A computational model reveals that the face domain of macaque inferotemporal cortex (IT) represents multiple visual features

2011 ◽  
Vol 71 ◽  
pp. e71 ◽  
Author(s):  
Takashi Owaki ◽  
Michel Vidal-Naquet ◽  
Takayuki Sato ◽  
Hideyuki Cateau ◽  
Shimon Ullman ◽  
...  
Keyword(s):  
Computational Model ◽  
Visual Features ◽  
Inferotemporal Cortex ◽  
The Face

scholarly journals View-tuned and view-invariant face encoding in IT cortex is explained by selected natural image fragments

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yunjun Nam ◽  
Takayuki Sato ◽  
Go Uchida ◽  
Ekaterina Malakhova ◽  
Shimon Ullman ◽  
...  
Keyword(s):  
Face Recognition ◽  
Visual Pathway ◽  
Visual Features ◽  
Natural Image ◽  
Face Representation ◽  
Low Level ◽  
Ventral Visual Pathway ◽  
Neural Substrate ◽  
The Face

AbstractHumans recognize individual faces regardless of variation in the facial view. The view-tuned face neurons in the inferior temporal (IT) cortex are regarded as the neural substrate for view-invariant face recognition. This study approximated visual features encoded by these neurons as combinations of local orientations and colors, originated from natural image fragments. The resultant features reproduced the preference of these neurons to particular facial views. We also found that faces of one identity were separable from the faces of other identities in a space where each axis represented one of these features. These results suggested that view-invariant face representation was established by combining view sensitive visual features. The face representation with these features suggested that, with respect to view-invariant face representation, the seemingly complex and deeply layered ventral visual pathway can be approximated via a shallow network, comprised of layers of low-level processing for local orientations and colors (V1/V2-level) and the layers which detect particular sets of low-level elements derived from natural image fragments (IT-level).

scholarly journals Interactions Among mTORC, AMPK and SIRT A Computational Model for Cell Energy Balance and Metabolism

2020 ◽  
Author(s):  
Mehrshad Sadria ◽  
Anita T. Layton
Keyword(s):  
Energy Balance ◽  
Computational Model ◽  
Metabolic Stress ◽  
Cellular Aging ◽  
Signalling Pathways ◽  
Environmental Signals ◽  
Metabolic Signalling ◽  
The Face ◽  
Cell Energy ◽  
Salvage Pathways

Abstract BackgroundCells adapt their metabolism and activities in response to signals from their surroundings, and this ability is essential for their survival in the face of perturbations. In tissues a deficit of these mechanisms is commonly associated with cellular aging and diseases, such as cardiovascular disease, cancer, immune system decline, and neurological pathologies. Several proteins have been identified as being able to respond directly to energy, nutrient, and growth factor levels and stress stimuli in order to mediate adaptations in the cell. In particular, mTOR, AMPK, and sirtuins are known to play an essential role in the management of metabolic stress and energy balance in mammals.MethodsTo understand the complex interactions of these signalling pathways and environmental signals, and how those interactions may impact lifespan and health-span, we have developed a computational model of metabolic signalling pathways. Specifically, the model includes the insulin/IGF-1 pathway, which couples energy and nutrient abundance to the execution of cell growth and division, (ii) mTORC1 and the amino acid sensors such as sestrin, (iii) the Preiss-Handler and salvage pathways, which regulate the metabolism of NAD+ and the NAD+-consuming factor SIRT1, (iv) the energy sensor AMPK, and (v) transcription factors FOXO and PGC-1α.ResultsThe model simulates the interactions among key regulators such as Akt, mTORC1, AMPK, NAD+, and SIRT, and predicts their dynamics. Key findings include the clinically important role of PRAS40 and diet in mTORC1 inhibition, and a potential link between SIRT1-activating compounds and premature autophagy. Moreover, the model captures the exquisite interactions of leucine, sestrin2, and arginine, and the resulting signal to the mTORC1 pathway. These results can be leveraged in the development of novel treatment of cancers and other diseases.ConclusionsThis study presents a state-of-the-art computational model for investigating the interactions among signaling pathways and environmental stimuli in growth, ageing, metabolism, and diseases. The model can be used as an essential component to simulate gene manipulation, therapies (e.g., rapamycin and wortmannin), calorie restrictions, and chronic stress, and assess their functional implications on longevity and ageing‐related diseases.

