Tags: nano/bio

Resources (61-80 of 460)

  1. [Illinois]: Perturbative Reinforcement Learning to Develop Distributed Representations

    10 Jul 2013 | | Contributor(s):: AbderRahman N Sobh, Jessica S Johnson, NanoBio Node

    This tool trains three-layered networks of sigmoidal units to associate patterns.

  2. [Illinois]: Perturbative Reinforcement Learning Using Directed Drift

    10 Jul 2013 | | Contributor(s):: AbderRahman N Sobh, Jessica S Johnson, NanoBio Node

    This tool trains two-layered networks of sigmoidal units to associate patterns using a real-valued adaptation of the directed drift algorithm.

  3. [Illinois]: Temporal Difference, Iterative Dynamic Programming, and Least Mean Squares

    11 Jul 2013 | | Contributor(s):: Bara Saadah, Nahil Sobh, AbderRahman N Sobh, Jessica S Johnson

    This tool updates state values using the Temporal Difference Algorithm.

  4. Crystalline Cellulose - Atomistic Toolkit

    17 Jul 2013 | | Contributor(s):: Mateo Gómez Zuluaga, Robert J. Moon, Fernando Luis Dri, Pablo Daniel Zavattieri

    Crystalline Cellulose - Atomistic Toolkit

  5. [Illinois] GEM4 Bionanotechnology Summer Institute 2013

    30 Jul 2013 |

    INSTITUTE FOCUS:Cancer Nanotechnology and Cellular MechanicsOBJECTIVESThe overall objective is to enhance the ability to address overarching challenges in the areas of Cancer Nanotechnology and Mechanobiology. Highly interdisciplinary in nature, the 2013 Summer Institute will offer faculty...

  6. [Illinois]: Direction Selectivity

    13 Jul 2013 | | Contributor(s):: Bara Saadah, Nahil Sobh, AbderRahman N Sobh, Jessica S Johnson, NanoBio Node

    This tool implements a simple direction selective network.

  7. [Illinois]: Predict Correct Set Up

    15 Jul 2013 | | Contributor(s):: Bara Saadah, Nahil Sobh, AbderRahman N Sobh, Jessica S Johnson, NanoBio Node

    This tool sets up a predictor-corrector model of target tracking

  8. [Illiniois]: SynchUp

    16 Jul 2013 | | Contributor(s):: Bara Saadah, Nahil Sobh, AbderRahman N Sobh, Jessica S Johnson, NanoBio Node

    This tool computes synchronous updates of autoassociative networks.

  9. [Illinois]: AsynchUp

    16 Jul 2013 | | Contributor(s):: Bara Saadah, Nahil Sobh, AbderRahman N Sobh, NanoBio Node, Jessica S Johnson

    This tool computes asynchronous updates of autoassociative networks.

  10. [Illinois] KohonenSOM

    09 Jul 2013 | | Contributor(s):: Bara Saadah, Nahil Sobh, Jessica S Johnson, NanoBio Node

    This tool implements the Kohonen self-organizing map (SOM) algorithm

  11. [Illinois]: BUTDprobInference

    17 Jul 2013 | | Contributor(s):: Bara Saadah, Jessica S Johnson, NanoBio Node

    This tool stimulates bottom-up/top-down processing in the visual system using probabilistic inference.

  12. [Illinois]: BUTDjointDistribution

    16 Jul 2013 | | Contributor(s):: Bara Saadah, Jessica S Johnson, NanoBio Node

    this tool simulates bottom-up/top-down processing in the visual system using the joint distribution

  13. [Illinois]: Running Average

    10 Jul 2013 | | Contributor(s):: Bara Saadah, Nahil Sobh, Jessica S Johnson, NanoBio Node

    This tool implements a running average of a noise series.

  14. [illinois] BioMEMS and Bionanotechnology: Integration of Life Sciences and Engineering at the Micro and Nanoscale

    10 Jul 2013 | | Contributor(s):: Rashid Bashir

  15. [Illinois]: Error Gradient Estimations Due to Perturbation of One Weight at a Time

    29 Jun 2013 | | Contributor(s):: AbderRahman N Sobh, Jessica S Johnson, NanoBio Node

    This tool trains two-layered networks of sigmoidal units to associate patterns using perturbation of one weight at a time.

  16. MAE 6291 Lecture 07: FET, WGM and GMR as Signal Transducers

    06 Jul 2013 | | Contributor(s):: jonathan silver

  17. MAE 6291 Lecture 06: Single-Molecule Fluorescence, TIRF, FRET

    06 Jul 2013 | | Contributor(s):: jonathan silver

  18. [Illinois]: Big Mess

    26 Jun 2013 | | Contributor(s):: Bara Saadah

    This tool stimulates the pulse or step response of a neural network with ten input and ten output units that are randomly connected.

  19. [Illinois]: Posterior target probability given single-sensory input (delta rule)

    28 Jun 2013 | | Contributor(s):: Lisa Sproat, Jessica S Johnson, NanoBio Node

    Trains a single sigmoidal unit using the delta rule to estimate posterior target probability given sensory input of one modality (i.e., visual)

  20. [Illinois]: Posterior probability of a target given single-sensory input (Bayes')

    28 Jun 2013 | | Contributor(s):: Lisa Sproat, Jessica S Johnson, NanoBio Node

    Computes the posterior probability of a target given sensory input of one modality (i.e., visual)