All Categories (1-20 of 20)

1. 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

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

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

3. [Illinois] MCB 493 Lecture 11: Temporal-Difference Learning and Reward Prediction

   18 Jul 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.

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

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

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

   18 Jul 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

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

   03 Feb 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.

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

   21 Feb 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.

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

   21 Feb 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.

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

   25 Feb 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.

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

    03 Jul 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.

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

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

    03 Jul 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.

13. [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).

14. [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.

15. [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.

16. [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.

17. [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

18. [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.

19. [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.

20. [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.