scholarly journals Efficient inverse graphics in biological face processing

2018 ◽  
Author(s):  
Ilker Yildirim ◽  
Mario Belledonne ◽  
Winrich Freiwald ◽  
Joshua Tenenbaum
Keyword(s):  
Three Dimensional ◽  
Generative Models ◽  
Sensory Data ◽  
Plausible Model ◽  
Analysis By Synthesis ◽  
Underlying Causes ◽  
Multiple Levels ◽  
High Level ◽  
The Brain ◽  
Better Than

Vision must not only recognize and localize objects, but perform richer inferences about the underlying causes in the world that give rise to sensory data. How the brain performs these inferences remains unknown: Theoretical proposals based on inverting generative models (or “analysis-by-synthesis”) have a long history but their mechanistic implementations have typically been too slow to support online perception, and their mapping to neural circuits is unclear. Here we present a neurally plausible model for efficiently inverting generative models of images and test it as an account of one high-level visual capacity, the perception of faces. The model is based on a deep neural network that learns to invert a three-dimensional (3D) face graphics program in a single fast feedforward pass. It explains both human behavioral data and multiple levels of neural processing in non-human primates, as well as a classic illusion, the “hollow face” effect. The model fits qualitatively better than state-of-the-art computer vision models, and suggests an interpretable reverse-engineering account of how images are transformed into percepts in the ventral stream.

scholarly journals Efficient inverse graphics in biological face processing

2020 ◽  
Vol 6 (10) ◽  
pp. eaax5979 ◽  
Author(s):  
Ilker Yildirim ◽  
Mario Belledonne ◽  
Winrich Freiwald ◽  
Josh Tenenbaum
Keyword(s):  
Face Processing ◽  
Three Dimensional ◽  
Generative Models ◽  
Neural Data ◽  
Primate Brain ◽  
Scene Structure ◽  
Analysis By Synthesis ◽  
The Brain ◽  
Processing Circuit ◽  
Better Than

Vision not only detects and recognizes objects, but performs rich inferences about the underlying scene structure that causes the patterns of light we see. Inverting generative models, or “analysis-by-synthesis”, presents a possible solution, but its mechanistic implementations have typically been too slow for online perception, and their mapping to neural circuits remains unclear. Here we present a neurally plausible efficient inverse graphics model and test it in the domain of face recognition. The model is based on a deep neural network that learns to invert a three-dimensional face graphics program in a single fast feedforward pass. It explains human behavior qualitatively and quantitatively, including the classic “hollow face” illusion, and it maps directly onto a specialized face-processing circuit in the primate brain. The model fits both behavioral and neural data better than state-of-the-art computer vision models, and suggests an interpretable reverse-engineering account of how the brain transforms images into percepts.

scholarly journals Electrophysiological signatures of hierarchical learning

2021 ◽  
Author(s):  
Meng Liu ◽  
Wenshan Dong ◽  
Shaozheng Qin ◽  
Tom Verguts ◽  
Qi Chen
Keyword(s):  
Cognitive Process ◽  
Human Perception ◽  
Learning Task ◽  
Prediction Errors ◽  
Neural Basis ◽  
Hierarchical Learning ◽  
Low Level ◽  
High Level ◽  
The Brain ◽  
Better Than

AbstractHuman perception and learning is thought to rely on a hierarchical generative model that is continuously updated via precision-weighted prediction errors (pwPEs). However, the neural basis of such cognitive process and how it unfolds during decision making, remain poorly understood. To investigate this question, we combined a hierarchical Bayesian model (i.e., Hierarchical Gaussian Filter, HGF) with electrophysiological (EEG) recording, while participants performed a probabilistic reversal learning task in alternatingly stable and volatile environments. Behaviorally, the HGF fitted significantly better than two control, non-hierarchical, models. Neurally, low-level and high-level pwPEs were independently encoded by the P300 component. Low-level pwPEs were reflected in the theta (4-8 Hz) frequency band, but high-level pwPEs were not. Furthermore, the expressions of high-level pwPEs were stronger for participants with better HGF fit. These results indicate that the brain employs hierarchical learning, and encodes both low- and high-level learning signals separately and adaptively.

scholarly journals A neurally plausible model for online recognition and postdiction in a dynamical environment

