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[Illinois] B3SI 2012: Closing Session
17 Dec 2013 | Online Presentations | Contributor(s): Rashid Bashir, Lizzanne DeStefano, Hsiang-Yu Angie Wang, Hao-Ming Hsiao, Jimmy K. Hsia, Laura Arriola Miller
Deeper understandings of biological systems and advances in nanotechnology have provided new opportunities to make significant progress in mechanobiology, biosensing and dynamic control of...
[Illinois] Microcapsules for Luminescent Tracking and Controlled Drug Delivery
16 Dec 2013 | Online Presentations | Contributor(s): Yulia Maximenko
Polyelectrolyte microcapsules are formed on porous calcium carbonate templates that are impregnated and coated with 2.9 nanometer luminescent silicon nanoparticles. The complexes are characterized...
[Illinois] MCB 493 Lecture 13: Predictor-Corrector Models and Probabilistic Inference
30 Oct 2013 | Online Presentations | Contributor(s): Thomas J. Anastasio
[Illinois] MCB 493 Lecture 10: Time Series Learning and Nonlinear Signal Processing
[Illinois] MCB 493 Lecture 1: Vectors, Matrices, and Basic Neural Computations
Using mathematical and computational methods to simulate many aspects of neural systems function.
[Illinois] MCB 493 Lecture 3: Forward and Recurrent Lateral Inhibition
Networks with forward and recurrent laterally inhibitory connectivity profiles can shape signals in space and time, and simulate certain forms of sensory and motor processing.
[Illinois] MCB 493 Lecture 6: Supervised Learning and Non-Uniform Representations
Supervised learning algorithms can train neural networks to associate patterns and simulate the non-uniform distributed representations found in many brain regions.
[Illinois] MCB 493 Lecture 7: Reinforcement Learning and Associative Conditioning
Reinforcement learning algorithms can simulate certain types of associative conditioning and train neural networks to form non-uniform distributed representations.
[Illinois] MCB 493 Lecture 2: Recurrent Connections and Simple Neural Circuits
29 Oct 2013 | Online Presentations | Contributor(s): Thomas J. Anastasio
Networks with recurrent connections, forming circuits, and containing only a few neural units can shape signals in time, produce oscillations, and simulate certain forms of low-level motor control.
[Illinois] MCB 493 Lecture 4: Covariation Learning and Auto-Associative Memory
Networks with recurrent connection weights that reflect the covariation between pattern elements can dynamically recall patterns and simulate certain forms of memory.
[Illinois] MCB 493 Lecture 5: Unsupervised Learning and Distributed Representations
Unsupervised learning algorithms, given only a set of input patterns, can train neural networks to form distributed representations of those patterns that resemble brain maps.
[Illinois] MCB 493 Lecture 8: Information Transmission and Unsupervised Learning
Unsupervised learning algorithms can train neural networks to increase the amount of information they contain about their inputs and simulate the properties of sensory neurons.
[Illinois] Phys550 Lecture 14: Physics of the Neuron II
29 Oct 2013 | Online Presentations | Contributor(s): Klaus Schulten
[Illinois] Phys550 Lecture 15: Physics of the Neuron III
Molecular Dynamics Showcase
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14 Oct 2013 | Tools | Contributor(s): Michael McLennan, Chen-Yu Li, john stone, Aleksei Aksimentiev, George A. Howlett
View interesting features of a molecular dynamics trajectory file
[Illinois] Phys550 Lecture 12: Stochastic Processes III
07 Oct 2013 | Online Presentations | Contributor(s): Klaus Schulten
[Illinois] Phys550 Lecture 11: Stochastic Processes II
[Illinois] Phys550 Lecture 8: Vision II
02 Oct 2013 | Online Presentations | Contributor(s): Klaus Schulten
[Illinois] Phys550 Lecture 10: Stochastic Processes I
[Illinois] Phys550 Lecture 9: Protein Overview