Resources (1-20 of 34)

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 10: Time Series Learning and Nonlinear Signal Processing

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

5. [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.

6. [Illinois] MCB 493 Lecture 13: Predictor-Corrector Models and Probabilistic Inference

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

7. [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

8. [Illinois] MCB 493 Lecture 1: Vectors, Matrices, and Basic Neural Computations

   03 Feb 2013 | | Contributor(s):: Thomas J. Anastasio

   Using mathematical and computational methods to simulate many aspects of neural systems function.

9. [Illinois] MCB 493 Lecture 2: Recurrent Connections and Simple Neural Circuits

   03 Feb 2013 | | Contributor(s):: Thomas J. Anastasio

   Networks with recurrent connections, forming circuits, and containing only a few neural units can shape signals in time, produce oscillations, and simulate certain forms of low-level motor control.

10. [Illinois] MCB 493 Lecture 3: Forward and Recurrent Lateral Inhibition

    03 Feb 2013 | | Contributor(s):: Thomas J. Anastasio

    Networks with forward and recurrent laterally inhibitory connectivity profiles can shape signals in space and time, and simulate certain forms of sensory and motor processing.

11. [Illinois] MCB 493 Lecture 4: Covariation Learning and Auto-Associative Memory

    21 Feb 2013 | | Contributor(s):: Thomas J. Anastasio

    Networks with recurrent connection weights that reflect the covariation between pattern elements can dynamically recall patterns and simulate certain forms of memory.

12. [Illinois] MCB 493 Lecture 5: Unsupervised Learning and Distributed Representations

    21 Feb 2013 | | Contributor(s):: Thomas J. Anastasio

    Unsupervised learning algorithms, given only a set of input patterns, can train neural networks to form distributed representations of those patterns that resemble brain maps.

13. [Illinois] MCB 493 Lecture 6: Supervised Learning and Non-Uniform Representations

    25 Feb 2013 | | Contributor(s):: Thomas J. Anastasio

    Supervised learning algorithms can train neural networks to associate patterns and simulate the non-uniform distributed representations found in many brain regions.

14. [Illinois] MCB 493 Lecture 7: Reinforcement Learning and Associative Conditioning

    03 Jul 2013 | | Contributor(s):: Thomas J. Anastasio

    Reinforcement learning algorithms can simulate certain types of associative conditioning and train neural networks to form non-uniform distributed representations.

15. [Illinois] MCB 493 Lecture 8: Information Transmission and Unsupervised Learning

    03 Jul 2013 | | Contributor(s):: Thomas J. Anastasio

    Unsupervised learning algorithms can train neural networks to increase the amount of information they contain about their inputs and simulate the properties of sensory neurons.

16. [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.

17. [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...

18. [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.

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

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

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

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