All Categories (21-40 of 48)

1. [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

2. [Illinois] MCB 493 Lecture 1: Vectors, Matrices, and Basic Neural Computations

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

   Using mathematical and computational methods to simulate many aspects of neural systems function.

3. [Illinois] MCB 493 Lecture 3: Forward and Recurrent Lateral Inhibition

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

   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.

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. [Illinois] MCB 493 Neural Systems Modeling

    29 Oct 2013 | | 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...

13. [Illinois]: Posterior target probability given single-sensory input (delta rule)

    02 Jul 2013 | | Contributor(s):: Lisa Sproat, Jessica S Johnson, NanoBio Node

    Trains a single sigmoidal unit using the delta rule to estimate posterior target probability given sensory input of one modality (i.e., visual)

14. [Illinois]: Posterior probability of a target given single-sensory input (Bayes')

    28 Jun 2013 | | Contributor(s):: Lisa Sproat, Jessica S Johnson, NanoBio Node

    Computes the posterior probability of a target given sensory input of one modality (i.e., visual)

15. [Illinois]: Posterior probability of a target given input for two senses (Bayes')

    28 Jun 2013 | | Contributor(s):: Lisa Sproat, Jessica S Johnson, NanoBio Node

    Computes the posterior probability of a target given sensory input of two modalities (i.e., visual and auditory)

16. [Illinois]: Posterior probability of a target given input for two senses (delta)

    28 Jun 2013 | | Contributor(s):: Lisa Sproat, Jessica S Johnson, NanoBio Node

    Trains a single sigmoidal unit using the delta rule to estimate posterior target probability given sensory input of two modalities (i.e., visual and auditory)

17. [Illinois]: Posterior probabilities of hypothetical fish classes

    28 Jun 2013 | | Contributor(s):: Lisa Sproat, Jessica S Johnson, NanoBio Node

    Computes the posterior probabilities of each of three hypothetical fish classes using Bayes' rule

18. [Illinois]: Fish classification using back-propagation

    28 Jun 2013 | | Contributor(s):: Lisa Sproat, Jessica S Johnson, NanoBio Node

    Trains a three-layered network of sigmoidal units using back-propagation to classify fish according to their lengths

19. [Illinois]: Sigmoidal unit training with the delta rule

    26 Jun 2013 | | Contributor(s):: Lisa Sproat, NanoBio Node, Jessica S Johnson

    Uses the delta rule to train a single sigmoidal unit with feedback to simulate the responses of neurons in the parabigeminal nucleus

20. [Illinois]: Predictor-corrector simulation of parabigeminal nucleus neural responses

    24 Jun 2013 | | Contributor(s):: Lisa Sproat, NanoBio Node, Jessica S Johnson

    Implements a predictor-corrector simulation of the responses of neurons in the parabigeminal nucleus