Tags: neuroscience

Resources (1-20 of 23)

  1. [Illinois] MCB 529 BRH Reproductive Rhythms

    07 Jan 2014 | Online Presentations | Contributor(s): Megan Mahoney, Martha U. Gillette

    http://nanohub.org/resources/20121

  2. [Illinois] MCB 529 BRH Biological Rhythms in Health and Disease

    30 Dec 2013 | Courses | Contributor(s): Martha U. Gillette

    Our major research thrusts are to understand: 1) signals that engage the circadian clockwork in the brain, 2) sub-cellular micro-environments that shape neuronal dendrites in development and...

    http://nanohub.org/resources/20114

  3. [Illinois] MCB 529 BRH Nocturnal and Diurnal Adaptations

    30 Dec 2013 | Online Presentations | Contributor(s): Rhanor Gillette, Martha U. Gillette

    http://nanohub.org/resources/20117

  4. [Illinois] MCB 529 BRH Circadian Control of Liver Function

    30 Dec 2013 | Online Presentations | Contributor(s): Shelley Tischkau, Martha U. Gillette

    http://nanohub.org/resources/20118

  5. [Illinois] MCB 529 BRH Drugs of Abuse and Circadian Rhythms

    30 Dec 2013 | Online Presentations | Contributor(s): Joshua M Gulley, Martha U. Gillette

    http://nanohub.org/resources/20119

  6. [Illinois] MCB 493 Lecture 9: Probability Estimation and Supervised Learning

    30 Oct 2013 | Online Presentations | Contributor(s): Thomas J. Anastasio

    Supervised learning algorithms can train neural units and networks to estimate probabilities and simulate the responses of neurons to multisensory stimulation.

    http://nanohub.org/resources/18834

  7. [Illinois] MCB 493 Lecture 14: Future Directions in Neural Systems Modeling

    30 Oct 2013 | Online Presentations | Contributor(s): Thomas J. Anastasio

    In the future, neural systems models will become increasingly complex and will span levels from molecular interactions within neurons to interactions between networks

    http://nanohub.org/resources/18948

  8. [Illinois] MCB 493 Neural Systems Modeling

    29 Oct 2013 | Courses | Contributor(s): Thomas J. Anastasio

    The purpose of this independent study is to give students hands-on experience in using computers to model neural systems. A neural system is a system of interconnected neural elements, or units....

    http://nanohub.org/resources/16704

  9. [Illinois] MCB 493 Lecture 11: Temporal-Difference Learning and Reward Prediction

    29 Oct 2013 | Online Presentations | Contributor(s): Thomas J. Anastasio

    Temporal-difference learning can train neural networks to estimate the future value of a current state and simulate the responses of neurons involved in reward processing.

    http://nanohub.org/resources/18947

  10. [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

  11. [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

  12. [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

  13. [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

  14. [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

  15. [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

  16. [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

  17. [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

  18. [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

  19. [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

  20. 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