Artificial neural networks
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Spaun | Repository of Neural and Cognitive Models
Description: Spaun is a biologically realistic model of cognition that is not only able to perform multiple (at least 10) cognitive, perceptual, and motor tasks, but also utilizes the same model parameters across all tasks. Spaun is able to perform tasks that encompass strictly visual tasks (e.g. recognition of handwritten digits), memory tasks (e.g. forward and backward recall of a list), simple cognitive tasks (e.g. counting), and complex fluid intelligence tasks (e.g. solving the Raven's Progressive Matrices).AN ARTIFICIAL brain has taught itself to estimate the number of objects in an image without actually counting them, emulating abilities displayed by some animals including lions and fish , as well as humans. Because the model was not preprogrammed with numerical capabilities, the feat suggests that this skill emerges due to general learning processes rather than number-specific mechanisms.
Neural network gets an idea of number without counting - tech - 20 January 2012
http://graphics.cs.cmu.edu/projects/crossDomainMatching/
Data-driven Visual Similarity for Cross-domain Image Matching
Intro & basic
Tech papers
Neuroscience
Information transmission with spiking Bayesian neurons
New J. Phys. 10 (2008) 055019 doi:10.1088/1367-2630/10/5/055019 Information transmission with spiking Bayesian neurons
Simulators and tools
Hopfield Neural Network Example
Get the entire book! Now that you have been shown some of the basic concepts of neural network we will example an actual Java example of a neural network. The example program for this chapter implements a simple Hopfield neural network that you can used to experiment with Hopfield neural networks.
Associations - While individual items and contexts are represented as single vectors ( a , b , x ), associations between items and contexts are represented by matrices derived from the matrix product of these vectors. The resulting matrix product represents the association (or binding) between either items, or between items and context. The memory of the matrix Model is formed by adding these associations together.
Long Term Memory: Matching versus Retrieval, Episodic versus Semantic
Also, if the evidence and cross validation are strongly in disagreement, I would predict that cross validation would be the better method for predicting generalisation error.
Bayesian FAQ
Vision