Tags: nano/bio

Tools (41-60 of 83)

  1. [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.

  2. [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.

  3. [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.

  4. [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)

  5. [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)

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

    28 Jun 2013 | | 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)

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

    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 two modalities (i.e., visual and auditory)

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

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

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

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

    28 Jun 2013 | | 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

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

    25 Jun 2013 | | 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.

  11. [Illinois]: Avoidance Learn Simulation

    20 Jun 2013 | | 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).

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

    26 Jun 2013 | | 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

  13. Hydrodynamic Particle Trapping

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

    Simulates the motion of a nanoparticle in a hydrodynamic trap.

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

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

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

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

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

    Simulates the responses of midbrain dopamine neurons using temporal difference learning

  16. [Illinois]: Velocity storage and leakage

    04 Jun 2013 | | 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

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

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

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

  18. Locust-flight central pattern generator

    06 Jun 2013 | | 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

  19. Two-unit oculomotor integrator

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

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

  20. [Illinois]: Two leaky integrators in series

    31 May 2013 | | 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