Tags: Neural Systems Modeling

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

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

  2. [Illinois] MCB 493 Lecture 10: Time Series Learning and Nonlinear Signal Processing

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

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

    30 Oct 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

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

    30 Oct 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.

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

    30 Oct 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.

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

    30 Oct 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 Lecture 11: Temporal-Difference Learning and Reward Prediction

    29 Oct 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.

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

    29 Oct 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.

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

    29 Oct 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.

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

    29 Oct 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.

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

    29 Oct 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.

  12. Neural Systems Modeling Ch10-13 Master Tool

    02 Aug 2013 | | Contributor(s):: Jessica S Johnson, NanoBio Node

    Combination of all tools used in Chapters 10-13 of Neural Systems Modeling by Anastasio

  13. [Illinois]: Neural Systems Modeling Ch 6-9 Master Tool

    26 Jul 2013 | | Contributor(s):: Jessica S Johnson, NanoBio Node

    Combination of all tools used in Chapters 6-9 of Neural Systems Modeling by Anastasio

  14. [Illinois]: Error Gradient Estimations Due to Parallel Perturbation of Weights

    07 Jul 2013 | | Contributor(s):: AbderRahman N Sobh, Jessica S Johnson, NanoBio Node

    This tool trains two-layered networks of sigmoidal units to associate patterns using simultaneous perturbation of weights.

  15. [Illinois]: Perturbative Reinforcement Learning to Develop Distributed Representations

    10 Jul 2013 | | Contributor(s):: AbderRahman N Sobh, Jessica S Johnson, NanoBio Node

    This tool trains three-layered networks of sigmoidal units to associate patterns.

  16. [Illinois]: Perturbative Reinforcement Learning Using Directed Drift

    10 Jul 2013 | | Contributor(s):: AbderRahman N Sobh, Jessica S Johnson, NanoBio Node

    This tool trains two-layered networks of sigmoidal units to associate patterns using a real-valued adaptation of the directed drift algorithm.

  17. [Illinois]: Error Gradient Estimations Due to Perturbation of One Weight at a Time

    29 Jun 2013 | | Contributor(s):: AbderRahman N Sobh, Jessica S Johnson, NanoBio Node

    This tool trains two-layered networks of sigmoidal units to associate patterns using perturbation of one weight at a time.

  18. [Illinois]: Avoidance Learn Simulation with 'Call' Neuron

    25 Jun 2013 | | Contributor(s):: AbderRahman N Sobh, NanoBio Node, Jessica S Johnson

    This script simulates avoidance learning as a reinforcement learning with two upper motoneurons (sumo and fumo) and one "call" neuron.

  19. [Illinois]: Avoidance Learn Simulation

    20 Jun 2013 | | Contributor(s):: AbderRahman N Sobh, NanoBio Node, Jessica S Johnson

    This script simulates avoidance conditioning as reinforcement learning with two upper motoneurons (SUMO and FUMO).

  20. [Illinois]: Optimize Connectivity Profile of Activity-Bubble Network

    25 Jun 2013 | | Contributor(s):: Jessica S Johnson, NanoBio Node

    Use genetic algorithm with binary chromosomes to optimize activity-bubble network.