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Tags: NanoBio Node

Resources (41-60 of 507)

  1. [Illinois] A Microfluidic Approach for Cocrystallization of Drugs and Analysis via X-ray Diffraction

    16 Dec 2013 | Online Presentations | Contributor(s): Elizabeth Horstman

    The process of pharmaceutical drug development is cost- and time-intensive. Candidate drugs (CDs) are screened with many counter ions (salt or cocrystal formers) to find solid forms of the drug...

    http://nanohub.org/resources/19500

  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 9: Probability Estimation and Supervised Learning

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

    Supervised learning algorithms can train neural units and networks to estimate probabilities and simulate the responses of neurons to multisensory stimulation.

    http://nanohub.org/resources/18834

  10. [Illinois] MCB 493 Lecture 14: Future Directions in Neural Systems Modeling

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

    In the future, neural systems models will become increasingly complex and will span levels from molecular interactions within neurons to interactions between networks

    http://nanohub.org/resources/18948

  11. [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

  12. [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

  13. [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

  14. [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

  15. [Illinois] MCB 493 Lecture 11: Temporal-Difference Learning and Reward Prediction

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

    Temporal-difference learning can train neural networks to estimate the future value of a current state and simulate the responses of neurons involved in reward processing.

    http://nanohub.org/resources/18947

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

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

    http://nanohub.org/resources/19654

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

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

    http://nanohub.org/resources/19655

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

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

    http://nanohub.org/resources/19554

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

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

    http://nanohub.org/resources/19553

  20. [Illinois] Nanopore Sequencing of DNA

    04 Oct 2013 | Online Presentations | Contributor(s): Aleksei Aksimentiev

    The idea of using a nanopore to sequence DNA continues to generate excitement among scientists and entrepreneurs. The spectacular progress in using biological enzymes to enable nanopore sequencing...

    http://nanohub.org/resources/19480

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