Multi-scale Modeling and Sensitivity Analysis in Biological Systems

2021 ◽  
Author(s):  
◽  
An Do Dela
Keyword(s):  
Sensitivity Analysis ◽  
Computational Model ◽  
Type I Diabetes ◽  
Model Fitting ◽  
Type I ◽  
Stochastic Fluctuations ◽  
Highly Nonlinear ◽  
Critical Issues ◽  
The Face

We develop a multi-method sensitivity framework, which incorporates two variance-based methods, namely Sobol's method, eFAST and Derivative-based global measures to identify which parameters are most influential to the model outputs. A new implementation version of eFAST, namely DeFAST, was developed to address some critical issues in an existing published algorithm. Sensitivity analysis is a powerful tool in the modeling process that can be leveraged in various ways including model reduction and model fitting to data. There are two novel models that have been developed in this work where sensitivity analysis was applied. A stochastic computational model was constructed to understand mechanistic division event in Caulobacter crecentus bacterium in order to investigate how precise measurements can be made at the micron scale in the face of stochastic fluctuations. In this context, sensitivity analysis is used to derive a minimal PDE model in a minimal intermittent-search framework that could capture key results of the computational model closely. In addition, a new single compartment mathematical model for type I diabetes was analyzed to understand which parameters are the main driver of the blood glucose dynamics with the intention to understand the curative potential of dendritic-cell-based vaccine therapies. In this case, the sensitivity analysis was used to rank parameters and reduce the parameter space so that we can calibrate the model with in-vivo data in the future. The novelty of this work is that we validate our sensitivity analysis approach on highly nonlinear and stochastic models. These complex models present significant challenges for the application of sensitivity analysis algorithms as compared to the simpler case-study models that are typically used for testing sensitivity analysis methods.

scholarly journals Distraction biases working memory for faces

2019 ◽  
Author(s):  
Remington Mallett ◽  
Anurima Mummaneni ◽  
Jarrod Lewis-Peacock
Keyword(s):  
Working Memory ◽  
Irrelevant Information ◽  
Visual Features ◽  
Maintenance Period ◽  
Stimulus Space ◽  
Estimation Task ◽  
Low Level ◽  
The Face ◽  
High Level ◽  
Task Irrelevant

Working memory persists in the face of distraction, yet not without consequence. Previous research has shown that memory for low-level visual features is systematically influenced by the maintenance or presentation of a similar distractor stimulus. Responses are frequently biased in stimulus space towards a perceptual distractor, though this has yet to be determined for high-level stimuli. We investigated whether these influences are shared for complex visual stimuli such as faces. To quantify response accuracies for these stimuli, we used a delayed-estimation task with a computer-generated “face space” consisting of eighty faces that varied continuously as a function of age and sex. In a set of three experiments, we found that responses for a target face held in working memory were biased towards a distractor face presented during the maintenance period. The amount of response bias did not vary as a function of distance between target and distractor. Our data suggest that, similar to low-level visual features, high-level face representations in working memory are biased by the processing of related but task-irrelevant information.

scholarly journals The role of animal faces in the animate-inanimate distinction in the ventral temporal cortex

2020 ◽  
Author(s):  
D. Proklova ◽  
M.A. Goodale
Keyword(s):  
Temporal Cortex ◽  
Visual Features ◽  
Response Patterns ◽  
Prominent Feature ◽  
Fmri Study ◽  
The Face ◽  
Per Se ◽  
Ventral Temporal Cortex ◽  
Animal Faces

