Tags: nano/bio

Tools (41-60 of 83)

  1. [Illinois]: Running Average

    24 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

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

    09 Jul 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

  3. [Illinois]: Big Mess

    02 Jul 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

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

    02 Jul 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

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

    01 Jul 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

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

    01 Jul 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

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

    01 Jul 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

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

    01 Jul 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

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

    01 Jul 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

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

    01 Jul 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

  11. [Illinois]: Avoidance Learn Simulation

    01 Jul 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

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

    27 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

  13. Hydrodynamic Particle Trapping

    26 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

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

    26 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

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

    26 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

  16. [Illinois]: Velocity storage and leakage

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

    Implements the parallel-pathway and positive-feedback models of velocity storage, and the negative-feedback model of velocity leakage

    http://nanohub.org/resources/velstoreleak

  17. [Illinois]: Kohonen self-organizing map (SOM) algorithm

    21 Jun 2013 | Tools | Contributor(s): Bara Saadah, John Feser, NanoBio Node, Jessica S Johnson

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

    http://nanohub.org/resources/tonotopicsom

  18. Locust-flight central pattern generator

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

    Implements a linear version of Wilson's model of the locust-flight central pattern generator

    http://nanohub.org/resources/wilsoncpg

  19. Two-unit oculomotor integrator

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

    Implements the two-unit model of the integrator of the oculomotor system

    http://nanohub.org/resources/twounitintegrat

  20. [Illinois]: Two leaky integrators in series

    20 Jun 2013 | Tools | Contributor(s): Lisa L Sproat, John Feser, Jessica S Johnson, NanoBio Node

    Implements a model having two units (leaky integrators) in series, each with recurrent, excitatory self-connections allowing the units to exert positive feedback on themselves

    http://nanohub.org/resources/twoleakseries