Neural Networks

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OPENNN. HasegawaLab. RESEARCH | Hasegawa Lab., Imaging Science and Engineering Laboratory. Tokyo Institute of Technology. In this page, we introduce our unsupervised online incremental learning method, Self-Organizing Incremental Neural Network (SOINN).

RESEARCH | Hasegawa Lab., Imaging Science and Engineering Laboratory. Tokyo Institute of Technology

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 Plasticity. Results 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.

Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity

Within this network we analyze a variant of a STDP rule, in which the strength of potentiation depends on the current weight value. What is RoboEarth ? Resurgence in Neural Networks - tjake.blog. If you’ve been paying attention, you’ll notice there has been a lot of news recently about neural networks and the brain.

Resurgence in Neural Networks - tjake.blog

A few years ago the idea of virtual brains seemed so far from reality, especially for me, but in the past few years there has been a breakthrough that has turned neural networks from nifty little toys to actual useful things that keep getting better at doing tasks computers are traditionally very bad at. In this post I’ll cover some background on Neural networks and my experience with them. Then go over the recent discoveries I’ve learned about.

At the end of the post I’ll share a sweet little github project I wrote that implements this new neural network approach. Towards the evolution of an artificial homeostatic system. This paper presents an artificial homeostatic system (AHS) devoted to the autonomous navigation of mobile robots, with emphasis on neuro-endocrine interactions.

Towards the evolution of an artificial homeostatic system

The AHS is composed of two modules, each one associated with a particular reactive task and both implemented using an extended version of the GasNet neural model, denoted spatially unconstrained GasNet model or simply non-spatial GasNet (NSGasNet). There is a coordination system, which is responsible for the specific role of each NSGasNet at a given operational condition. PyBrain. Programming Collective Intelligence. Subscriber Reviews Average Rating: Based on 8 Ratings "Useful book on machine learning, etc.

Programming Collective Intelligence

" - by Alex Ott on 20-JUL-2011Reviewer Rating: Fast Artificial Neural Network Library. Temporal difference learning. Temporal difference (TD) learning is an approach to learning how to predict a quantity that depends on future values of a given signal.

Temporal difference learning

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. Heuristic search project.