visual acuity and apparent object size. Our results reveal idiosyncratic biases in perceived position and size, originating from a heterogeneous spatial resolution that carries across the visual hierarchy.

scholarly journals Face Recognition Using Neural Networks

2011 ◽  
pp. 76-79
Author(s):  
V. Ramya ◽  
G. Sivashankari
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Recognition Performance ◽  
Back Propagation ◽  
Rbf Networks ◽  
End Point ◽  
Novel Approach ◽  
Wide Variability ◽  
Human Faces ◽  
Face Localization

Face recognition from the images is challenging due to the wide variability of face appearances and the complexity of the image background. This paper proposes a novel approach for recognizing the human faces. The recognition is done by comparing the characteristics of the new face to that of known individuals. It has Face localization part, where mouth end point and eyeballs will be obtained. In feature Extraction, Distance between eyeballs and mouth end point will be calculated. The recognition is performed by Neural Network (NN) using Back Propagation Networks (BPN) and Radial Basis Function (RBF) networks. The recognition performance of the proposed method is tabulated based on the experiments performed on a number of images.

Temporal Correlations in Presentation Order during Learning Affects Human Object Recognition

Perception ◽  
1997 ◽  
Vol 26 (1_suppl) ◽  
pp. 33-33
Author(s):  
G M Wallis ◽  
H H Bülthoff
Keyword(s):  
Neural Network ◽  
Object Recognition ◽  
Recognition Performance ◽  
Presentation Order ◽  
Temporal Association ◽  
Spatial Correlations ◽  
Temporal Correlations ◽  
Human Object ◽  
Network Simulations ◽  
Recognition Learning

The view-based approach to object recognition supposes that objects are stored as a series of associated views. Although representation of these views as combinations of 2-D features allows generalisation to similar views, it remains unclear how very different views might be associated together to allow recognition from any viewpoint. One cue present in the real world other than spatial similarity, is that we usually experience different objects in temporally constrained, coherent order, and not as randomly ordered snapshots. In a series of recent neural-network simulations, Wallis and Baddeley (1997 Neural Computation9 883 – 894) describe how the association of views on the basis of temporal as well as spatial correlations is both theoretically advantageous and biologically plausible. We describe an experiment aimed at testing their hypothesis in human object-recognition learning. We investigated recognition performance of faces previously presented in sequences. These sequences consisted of five views of five different people's faces, presented in orderly sequence from left to right profile in 45° steps. According to the temporal-association hypothesis, the visual system should associate the images together and represent them as different views of the same person's face, although in truth they are images of different people's faces. In a same/different task, subjects were asked to say whether two faces seen from different viewpoints were views of the same person or not. In accordance with theory, discrimination errors increased for those faces seen earlier in the same sequence as compared with those faces which were not ( p<0.05).

Sign in / Sign up

Export Citation Format

Share Document