Artificial neuron

An artificial neuron is a mathematical function conceived as a model of biological neurons in a neural network. Artificial neurons are the elementary units of artificial neural networks.^[1] The artificial neuron is a function that receives one or more inputs, applies weights to these inputs, and sums them to produce an output.

The design of the artificial neuron was inspired by neural circuitry. Its inputs are analogous to excitatory postsynaptic potentials and inhibitory postsynaptic potentials at neural dendrites, or activation, its weights are analogous to synaptic weight, and its output is analogous to a neuron's action potential which is transmitted along its axon.

Usually, each input is separately weighted, and the sum is often added to a term known as a bias (loosely corresponding to the threshold potential), before being passed through a non-linear function known as an activation function or transfer function^{[clarification needed]}. The transfer functions usually have a sigmoid shape, but they may also take the form of other non-linear functions, piecewise linear functions, or step functions. They are also often monotonically increasing, continuous, differentiable and bounded. Non-monotonic, unbounded and oscillating activation functions with multiple zeros that outperform sigmoidal and ReLU-like activation functions on many tasks have also been recently explored. The thresholding function has inspired building logic gates referred to as threshold logic; applicable to building logic circuits resembling brain processing. For example, new devices such as memristors have been extensively used to develop such logic in recent times.^[2]

The artificial neuron transfer function should not be confused with a linear system's transfer function.

An artificial neuron may be referred to as a semi-linear unit, Nv neuron, binary neuron, linear threshold function, or McCulloch–Pitts (MCP) neuron, depending on the structure used.

Simple artificial neurons, such as the McCulloch–Pitts model, are sometimes described as "caricature models", since they are intended to reflect one or more neurophysiological observations, but without regard to realism.^[3] Artificial neurons can also refer to artificial cells in neuromorphic engineering (see below) that are similar to natural physical neurons.

^ Rami A. Alzahrani; Alice C. Parker. "Neuromorphic Circuits With Neural Modulation Enhancing the Information Content of Neural Signaling". Proceedings of International Conference on Neuromorphic Systems 2020. Art. 19. New York: Association for Computing Machinery. doi:10.1145/3407197.3407204. ISBN 978-1-4503-8851-1. S2CID 220794387.
^ Maan, A. K.; Jayadevi, D. A.; James, A. P. (1 January 2016). "A Survey of Memristive Threshold Logic Circuits". IEEE Transactions on Neural Networks and Learning Systems. PP (99): 1734–1746. arXiv:1604.07121. Bibcode:2016arXiv160407121M. doi:10.1109/TNNLS.2016.2547842. ISSN 2162-237X. PMID 27164608. S2CID 1798273.
^ F. C. Hoppensteadt and E. M. Izhikevich (1997). Weakly connected neural networks. Springer. p. 4. ISBN 978-0-387-94948-2.

[1] Rami A. Alzahrani; Alice C. Parker. "Neuromorphic Circuits With Neural Modulation Enhancing the Information Content of Neural Signaling". Proceedings of International Conference on Neuromorphic Systems 2020. Art. 19. New York: Association for Computing Machinery. doi:10.1145/3407197.3407204. ISBN 978-1-4503-8851-1. S2CID 220794387.

[2] Maan, A. K.; Jayadevi, D. A.; James, A. P. (1 January 2016). "A Survey of Memristive Threshold Logic Circuits". IEEE Transactions on Neural Networks and Learning Systems. PP (99): 1734–1746. arXiv:1604.07121. Bibcode:2016arXiv160407121M. doi:10.1109/TNNLS.2016.2547842. ISSN 2162-237X. PMID 27164608. S2CID 1798273.

[3] F. C. Hoppensteadt and E. M. Izhikevich (1997). Weakly connected neural networks. Springer. p. 4. ISBN 978-0-387-94948-2.

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