Find information on common issues.
Ask questions and find answers from other users.
Suggest a new site feature or improvement.
Check on status of your tickets.
[Illinois] MCB 493 Lecture 13: Predictor-Corrector Models and Probabilistic Inference
30 Oct 2013 | Online Presentations | Contributor(s): Thomas J. Anastasio
[Illinois] MCB 493 Lecture 10: Time Series Learning and Nonlinear Signal Processing
[Illinois] MCB 493 Lecture 14: Future Directions in Neural Systems Modeling
In the future, neural systems models will become increasingly complex and will span levels from molecular interactions within neurons to interactions between networks
[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.
[Illinois] MCB 493 Lecture 9: Probability Estimation and Supervised Learning
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 8: Information Transmission and Unsupervised Learning
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 7: Reinforcement Learning and Associative Conditioning
Reinforcement learning algorithms can simulate certain types of associative conditioning and train neural networks to form non-uniform distributed representations.
[Illinois] MCB 493 Lecture 6: Supervised Learning and Non-Uniform Representations
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 5: Unsupervised Learning and Distributed Representations
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 4: Covariation Learning and Auto-Associative Memory
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 3: Forward and Recurrent Lateral Inhibition
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 2: Recurrent Connections and Simple Neural Circuits
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 1: Vectors, Matrices, and Basic Neural Computations
Using mathematical and computational methods to simulate many aspects of neural systems function.
[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. Students will use existing computer programs which will simulate real neural systems. They will...