Tags: neuroscience

All Categories (1-20 of 29)

  1. Janae

    https://nanohub.org/members/134905

  2. Nikhil Khinchi

    https://nanohub.org/members/127261

  3. Sep 18 2014

    8th Neurodegenerative Conditions Research & Development Conference

    It is our pleasure to announce the 8th Neurodegenerative Conditions Research & Development Conference, to be held on September 18-19, 2014 in San Francisco, CA to discuss the latest scientific...

    https://nanohub.org/events/details/994

  4. AJAY RAJAK

    https://nanohub.org/members/94009

  5. [Illinois] MCB 529 BRH Reproductive Rhythms

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

    https://nanohub.org/resources/20121

  6. [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...

    https://nanohub.org/resources/20114

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

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

    https://nanohub.org/resources/20118

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

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

    https://nanohub.org/resources/20119

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

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

    https://nanohub.org/resources/20117

  10. [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

    https://nanohub.org/resources/18948

  11. [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.

    https://nanohub.org/resources/18834

  12. [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.

    https://nanohub.org/resources/18947

  13. [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....

    https://nanohub.org/resources/16704

  14. [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)

    https://nanohub.org/resources/unisensorydelta

  15. [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)

    https://nanohub.org/resources/unisensorybayes

  16. [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)

    https://nanohub.org/resources/bisensorybayes

  17. [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)

    https://nanohub.org/resources/bisensorydelta

  18. [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

    https://nanohub.org/resources/fishbayesrule

  19. [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

    https://nanohub.org/resources/fishbackprop

  20. [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

    https://nanohub.org/resources/pbndeltarule