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Neural Network ○꠹｜Definition｜1st｜20260628123146-00-⌔
Neural network (machine learning) - Wikipedia
Neural network (machine learning)
In machine learning, a neural network (NN) or neural net, is a computational model inspired by the structure and functions of biological neural networks.12
A neural network consists of connected units or nodes called artificial neurons, which loosely model the neurons in the brain. Artificial neuron models that mimic biological neurons more closely have also been recently investigated and shown to significantly improve performance. These are connected by edges, which model the synapses in the brain. Each artificial neuron receives signals from connected neurons, then processes them and sends a signal to other connected neurons. The “signal” is a real number, and the output of each neuron is computed by some non-linear function of the totality of its inputs, called the activation function. The strength of the signal at each connection is determined by a weight, which adjusts as part of the training process.
Groups of neurons are aggregated into layers. Each layer performs a transformation on its inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), typically passing through multiple intermediate layers (hidden layers). A network is typically called a deep neural network if it has at least two hidden layers. Deep neural networks are capable of learning sophisticated hierarchical representations.3
Training neural networks is a compute-intensive process, accelerated by the use of graphics processing units (GPUs), and large datasets.
Architectural innovations such as convolutional neural networks (CNNs) significantly improved performance in computer vision tasks, while recurrent neural networks (RNNs) enabled modeling of sequential data such as speech and time-series information. Transformer architectures introduced attention mechanisms that allow neural networks to model long-range dependencies in data and have the basis of large language models.4
Artificial neural networks are used for a myriad of tasks including chatbots, large-scale text, image, and video generation, and robotics.
Simplified example of training a neural network in object detection: The network is trained by multiple images that are known to depict starfish and sea urchins, which are correlated with “nodes” that represent visual features. The starfish match with a ringed texture and a star outline, whereas most sea urchins match with a striped texture and oval shape. However, the instance of a ring textured sea urchin creates a weakly weighted association between them.
Subsequent run of the network on an input image:5 The network correctly detects the starfish. However, the weakly weighted association between ringed texture and sea urchin also confers a weak signal to the latter from one of two intermediate nodes. In addition, a shell that was not included in the training gives a weak signal for the oval shape, also resulting in a weak signal for the sea urchin output. These weak signals may result in a false positive result for sea urchin.
In reality, textures and outlines would not be represented by single nodes, but rather by associated weight patterns of multiple nodes.Printed 2026-06-28.
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Link to original Footnotes
Hardesty L (14 April 2017). “Explained: Neural networks”. MIT News Office. Archived from the original on 18 March 2024. Retrieved 2 June 2022. ↩
Yang Z, Yang Z (2014). Comprehensive Biomedical Physics. Karolinska Institute, Stockholm, Sweden: Elsevier. p. 1. ISBN 978-0-444-53633-4. Archived from the original on 28 July 2022. Retrieved 28 July 2022. ↩
Bishop CM (17 August 2006). Pattern Recognition and Machine Learning (PDF). New York: Springer. ISBN 978-0-387-31073-2. ↩
Vaswani A, Shazeer N, Parmar N (2017). “Attention Is All You Need” (PDF). Advances in Neural Information Processing Systems. 30. arXiv:1706.03762. ↩
Ferrie, C., Kaiser, S. (2019). Neural Networks for Babies. Sourcebooks. ISBN 978-1-4926-7120-6. ↩
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