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[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...