2019 ◽  
Author(s):  
Li Kevin Wenliang ◽  
Maneesh Sahani
Keyword(s):  
Internal Model ◽  
Dynamic Models ◽  
Causal Structure ◽  
Current Time ◽  
Sensory Data ◽  
Inferential Model ◽  
Plausible Model ◽  
Plausible Mechanism ◽  
Sensory Evidence ◽  
The Brain

AbstractHumans and other animals are frequently near-optimal in their ability to integrate noisy and ambiguous sensory data to form robust percepts, which are informed both by sensory evidence and by prior experience about the causal structure of the environment. It is hypothesized that the brain establishes these structures using an internal model of how the observed patterns can be generated from relevant but unobserved causes. In dynamic environments, such integration often takes the form of postdiction, wherein later sensory evidence affects inferences about earlier percepts. As the brain must operate in current time, without the luxury of acausal propagation of information, how does such postdictive inference come about? Here, we propose a general framework for neural probabilistic inference in dynamic models based on the distributed distributional code (DDC) representation of uncertainty, naturally extending the underlying encoding to incorporate implicit probabilistic beliefs about both present and past. We show that, as in other uses of the DDC, an inferential model can be learned efficiently using samples from an internal model of the world. Applied to stimuli used in the context of psychophysics experiments, the framework provides an online and plausible mechanism for inference, including postdictive effects.

Exploring the Multidimensional Facets of Authoritarianism: Authoritarian Aggression and Social Dominance Orientation

2008 ◽  
Vol 67 (1) ◽  
pp. 51-60 ◽  
Author(s):  
Stefano Passini
Keyword(s):  
Social Dominance ◽  
Factor Model ◽  
Three Dimensional ◽  
Social Dominance Orientation ◽  
Model Fit ◽  
Regression Analyses ◽  
One Dimensional ◽  
Confirmatory Factor ◽  
The One ◽  
Better Than

The relation between authoritarianism and social dominance orientation was analyzed, with authoritarianism measured using a three-dimensional scale. The implicit multidimensional structure (authoritarian submission, conventionalism, authoritarian aggression) of Altemeyer’s (1981, 1988) conceptualization of authoritarianism is inconsistent with its one-dimensional methodological operationalization. The dimensionality of authoritarianism was investigated using confirmatory factor analysis in a sample of 713 university students. As hypothesized, the three-factor model fit the data significantly better than the one-factor model. Regression analyses revealed that only authoritarian aggression was related to social dominance orientation. That is, only intolerance of deviance was related to high social dominance, whereas submissiveness was not.

Guided surgery with 3D printed device: a case report

2020 ◽  
Author(s):  
Michelle Carvalho de Sales ◽  
Rafael Maluza Flores ◽  
Julianny da Silva Guimaraes ◽  
Gustavo Vargas da Silva Salomao ◽  
Tamara Kerber Tedesco ◽  
...  
Keyword(s):  
Operating Time ◽  
Three Dimensional ◽  
Conventional Technique ◽  
Cervical Region ◽  
Mean Deviation ◽  
Apical Region ◽  
Virtual Planning ◽  
Surgical Guide ◽  
Surgical Guides ◽  
High Level

Dental surgeons need in-depth knowledge of the bone tissue status and gingival morphology of atrophic maxillae. The aim of this study is to describe preoperative virtual planning of placement of five implants and to compare the plan with the actual surgical results. Three-dimensional planning of rehabilitation using software programs enables surgical guides to be specially designed for the implant site and manufactured using 3D printing. A patient with five teeth missing was selected for this study. The patient’s maxillary region was scanned with CBCT and a cast model was produced. After virtual planning using ImplantViewer, five implants were placed using a printed surgical guide. Two weeks after the surgical procedure, the patient underwent another CBCT scan of the maxilla. Statistically significant differences were detected between the virtually planned positions and the actual positions of the implants, with a mean deviation of 0.36 mm in the cervical region and 0.7 mm in the apical region. The surgical technique used enables more accurate procedures when compared to the conventional technique. Implants can be better positioned, with a high level of predictability, reducing both operating time and patient discomfort.

