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The NEURON software, developed at Duke University, is a simulation environment for modeling individual neurons and networks of neurons. [25] The NEURON environment is a self-contained environment allowing interface through its GUI or via scripting with hoc or python. The NEURON simulation engine is based on a Hodgkin–Huxley type model using a ...
The goal of any supervised learning algorithm is to find a function that best maps a set of inputs to their correct output. The motivation for backpropagation is to train a multi-layered neural network such that it can learn the appropriate internal representations to allow it to learn any arbitrary mapping of input to output.
A common use of the phrase "ANN model" is really the definition of a class of such functions (where members of the class are obtained by varying parameters, connection weights, or specifics of the architecture such as the number of neurons, number of layers or their connectivity).
When multiple layers use the identity activation function, the entire network is equivalent to a single-layer model. Range When the range of the activation function is finite, gradient-based training methods tend to be more stable, because pattern presentations significantly affect only limited weights.
Universal approximation theorems are limit theorems: They simply state that for any and a criterion of closeness >, if there are enough neurons in a neural network, then there exists a neural network with that many neurons that does approximate to within . There is no guarantee that any finite size, say, 10000 neurons, is enough.
A wide variety of sigmoid functions including the logistic and hyperbolic tangent functions have been used as the activation function of artificial neurons. Sigmoid curves are also common in statistics as cumulative distribution functions (which go from 0 to 1), such as the integrals of the logistic density , the normal density , and Student's ...
But they can be broadly understood in 4 categories of learning methods, though these categories don't have clear boundaries and they tend to belong to multiple categories of learning methods [3] - Hebbian - Neocognitron, Brain-state-in-a-box [4] Gradient Descent - ADALINE, Hopfield Network, Recurrent Neural Network