May 182016
 

This is the second article in the series about artificial neural networks. If you have not already done so, I recommend you read the first article, “Neural Networks: The Node“, before proceeding. It covers material that should be understood before attempting to tackle the topics presented here and in future articles in this series.

There are several properties that define the structure and functionality of neural networks: the network architecture, the learning paradigm, the learning rule, and the learning algorithm.

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May 082016
 

As I covered previously in “Introduction to Neural Networks,” artificial neural networks (ANN) are simplified representations of biological neural networks in which the basic computational unit known as an artificial neuron, or node, represents its biological counterpart, the neuron. In order to understand how neural networks can be taught to identify and classify, it is first necessary to explore the characteristics and functionality of the basic building block itself, the node.

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Mar 102016
 

neural network

Modeled after observed biology and behavior within the brain, neural networks are arguably the most popular of the biologically inspired AI methods. Neural networks excel at pattern recognition and classification tasks including facial, speech, and handwriting recognition. They also often play a central role in video game character AI.
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Nov 052005
 
Visual Neural Patterns

source: Poggio/DiCarlo Labs

MIT has published a news release about how neuroscientists in the McGovern Institute for Brain Research have recently made significant advances in their attempts to learn how the inferotemporal (IT) cortex identifies and categorizes visual data. The ability to visually recognize objects, while usually taken for granted because it happens quickly, automatically, and subconsciously, is actually a complex problem for the brain to solve. This research provides some insight into how the brain encodes, formats and saves visual information.

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