AbstractAnimate and inanimate objects elicit distinct response patterns in the human ventral temporal cortex (VTC), but the exact features driving this distinction are still poorly understood. One prominent feature that distinguishes typical animals from inanimate objects and that could potentially explain the animate-inanimate distinction in the VTC is the presence of a face. In the current fMRI study, we investigated this possibility by creating a stimulus set that included animals with faces, faceless animals, and inanimate objects, carefully matched in order to minimize other visual differences. We used both searchlight-based and ROI-based representational similarity analysis (RSA) to test whether the presence of a face explains the animate-inanimate distinction in the VTC. The searchlight analysis revealed that when animals with faces were removed from the analysis, the animate-inanimate distinction almost disappeared. The ROI-based RSA revealed a similar pattern of results, but also showed that, even in the absence of faces, information about agency (a combination of animal’s ability to move and think) is present in parts of the VTC that are sensitive to animacy. Together, these analyses showed that animals with faces do elicit a stronger animate/inanimate response in the VTC, but that this effect is driven not by faces per se, or the visual features of faces, but by other factors that correlate with face presence, such as the capacity for self-movement and thought. In short, the VTC appears to treat the face as a proxy for agency, a ubiquitous feature of familiar animals.Significance StatementMany studies have shown that images of animals are processed differently from inanimate objects in the human brain, particularly in the ventral temporal cortex (VTC). However, what features drive this distinction remains unclear. One important feature that distinguishes many animals from inanimate objects is a face. Here, we used fMRI to test whether the animate/inanimate distinction is driven by the presence of faces. We found that the presence of faces did indeed boost activity related to animacy in the VTC. A more detailed analysis, however, revealed that it was the association between faces and other attributes such as the capacity for self-movement and thinking, not the faces per se, that was driving the activity we observed.

scholarly journals Build-up of serial dependence in color working memory

2018 ◽  
Author(s):  
João Barbosa ◽  
Albert Compte
Keyword(s):  
Visual Features ◽  
Experimental Session ◽  
Natural Scenes ◽  
Strongly Correlated ◽  
Natural Scene ◽  
Serial Dependence ◽  
Natural Scene Statistics ◽  
Short Term ◽  
Total N ◽  
The Face

AbstractSerial dependence, how recent experiences bias our current estimations, has been described experimentally during delayed-estimation of many different visual features, with subjects tending to make estimates biased towards previous ones. It has been proposed that these attractive biases help perception stabilization in the face of correlated natural scene statistics as an adaptive mechanism, although this remains mostly theoretical. Color, which is strongly correlated in natural scenes, has never been studied with regard to its serial dependencies. Here, we found significant serial dependence in 6 out of 7 datasets with behavioral data of humans (total n=111) performing delayed-estimation of color with uncorrelated sequential stimuli. Consistent with a drifting memory model, serial dependence was stronger when referenced relative to previous report, rather than to previous stimulus. In addition, it built up through the experimental session, suggesting metaplastic mechanisms operating at a slower time scale than previously proposed (e.g. short-term synaptic facilitation). Because, in contrast with natural scenes, stimuli were temporally uncorrelated, this build-up casts doubt on serial dependencies being an ongoing adaptation to the stable statistics of the environment.

scholarly journals Modeling Robustness of COVID-19 Containment Policies

2021 ◽  
Author(s):  
Ross Hammond ◽  
Joseph T. Ornstein ◽  
Rob Purcell ◽  
Matthew D. Haslam ◽  
Matt Kasman
Keyword(s):  
Computational Model ◽  
Containment Measures ◽  
The Face ◽  
Substantial Uncertainty

We report on results from the application of a new computational model designed to address challenges faced by policymakers in designing and implementing COVID-19 containment measures in the face of substantial uncertainty and heterogeneity.

