Support

Support Options

Submit a Support Ticket

 

Tags: nano/bio

Tools (21-40 of 72)

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

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

    This tool updates state values using the Temporal Difference Algorithm.

    http://nanohub.org/resources/tempdiff

  2. Crystalline Cellulose - Atomistic Toolkit

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

    Crystalline Cellulose - Atomistic Toolkit

    http://nanohub.org/resources/ccamt

  3. [Illinois]: Direction Selectivity

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

    This tool implements a simple direction selective network.

    http://nanohub.org/resources/dirselectivity

  4. [Illinois]: Predict Correct Set Up

    15 Jul 2013 | Tools | 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

    http://nanohub.org/resources/predcorsetup

  5. [Illiniois]: SynchUp

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

    This tool computes synchronous updates of autoassociative networks.

    http://nanohub.org/resources/synchup

  6. [Illinois]: AsynchUp

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

    This tool computes asynchronous updates of autoassociative networks.

    http://nanohub.org/resources/asynchup

  7. [Illinois] KohonenSOM

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

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

    http://nanohub.org/resources/kohonensom

  8. [Illinois]: BUTDprobInference

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

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

    http://nanohub.org/resources/butdprobinf

  9. [Illinois]: BUTDjointDistribution

    16 Jul 2013 | Tools | 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

    http://nanohub.org/resources/butdjdist

  10. [Illinois]: Running Average

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

    This tool implements a running average of a noise series.

    http://nanohub.org/resources/runningaverage

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

    29 Jun 2013 | Tools | 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.

    http://nanohub.org/resources/pertgrad1by1

  12. [Illinois]: Big Mess

    26 Jun 2013 | Tools | 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.

    http://nanohub.org/resources/bigmess

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

    28 Jun 2013 | Tools | 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)

    http://nanohub.org/resources/unisensorydelta

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

    28 Jun 2013 | Tools | 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)

    http://nanohub.org/resources/unisensorybayes

  15. [Illinois]: Posterior probability of a target given input for two senses (Bayes')

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

    Computes the posterior probability of a target given sensory input of two modalities (i.e., visual and auditory)

    http://nanohub.org/resources/bisensorybayes

  16. [Illinois]: Posterior probability of a target given input for two senses (delta)

    28 Jun 2013 | Tools | 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 two modalities (i.e., visual and auditory)

    http://nanohub.org/resources/bisensorydelta

  17. [Illinois]: Posterior probabilities of hypothetical fish classes

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

    Computes the posterior probabilities of each of three hypothetical fish classes using Bayes' rule

    http://nanohub.org/resources/fishbayesrule

  18. [Illinois]: Fish classification using back-propagation

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

    Trains a three-layered network of sigmoidal units using back-propagation to classify fish according to their lengths

    http://nanohub.org/resources/fishbackprop

  19. [Illinois]: Avoidance Learn Simulation with 'Call' Neuron

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

    This script simulates avoidance learning as a reinforcement learning with two upper motoneurons (sumo and fumo) and one "call" neuron.

    http://nanohub.org/resources/avoidlearncall

  20. [Illinois]: Avoidance Learn Simulation

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

    This script simulates avoidance conditioning as reinforcement learning with two upper motoneurons (SUMO and FUMO).

    http://nanohub.org/resources/avoidlearn

nanoHUB.org, a resource for nanoscience and nanotechnology, is supported by the National Science Foundation and other funding agencies. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.