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A neural network, also called a neuronal network, is an interconnected population of (typically containing multiple ). Biological neural networks are studied to understand the organization and functioning of .
Closely related are artificial neural networks, machine learning models inspired by biological neural networks. They consist of artificial neurons, which are mathematical functions that are designed to be analogous to the mechanisms used by neural circuits.
Artificial intelligence, cognitive modelling, and artificial neural networks are information processing paradigms inspired by how biological neural systems process data. Artificial intelligence and cognitive modelling try to simulate some properties of biological neural networks. In the artificial intelligence field, artificial neural networks have been applied successfully to speech recognition, image analysis and adaptive control, in order to construct software agents (in Video game) or .
Neural network theory has served to identify better how the neurons in the brain function and provide the basis for efforts to create artificial intelligence.
For Bain, every activity led to the firing of a certain set of neurons. When activities were repeated, the connections between those neurons strengthened. According to his theory, this repetition was what led to the formation of memory. The general scientific community at the time was skeptical of Bain's theory because it required what appeared to be an inordinate number of neural connections within the brain. It is now apparent that the brain is exceedingly complex and that the same brain "wiring" can handle multiple problems and inputs.
James' theory was similar to Bain's; however, he suggested that memories and actions resulted from electrical currents flowing among the neurons in the brain. His model, by focusing on the flow of electrical currents, did not require individual neural connections for each memory or action.
C. S. Sherrington (1898) conducted experiments to test James' theory. He ran electrical currents down the spinal cords of rats. However, instead of demonstrating an increase in electrical current as projected by James, Sherrington found that the electrical current strength decreased as the testing continued over time. Importantly, this work led to the discovery of the concept of habituation.
Warren McCulloch and Walter Pitts (1943) also created a computational model for neural networks based on mathematics and algorithms. They called this model threshold logic. These early models paved the way for neural network research to split into two distinct approaches. One approach focused on biological processes in the brain and the other focused on the application of neural networks to artificial intelligence.
In 1956, Svaetichin discovered some of the neural processes underlaying neural networks in vivo. He had studied the functioning of second order retinal cells (Horizontal Cells), and discovered that in a this first processing layer they operated by an opponency mechanism. This helped explain the first layer of processing of the visual system.
The connectionism of the mid-1980s became popular under the name connectionism. The text by Rumelhart and McClelland (1986) provided a full exposition on the use of connectionism in computers to simulate neural processes.
Artificial neural networks, as used in artificial intelligence, have traditionally been viewed as simplified models of neural processing in the brain, even though the relation between this model and brain biological architecture is debated, as it is not clear to what degree artificial neural networks mirror brain function.
The aim of the field is to create models of biological neural systems in order to understand how biological systems work. To gain this understanding, neuroscientists strive to make a link between observed biological processes (data), biologically plausible mechanisms for neural processing and learning (neural network models) and theory (statistical learning theory and information theory).
Biophysics models, such as BCM theory, have been important in understanding mechanisms for synaptic plasticity, and have had applications in both computer science and neuroscience.
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