Tags: neuroscience

Tools (1-13 of 13)

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

    02 Jul 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)

  2. [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)

  3. [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)

  4. [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)

  5. [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

  6. [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

  7. [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

  8. [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

  9. [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

  10. [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

  11. 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

  12. 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

  13. [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