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Neuromorphology (from Greek νεῦρον, neuron, "nerve"; μορφή, morphé, "form"; -λογία, -logia, “study of” [1] [2]) is the study of nervous system form, shape, and structure. The study involves looking at a particular part of the nervous system from a molecular and cellular level and connecting it to a physiological and ...
Artificial neuron structure. An artificial neuron is a mathematical function conceived as a model of a biological neuron in a neural network. The artificial neuron is the elementary unit of an artificial neural network. [1] The design of the artificial neuron was inspired by biological neural circuitry.
Structure of a typical neuron with Schwann cells in the peripheral nervous system. Neurons are highly specialized for the processing and transmission of cellular signals. Given the diversity of functions performed in different parts of the nervous system, there is a wide variety in their shape, size, and electrochemical properties.
Mathematically, a neuron's network function () is defined as a composition of other functions (), that can further be decomposed into other functions. This can be conveniently represented as a network structure, with arrows depicting the dependencies between functions.
The focus of this article is a comprehensive view of modeling a neural network (technically neuronal network based on neuron model). Once an approach based on the perspective and connectivity is chosen, the models are developed at microscopic (ion and neuron), mesoscopic (functional or population), or macroscopic (system) levels.
The "signal" input to each neuron is a number, specifically a linear combination of the outputs of the connected neurons in the previous layer. The signal each neuron outputs is calculated from this number, according to its activation function. The behavior of the network depends on the strengths (or weights) of the connections between neurons.
The two main neuronal classes in the cerebral cortex are excitatory projection neurons (around 70-80%) and inhibitory interneurons (around 20–30%). [2] Neurons are often grouped into a cluster known as a nucleus where they usually have roughly similar connections and functions. [3] Nuclei are connected to other nuclei by tracts of white matter.
The most basic model of a neuron consists of an input with some synaptic weight vector and an activation function or transfer function inside the neuron determining output. This is the basic structure used for artificial neurons, which in a neural network often looks like = ()