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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

[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

[Illinois]: Neural Systems Modeling Ch 1-5 Master Tool

24 Jul 2013 | Tools | Contributor(s): Jessica S Johnson, Nahil Sobh, NanoBio Node

Combination of all tools used in Chapters 1-5 of Neural Systems Modeling by Anastasio

[Illinois]: BUTDprobInference

17 Jul 2013 | Tools | Contributor(s): Bara Saadah, Jessica S Johnson, NanoBio Node

This tool stimulates bottom-up/top-down processing in the visual system using probabilistic inference.

[Illinois]: BUTDjointDistribution

16 Jul 2013 | Tools | Contributor(s): Bara Saadah, Jessica S Johnson, NanoBio Node

this tool simulates bottom-up/top-down processing in the visual system using the joint distribution

[Illinois]: AsynchUp

16 Jul 2013 | Tools | Contributor(s): Bara Saadah, Nahil Sobh, AbderRahman N Sobh, NanoBio Node, Jessica S Johnson

This tool computes asynchronous updates of autoassociative networks.

[Illiniois]: SynchUp

16 Jul 2013 | Tools | Contributor(s): Bara Saadah, Nahil Sobh, AbderRahman N Sobh, Jessica S Johnson, NanoBio Node

This tool computes synchronous updates of autoassociative networks.

[Illinois]: Predict Correct Set Up

15 Jul 2013 | Tools | Contributor(s): Bara Saadah, Nahil Sobh, AbderRahman N Sobh, Jessica S Johnson, NanoBio Node

This tool sets up a predictor-corrector model of target tracking

[Illinois]: Direction Selectivity

13 Jul 2013 | Tools | Contributor(s): Bara Saadah, Nahil Sobh, AbderRahman N Sobh, Jessica S Johnson, NanoBio Node

This tool implements a simple direction selective network.

[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.

[Illinois]: Perturbative Reinforcement Learning Using Directed Drift

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

[Illinois]: Running Average

10 Jul 2013 | Tools | Contributor(s): Bara Saadah, Nahil Sobh, Jessica S Johnson, NanoBio Node

This tool implements a running average of a noise series.

[Illinois] KohonenSOM

09 Jul 2013 | Tools | Contributor(s): Bara Saadah, Nahil Sobh, Jessica S Johnson, NanoBio Node

This tool implements the Kohonen self-organizing map (SOM) algorithm

[Illinois] A Response of a Single Neuron with Positive Feedback

This tool stimulates the pulse or step response of a single neuron with positive feedback

[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.

[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.

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

28 Jun 2013 | Tools | 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)

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

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

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

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

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

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

[Illinois]: Posterior probabilities of hypothetical fish classes

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

[Illinois]: Fish classification using back-propagation

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

[Illinois]: Sigmoidal unit training with the delta rule

26 Jun 2013 | Tools | 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

[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.

[Illinois]: 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.