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In this page, we introduce our unsupervised online incremental learning method, Self-Organizing Incremental Neural Network (SOINN). We will release the 2nd-Generation SOINN, which is designed based on the Bayes Theory. What is SOINN? The SOINN is an unsupervised online-learning method, which is capable of incremental learning, based on Growing Neural Gas (GNG) and Self-Organizing Map (SOM).
Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent PlasticityResults In this section we define a simple model circuit and show that every spiking event of the circuit can be described as one independent sample of a discrete probability distribution, which itself evolves over time in response to the spiking input. Within this network we analyze a variant of a STDP rule, in which the strength of potentiation depends on the current weight value.
At its core, RoboEarth is a World Wide Web for robots: a giant network and database repository where robots can share information and learn from each other about their behavior and their environment.
If you’ve been paying attention, you’ll notice there has been a lot of news recently about neural networks and the brain .
Subscriber Reviews Average Rating: Based on 8 Ratings "Useful book on machine learning, etc." - by Alex Ott on 20-JUL-2011 Reviewer Rating:
Fast Artificial Neural Network Library is a free open source neural network library, which implements multilayer artificial neural networks in C with support for both fully connected and sparsely connected networks. Cross-platform execution in both fixed and floating point are supported. It includes a framework for easy handling of training data sets. It is easy to use, versatile, well documented, and fast. Bindings to more than 15 programming languages are available. An easy to read introduction article and a reference manual accompanies the library with examples and recommendations on how to use the library.
Temporal difference (TD) learning is an approach to learning how to predict a quantity that depends on future values of a given signal. The name TD derives from its use of changes, or differences, in predictions over successive time steps to drive the learning process. The prediction at any given time step is updated to bring it closer to the prediction of the same quantity at the next time step. It is a supervised learning process in which the training signal for a prediction is a future prediction.
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