Tags: neuroscience

Resources (1-20 of 24)

  1. 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

  2. Soft, Biocompatible Optoelectronic Interfaces to the Brain

    07 Jun 2017 | | Contributor(s):: John A. Rogers

    In this talk, we will describe foundational concepts in physics and materials science for these types of technologies, in 1D, 2D and 3D architectures. Examples in system level demonstrations include experiments on freely moving animals with ‘cellular-scale’, injectable optofluidic...

  3. 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

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

    18 Jul 2013 | | 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.

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

    18 Jul 2013 | | 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

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

    03 Jul 2013 | | 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.

  7. [Illinois] MCB 493 Neural Systems Modeling

    03 Feb 2013 | | 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. Students will use existing computer programs which will simulate real neural systems. They will...

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

    24 Dec 2013 | | 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 repair, and 3) emergent behaviors of integrated neuronal systems.

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

    24 Dec 2013 | | Contributor(s):: Shelley Tischkau, Martha U. Gillette

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

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

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

    24 Dec 2013 | | Contributor(s):: Rhanor Gillette, Martha U. Gillette

  12. [Illinois] MCB 529 BRH Reproductive Rhythms

    24 Dec 2013 | | Contributor(s):: Megan Mahoney, Martha U. Gillette

  13. [Illinois] MCB 529 BRH Why Do You Sleep At Night?

    01 Mar 2013 | | Contributor(s):: Martha U. Gillette

  14. [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

  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]: 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

  17. [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)

  18. [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)

  19. [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)

  20. [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)