30-08-2017, 02:51 PM
In neuroscience a biological neural network is a series of interconnected neurons whose activation defines a recognizable linear pathway. The interface through which neurons interact with their neighbors usually consists of several axonal terminals connected by synapses to dendrites in other neurons. If the sum of the input signals in a neuron exceeds a certain threshold, the neuron sends an action potential (AP) on the axon hill and transmits this electrical signal along the axon. Biological neural networks have inspired the design of artificial neural networks.
The Principles of Psychology by Herbert Spencer, The Third Edition (1872), Theodor Meynert's Psychiatry (1884), The Principles of Psychology by William James (1890) and Sigmund Freud's Project for Scientific Psychology (composed in 1895). The first rule of neuronal learning was described by Hebb in 1949, Hebbian learning. Therefore, the hebbian pairing of pre-synaptic and post-synaptic activity can substantially alter the dynamic characteristics of the synaptic connection and thus facilitate or inhibit signal transmission. Neuro-scientists Warren Sturgis McCulloch and Walter Pitts published the first works on neural network processing. They demonstrated theoretically that artificial neuron networks could implement logical, arithmetic, and symbolic functions. Simplified models of biological neurons, now generally called perceptrons or artificial neurons, were established. These simple models represented the neural sum (ie, potentials in the post-synaptic membrane will aggregate in the cell body). Later models also provided excitatory and inhibitory synaptic transmission.
The Principles of Psychology by Herbert Spencer, The Third Edition (1872), Theodor Meynert's Psychiatry (1884), The Principles of Psychology by William James (1890) and Sigmund Freud's Project for Scientific Psychology (composed in 1895). The first rule of neuronal learning was described by Hebb in 1949, Hebbian learning. Therefore, the hebbian pairing of pre-synaptic and post-synaptic activity can substantially alter the dynamic characteristics of the synaptic connection and thus facilitate or inhibit signal transmission. Neuro-scientists Warren Sturgis McCulloch and Walter Pitts published the first works on neural network processing. They demonstrated theoretically that artificial neuron networks could implement logical, arithmetic, and symbolic functions. Simplified models of biological neurons, now generally called perceptrons or artificial neurons, were established. These simple models represented the neural sum (ie, potentials in the post-synaptic membrane will aggregate in the cell body). Later models also provided excitatory and inhibitory synaptic transmission.