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Tags: Neural Systems Modeling

All Categories (1-20 of 28)

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

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

    http://nanohub.org/resources/19663

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

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

    http://nanohub.org/resources/19662

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

    30 Oct 2013 | Online Presentations | 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

    http://nanohub.org/resources/18948

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

    30 Oct 2013 | Online Presentations | 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.

    http://nanohub.org/resources/18834

  5. [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.

    http://nanohub.org/resources/18947

  6. Neural Systems Modeling Ch10-13 Master Tool

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

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

    http://nanohub.org/resources/nsmch10master

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

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

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

    http://nanohub.org/resources/nsmch6master

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

    07 Jul 2013 | Tools | 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.

    http://nanohub.org/resources/pertgradll

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

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

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

    http://nanohub.org/resources/pertdistrep

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

    10 Jul 2013 | Tools | 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.

    http://nanohub.org/resources/pertdd

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

    29 Jun 2013 | Tools | 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.

    http://nanohub.org/resources/pertgrad1by1

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

    25 Jun 2013 | Tools | 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.

    http://nanohub.org/resources/avoidlearncall

  13. [Illinois]: Avoidance Learn Simulation

    20 Jun 2013 | Tools | 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).

    http://nanohub.org/resources/avoidlearn

  14. Optimize Connectivity Profile of Activity-Bubble Network

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

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

    http://nanohub.org/resources/gabubble

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