Friday, December 10, 2010
Wednesday, December 8, 2010
This is true of ANNs as well. ANNs can process information at a great speed owing to their highly massive parallelism. Neural networks, with their remarkable ability to derive meaning from complicated or Imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. A trained neural network can be thought of as an "expert" in the category of information it has been given to analyse.
Biological Neural Networks
Much is still unknown about how the brain trains itself to process information, so theories abound. In the human brain, a typical neuron collects signals from others through a host of fine structures called dendrites . The neuron sends out spikes of electrical activity through a long, thin stand known as an axon , which splits into thousands of branches. At the end of each branch, a structure called a synapse converts the activity from the axon into electrical effects that inhibit or excite activity from the axon into electrical effects that inhibit or excite activity in the connected neurones. When a neuron receives Artificial Neural Networks Artificial neural networks are represented by a set of nodes, often arranged in layers, and a set of weighted directed links connecting them. The nodes are equivalent to neurons, while the links denote synapses. The nodes are the information processing units and the links acts as communicating media. There are a wide variety of networks depending on the nature of information processing carried out at individual nodes, the topology of the links, and the algorithm for adaptation of link weights. Some of the popular among them include:
The nueron sends out spikes of electrical activity through a long, thin strand known as an axon, which splits into thousands of brahches. At the end of each branch, a structure called a synapse converts the activity from te axon into electrcal effects that inhibit the activity in the connected neurons. When a nueron receieves excitatory input that sufficiently large compared to its inhiitary input, it sends a spike of electrical activity down its axon. Learning occurs by changing the effectiveness of the synapses so that the influence of one neuron on another changes.
• task T