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.