A Three-Dimensional Tetraphenylethylene-Based Fluorescence Covalent Organic Framework for Molecular Recognition

2020 ◽  
Author(s):  
Junxia Ren ◽  
Yaozu Liu ◽  
Xin Zhu ◽  
Yangyang Pan ◽  
Yujie Wang ◽  
...  
Keyword(s):  
Molecular Recognition ◽  
Fluorescence Probe ◽  
Three Dimensional ◽  
Hazardous Substances ◽  
Covalent Organic Framework ◽  
Highly Sensitive ◽  
Volatile Organic ◽  
Polycyclic Aromatic ◽  
Better Than ◽  
Organic Framework

<p><a></a><a></a><a></a><a></a><a></a><a></a><a></a><a>The development of highly-sensitive recognition of </a><a></a><a></a><a></a><a></a><a>hazardous </a>chemicals, such as volatile organic compounds (VOCs) and polycyclic aromatic hydrocarbons (PAHs), is of significant importance because of their widespread social concerns related to environment and human health. Here, we report a three-dimensional (3D) covalent organic framework (COF, termed JUC-555) bearing tetraphenylethylene (TPE) side chains as an aggregation-induced emission (AIE) fluorescence probe for sensitive molecular recognition.<a></a><a> </a>Due to the rotational restriction of TPE rotors in highly interpenetrated framework after inclusion of dimethylformamide (DMF), JUC-555 shows impressive AIE-based strong fluorescence. Meanwhile, owing to the large pore size (11.4 Å) and suitable intermolecular distance of aligned TPE (7.2 Å) in JUC-555, the obtained material demonstrates an excellent performance in the molecular recognition of hazardous chemicals, e.g., nitroaromatic explosives, PAHs, and even thiophene compounds, via a fluorescent quenching mechanism. The quenching constant (<i>K</i><sub>SV</sub>) is two orders of magnitude better than those of other fluorescence-based porous materials reported to date. This research thus opens 3D functionalized COFs as a promising identification tool for environmentally hazardous substances.</p>

Visible Laser Probing (VLP) with GaP Solid Immersion Lens Demonstrating 110 nm Resolution in Common Laser Probing Applications

2016 ◽  
Author(s):  
Travis Eiles ◽  
Patrick Pardy
Keyword(s):  
Gallium Phosphide ◽  
Fault Isolation ◽  
Solid Immersion Lens ◽  
Laser Probing ◽  
Isolation Methods ◽  
Visible Laser ◽  
High Level ◽  
Industry Needs ◽  
Diameter Reduction ◽  
Better Than

Abstract This paper demonstrates a breakthrough method of visible laser probing (VLP), including an optimized 577 nm laser microscope, visible-sensitive detector, and an ultimate-resolution gallium phosphide-based solid immersion lens on the 10 nm node, showing a 110 nm resolution. This is 2x better than what is achieved with the standard suite of probing systems using typical infrared (IR) wavelengths today. Since VLP provides a spot diameter reduction of 0.5x over IR methods, it is reasonable, based simply on geometry, to project that VLP using the 577 nm laser will meet the industry needs for laser probing for both the 10 nm and 7 nm process nodes. Based on its high level of optimization, including high resolution and specialized solid immersion lens, it is highly likely that this VLP technology will be one of the last optically-based fault isolation methods successfully used.

Consciousness

2018 ◽  
Author(s):  
Anil K. Seth
Keyword(s):  
Conscious Experience ◽  
Real Problem ◽  
Active Inference ◽  
Biological Mechanisms ◽  
Specific Content ◽  
Brain Responses ◽  
Biomimetic Approach ◽  
Multiple Levels ◽  
In Virtue Of ◽  
The Brain

Consciousness is perhaps the most familiar aspect of our existence, yet we still do not know its biological basis. This chapter outlines a biomimetic approach to consciousness science, identifying three principles linking properties of conscious experience to potential biological mechanisms. First, conscious experiences generate large quantities of information in virtue of being simultaneously integrated and differentiated. Second, the brain continuously generates predictions about the world and self, which account for the specific content of conscious scenes. Third, the conscious self depends on active inference of self-related signals at multiple levels. Research following these principles helps move from establishing correlations between brain responses and consciousness towards explanations which account for phenomenological properties—addressing what can be called the “real problem” of consciousness. The picture that emerges is one in which consciousness, mind, and life, are tightly bound together—with implications for any possible future “conscious machines.”

Sign in / Sign up

Export Citation Format

Share Document