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Tags: nano/bio

Tools (21-40 of 68)

  1. [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

  2. [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

  3. [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

  4. [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

  5. [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

  6. [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

  7. [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

  8. [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

  9. [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

  10. [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

  11. [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

  12. [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

  13. [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

  14. [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

  15. [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

  16. [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

  17. [Illinois]: Sigmoidal unit training with the delta rule

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

    Uses the delta rule to train a single sigmoidal unit with feedback to simulate the responses of neurons in the parabigeminal nucleus

    http://nanohub.org/resources/pbndeltarule

  18. Hydrodynamic Particle Trapping

    14 Jun 2013 | Tools | Contributor(s): Melikhan tanyerim@illinois.edu Tanyeri, John Feser, Nahil Sobh

    Simulates the motion of a nanoparticle in a hydrodynamic trap.

    http://nanohub.org/resources/particletrap

  19. [Illinois]: Predictor-corrector simulation of parabigeminal nucleus neural responses

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

    Implements a predictor-corrector simulation of the responses of neurons in the parabigeminal nucleus

    http://nanohub.org/resources/pbnpredict

  20. [Illinois]: Midbrain dopamine neuron responses to temporal-difference learning

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

    Simulates the responses of midbrain dopamine neurons using temporal difference learning

    http://nanohub.org/resources/midbraindopamin

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.