Learning to Discriminate Face Views

2010 ◽  
Vol 104 (6) ◽  
pp. 3305-3311 ◽  
Author(s):  
Taiyong Bi ◽  
Nihong Chen ◽  
Qiujie Weng ◽  
Dongjun He ◽  
Fang Fang
Keyword(s):  
Perceptual Learning ◽  
Visual Processing ◽  
Learning Effect ◽  
Visual Features ◽  
Complex Object ◽  
Trained Subjects ◽  
Configural Information ◽  
Face Images ◽  
The Face ◽  
Face View

Although perceptual learning of simple visual features has been studied extensively and intensively for many years, we still know little about the mechanisms of perceptual learning of complex object recognition. In a series of seven experiments, human perceptual learning in discrimination of in-depth orientation of face view was studied using psychophysical methods. We trained subjects to discriminate face orientations around a face view (i.e., 30°) over eight daily sessions, which resulted in a significant improvement in sensitivity to the face view orientation. This improved sensitivity was highly specific to the trained orientation and persisted up to 6 mo. Different from perceptual learning of simple visual features, this orientation-specific learning effect could completely transfer across changes in face size, visual field, and face identity. A complete transfer also occurred between two partial face images that were mutually exclusive but constituted a complete face. However, the transfer of the learning effect between upright and inverted faces and between a face and a paperclip object was very weak. These results shed light on the mechanisms of the perceptual learning of face view discrimination. They suggest that the visual system had learned how to compute face orientation from face configural information more accurately and that a large amount of plastic changes took place at a level of higher visual processing where size-, location-, and identity-invariant face views are represented.

scholarly journals Interactions among mTORC, AMPK and SIRT: a computational model for cell energy balance and metabolism

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Mehrshad Sadria ◽  
Anita T. Layton
Keyword(s):  
Energy Balance ◽  
Computational Model ◽  
Metabolic Stress ◽  
Cellular Aging ◽  
Signalling Pathways ◽  
Environmental Signals ◽  
Metabolic Signalling ◽  
The Face ◽  
Cell Energy ◽  
Salvage Pathways

Abstract Background Cells adapt their metabolism and activities in response to signals from their surroundings, and this ability is essential for their survival in the face of perturbations. In tissues a deficit of these mechanisms is commonly associated with cellular aging and diseases, such as cardiovascular disease, cancer, immune system decline, and neurological pathologies. Several proteins have been identified as being able to respond directly to energy, nutrient, and growth factor levels and stress stimuli in order to mediate adaptations in the cell. In particular, mTOR, AMPK, and sirtuins are known to play an essential role in the management of metabolic stress and energy balance in mammals. Methods To understand the complex interactions of these signalling pathways and environmental signals, and how those interactions may impact lifespan and health-span, we have developed a computational model of metabolic signalling pathways. Specifically, the model includes (i) the insulin/IGF-1 pathway, which couples energy and nutrient abundance to the execution of cell growth and division, (ii) mTORC1 and the amino acid sensors such as sestrin, (iii) the Preiss-Handler and salvage pathways, which regulate the metabolism of NAD+ and the NAD+ -consuming factor SIRT1, (iv) the energy sensor AMPK, and (v) transcription factors FOXO and PGC-1α. Results The model simulates the interactions among key regulators such as AKT, mTORC1, AMPK, NAD+ , and SIRT, and predicts their dynamics. Key findings include the clinically important role of PRAS40 and diet in mTORC1 inhibition, and a potential link between SIRT1-activating compounds and premature autophagy. Moreover, the model captures the exquisite interactions of leucine, sestrin2, and arginine, and the resulting signal to the mTORC1 pathway. These results can be leveraged in the development of novel treatment of cancers and other diseases. Conclusions This study presents a state-of-the-art computational model for investigating the interactions among signaling pathways and environmental stimuli in growth, ageing, metabolism, and diseases. The model can be used as an essential component to simulate gene manipulation, therapies (e.g., rapamycin and wortmannin), calorie restrictions, and chronic stress, and assess their functional implications on longevity and ageing‐related diseases.

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