... | ... | @@ -30,15 +30,11 @@ This talk presents HeAT, an in-house developed scientific big data library suppo |
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The Three-dimensional Polarized Light Imaging (3D-PLI) technology is used to capture high resolution images of thinly sliced segments of post-mortem brains. These images are then stacked to reconstruct the brain in 3D, which enables tracking of individual nerve fibers through the entire brain. We are investigating the application of deep Convolutional Neural Networks (CNN) for the precise demarcation of the highly irregular border between the brain tissue and the background in each image. In this talk, we’ll present challenges presented by this problem, and the solutions explored so far.
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### 15:30 - 15:50 <br>Fahad Khalid: Comparing internal dynamics of artificial recurrent neural networks with biologically plausible models of neural circuits
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For a given cognitive task, what are the differences and/or similarities between the solutions employed by the brain, and the models engineered using deep artificial neural networks? By employing direct and systematic comparisons between biologically inspired spiking neural network (SNN) models and state of-the-art artificial neural networks (ANN), we hope to gain insights into the nature and types of solutions that different systems find for the same problem domains, and use them to improve the current understanding of fundamental principles of neural computation and cognitive processing. In this talk, we’ll present our approach to tackle this research question with a focus on artificial recurrent neural networks for symbolic sequence processing.
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### 15:50 - 16:10 <br>Sandra Diaz: L2L on High Performance Computing (HPC) - JUPeX
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### 15:30 - 15:50 <br>Sandra Diaz: L2L on High Performance Computing (HPC) - JUPeX
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The effective usage of supercomputers to understand the brain is one of the key endeavors in the Human Brain Project. In order to ease the process of optimizing the behavior of brain models and based on a technique known in machine learning as Learning to Learn (L2L), the Graz Institute of Technology together with the Jülich Supercomputing Center are developing JUPeX. This software helps scientists explore, optimize and better understand the models they work with every day by enabling parameter space exploration and optimization on HPC. Although it is currently used mostly for neuroscience, the software and methodology is generally applicable to all scientific domains. JUPeX is integrated in the L2L framwork which can be downloaded here: https://github.com/IGITUGraz/L2L.
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### 16:10 - 16:30 <br>Kai Krajsek: Inverse Modeling of Brain Microstructures by Deep Learning
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### 15:50 - 16:10 <br>Kai Krajsek: Inverse Modeling of Brain Microstructures by Deep Learning
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Microstructures in the brain can be estimated by inverting models relating the brain microstructure with a measurable MRI signal. Established methods for inverting such models based on variational optimization or based on probabilistic estimation theory require a closed form forward model. This requirement is not fulfilled for modern fine grained models simulating DTI signals of brain microstructures by stochastic processes. This talk discusses alternative inversion methods based on Deep Learning.
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... | ... | @@ -46,4 +42,4 @@ Microstructures in the brain can be estimated by inverting models relating the b |
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last change: 26.3.2019 sw |
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last change: 26.3.2019 sw |
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