scholarly journals DeepCEST 3T: Robust MRI parameter determination and uncertainty quantification with neural networks—application to CEST imaging of the human brain at 3T

2019 ◽  
Vol 84 (1) ◽  
pp. 450-466 ◽  
Author(s):  
Felix Glang ◽  
Anagha Deshmane ◽  
Sergey Prokudin ◽  
Florian Martin ◽  
Kai Herz ◽  
...  
Keyword(s):  
Neural Networks ◽  
Human Brain ◽  
Uncertainty Quantification ◽  
Parameter Determination ◽  
Cest Imaging

Representation of adversarial images in deep neural networks and the human brain

2018 ◽  
Author(s):  
Chi Zhang ◽  
Xiaohan Duan ◽  
Ruyuan Zhang ◽  
Li Tong
Keyword(s):  
Neural Networks ◽  
Human Brain ◽  
Deep Neural Networks

scholarly journals The ventral visual pathway represents animal appearance over animacy, unlike human behavior and deep neural networks

2017 ◽  
Author(s):  
Stefania Bracci ◽  
Ioannis Kalfas ◽  
Hans Op de Beeck
Keyword(s):  
Neural Networks ◽  
Human Brain ◽  
Deep Neural Networks ◽  
Activity Patterns ◽  
Object Identification ◽  
Visual Appearance ◽  
Learning History ◽  
Fmri Study ◽  
Object Appearance ◽  
Biased Processing

AbstractRecent studies showed agreement between how the human brain and neural networks represent objects, suggesting that we might start to understand the underlying computations. However, we know that the human brain is prone to biases at many perceptual and cognitive levels, often shaped by learning history and evolutionary constraints. Here we explore one such bias, namely the bias to perceive animacy, and used the performance of neural networks as a benchmark. We performed an fMRI study that dissociated object appearance (how an object looks like) from object category (animate or inanimate) by constructing a stimulus set that includes animate objects (e.g., a cow), typical inanimate objects (e.g., a mug), and, crucially, inanimate objects that look like the animate objects (e.g., a cow-mug). Behavioral judgments and deep neural networks categorized images mainly by animacy, setting all objects (lookalike and inanimate) apart from the animate ones. In contrast, activity patterns in ventral occipitotemporal cortex (VTC) were strongly biased towards object appearance: animals and lookalikes were similarly represented and separated from the inanimate objects. Furthermore, this bias interfered with proper object identification, such as failing to signal that a cow-mug is a mug. The bias in VTC to represent a lookalike as animate was even present when participants performed a task requiring them to report the lookalikes as inanimate. In conclusion, VTC representations, in contrast to neural networks, fail to veridically represent objects when visual appearance is dissociated from animacy, probably due to a biased processing of visual features typical of animate objects.

Volcano-Seismic Transfer Learning and Uncertainty Quantification With Bayesian Neural Networks

2020 ◽  
Vol 58 (2) ◽  
pp. 892-902 ◽  
Author(s):  
Angel Bueno ◽  
Carmen Benitez ◽  
Silvio De Angelis ◽  
Alejandro Diaz Moreno ◽  
Jesus M. Ibanez
Keyword(s):  
Neural Networks ◽  
Uncertainty Quantification ◽  
Transfer Learning ◽  
Bayesian Neural Networks

scholarly journals Towards simulations of long-term behavior of neural networks: Modeling synaptic plasticity of connections within and between human brain regions

2020 ◽  
Vol 416 ◽  
pp. 38-44
Author(s):  
Emmanouil Giannakakis ◽  
Cheol E. Han ◽  
Bernd Weber ◽  
Frances Hutchings ◽  
Marcus Kaiser
Keyword(s):  
Neural Networks ◽  
Synaptic Plasticity ◽  
Human Brain ◽  
Brain Regions ◽  
Long Term Behavior
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