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Tags: Illinois

Resources (41-60 of 836)

  1. [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...

    http://nanohub.org/resources/15004

  2. [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...

    http://nanohub.org/resources/19509

  3. [Illinois] MCB 493 Lecture 13: Predictor-Corrector Models and Probabilistic Inference

    30 Oct 2013 | Online Presentations | Contributor(s): Thomas J. Anastasio

    http://nanohub.org/resources/19663

  4. [Illinois] MCB 493 Lecture 10: Time Series Learning and Nonlinear Signal Processing

    30 Oct 2013 | Online Presentations | Contributor(s): Thomas J. Anastasio

    http://nanohub.org/resources/19662

  5. [Illinois] MCB 493 Lecture 1: Vectors, Matrices, and Basic Neural Computations

    30 Oct 2013 | Online Presentations | Contributor(s): Thomas J. Anastasio

    Using mathematical and computational methods to simulate many aspects of neural systems function.

    http://nanohub.org/resources/16716

  6. [Illinois] MCB 493 Lecture 3: Forward and Recurrent Lateral Inhibition

    30 Oct 2013 | Online Presentations | Contributor(s): Thomas J. Anastasio

    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.

    http://nanohub.org/resources/16718

  7. [Illinois] MCB 493 Lecture 6: Supervised Learning and Non-Uniform Representations

    30 Oct 2013 | Online Presentations | Contributor(s): Thomas J. Anastasio

    Supervised learning algorithms can train neural networks to associate patterns and simulate the non-uniform distributed representations found in many brain regions.

    http://nanohub.org/resources/17022

  8. [Illinois] MCB 493 Lecture 7: Reinforcement Learning and Associative Conditioning

    30 Oct 2013 | Online Presentations | Contributor(s): Thomas J. Anastasio

    Reinforcement learning algorithms can simulate certain types of associative conditioning and train neural networks to form non-uniform distributed representations.

    http://nanohub.org/resources/18832

  9. [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.

    http://nanohub.org/resources/16717

  10. [Illinois] MCB 493 Lecture 4: Covariation Learning and Auto-Associative Memory

    29 Oct 2013 | Online Presentations | Contributor(s): Thomas J. Anastasio

    Networks with recurrent connection weights that reflect the covariation between pattern elements can dynamically recall patterns and simulate certain forms of memory.

    http://nanohub.org/resources/16950

  11. [Illinois] MCB 493 Lecture 5: Unsupervised Learning and Distributed Representations

    29 Oct 2013 | Online Presentations | Contributor(s): Thomas J. Anastasio

    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.

    http://nanohub.org/resources/16951

  12. [Illinois] MCB 493 Lecture 8: Information Transmission and Unsupervised Learning

    29 Oct 2013 | Online Presentations | Contributor(s): Thomas J. Anastasio

    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.

    http://nanohub.org/resources/18833

  13. [Illinois] Phys550 Lecture 14: Physics of the Neuron II

    29 Oct 2013 | Online Presentations | Contributor(s): Klaus Schulten

    http://nanohub.org/resources/19654

  14. [Illinois] Phys550 Lecture 15: Physics of the Neuron III

    29 Oct 2013 | Online Presentations | Contributor(s): Klaus Schulten

    http://nanohub.org/resources/19655

  15. [Illinois] Phys550 Lecture 12: Stochastic Processes III

    07 Oct 2013 | Online Presentations | Contributor(s): Klaus Schulten

    http://nanohub.org/resources/19554

  16. [Illinois] Phys550 Lecture 11: Stochastic Processes II

    07 Oct 2013 | Online Presentations | Contributor(s): Klaus Schulten

    http://nanohub.org/resources/19553

  17. [Illinois] Phys550 Lecture 8: Vision II

    02 Oct 2013 | Online Presentations | Contributor(s): Klaus Schulten

    http://nanohub.org/resources/19511

  18. [Illinois] Phys550 Lecture 10: Stochastic Processes I

    02 Oct 2013 | Online Presentations | Contributor(s): Klaus Schulten

    http://nanohub.org/resources/19514

  19. [Illinois] Phys550 Lecture 9: Protein Overview

    02 Oct 2013 | Online Presentations | Contributor(s): Klaus Schulten

    http://nanohub.org/resources/19516

  20. [Illinois] Phys550 Lecture 7: Vision I

    02 Oct 2013 | Online Presentations | Contributor(s): Klaus Schulten

    http://nanohub.org/resources/19510

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