Support

Support Options

Submit a Support Ticket

 

[Illinois] MCB 493 Neural Systems Modeling

By Thomas J. Anastasio

Department of Molecular and Integrative Physiology , University of Illinois at Urbana-Champaign, Urbana, IL

See also

Lecture Number/Topic Online Lecture Video Lecture Notes Supplemental Material Suggested Exercises
[Illinois] MCB 493 Lecture 1: Vectors, Matrices, and Basic Neural Computations View HTML
View Simulation Tool
Using mathematical and computational methods to simulate many aspects of neural systems function.

[Illinois] MCB 493 Lecture 2: Recurrent Connections and Simple Neural Circuits View HTML
View Simulation Tool
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.

[Illinois] MCB 493 Lecture 3: Forward and Recurrent Lateral Inhibition View HTML
View Simulation Tool
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.

[Illinois] MCB 493 Lecture 4: Covariation Learning and Auto-Associative Memory View HTML
View Simulation Tool
Networks with recurrent connection weights that reflect the covariation between pattern elements can dynamically recall patterns and simulate certain forms of memory.

[Illinois] MCB 493 Lecture 5: Unsupervised Learning and Distributed Representations View HTML
View Simulation Tool
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.

[Illinois] MCB 493 Lecture 6: Supervised Learning and Non-Uniform Representations View HTML
View Simulation Tool
Supervised learning algorithms can train neural networks to associate patterns and simulate the non-uniform distributed representations found in many brain regions.

[Illinois] MCB 493 Lecture 7: Reinforcement Learning and Associative Conditioning View HTML
View Simulation Tool
Reinforcement learning algorithms can simulate certain types of associative conditioning and train neural networks to form non-uniform distributed representations.

[Illinois] MCB 493 Lecture 8: Information Transmission and Unsupervised Learning View HTML
View Simulation Tool
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.

[Illinois] MCB 493 Lecture 9: Probability Estimation and Supervised Learning View HTML
View Simulation Tool
Supervised learning algorithms can train neural units and networks to estimate probabilities and simulate the responses of neurons to multisensory stimulation.

[Illinois] MCB 493 Lecture 10: Time Series Learning and Nonlinear Signal Processing View HTML
View Simulation Tool
[Illinois] MCB 493 Lecture 11: Temporal-Difference Learning and Reward Prediction View HTML
View Simulation Tool
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.

[Illinois] MCB 493 Lecture 13: Predictor-Corrector Models and Probabilistic Inference View HTML
View Simulation Tool
[Illinois] MCB 493 Lecture 14: Future Directions in Neural Systems Modeling View HTML
View
In the future, neural systems models will become increasingly complex and will span levels from molecular interactions within neurons to interactions between networks

nanoHUB.org, a resource for nanoscience and nanotechnology, is supported by the National Science Foundation and other funding agencies. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.