"An artificial neural network (or simply a neural network) is a biologically inspired computational model that consists of processing elements (neurons) and connections between them, as well as of training and recall algorithms." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)
"Biologically inspired computational model consisting of processing elements (called neurons) and connections between them with coefficients (weights) bound to the connections, which constitute the neuronal structure. Training and recall algorithms are also attached to the structure." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)
"massively parallel interconnected network of simple (usually adaptive) elements and their hierarchical organizations, intended to interact with the objects of the real world in the same way as the biological nervous systems do. In a more general sense, artificial neural networks also encompass abstract schemata, such as mathematical estimators and systems of symbolic rules, constructed automatically from masses of examples, without heuristic design or other human intervention. Such schemata are supposed to describe the operation of biological or artificial neural networks in a highly idealized form and define certain performance limits." (Teuvo Kohonen, "Self-Organizing Maps" 3rd Ed., 2001)
"A collaboration of simple, primitive processing elements that self-organize and self-optimize to achieve computation goals. While these occur in biological systems, in this context we usually mean artificial neural networks such as might be used in optical character recognition applications." (Bruce P Douglass, "Real-Time Agility", 2009)
"An artificial neural network, often just called a “neural network” (NN), is an interconnected group of artificial neurons that uses a mathematical model or computational model for information processing based on a connectionist approach to computation. Knowledge is acquired by the network from its environment through a learning process, and interneuron connection strengths (synaptic weighs) are used to store the acquired knowledge." (Larbi Esmahi et al, "Adaptive Neuro-Fuzzy Systems", Encyclopedia of Artificial Intelligence, 2009)
"An interconnected group of units or neurons that uses a mathematical model for information processing based on a connectionist approach to computation." (Soledad Delgado et al, "Growing Self-Organizing Maps for Data Analysis", Encyclopedia of Artificial Intelligence, 2009)
"Artificial neural networks (ANNs) are non-linear mapping structures based on the function of the human brain. They are powerful tools for modeling, especially when the underlying data relationship is unknown." (Siddhartha Bhattacharjee et al, "Quantum Backpropagation Neural Network Approach for Modeling of Phenol Adsorption from Aqueous Solution by Orange Peel Ash", 2013)
"A computer representation of knowledge that attempts to mimic the neural networks of the human body" (Nell Dale & John Lewis, "Computer Science Illuminated" 6th Ed., 2015)
"a massively parallel distributed processor made up of simple processing units, which has a natural propensity for storing experimental knowledge and making it available for use." (Pablo Escandell-Montero et al, "Artificial Neural Networks in Physical Therapy", 2015)
"Computational models inspired by brain's nervous systems which are capable of machine learning and pattern recognition. ANN are composed by simple, and highly interconnected processing elements that process information by their dynamic state response to external inputs." (Nuno Pombo et al, "Machine Learning Approaches to Automated Medical Decision Support Systems", 2015)
"Computational models inspired by the properties of biological nervous systems. Usually composed of layers of highly interconnected simple processing units, they are characterised by learning capabilities and can be implemented in software and hardware." (D T Pham & M Castellani, "The Bees Algorithm as a Biologically Inspired Optimisation Method", 2015)
"Computer models of interconnected neurons that can be trained to carry out pattern recognition and other low-level cognitive functions through supervised or unsupervised of learning." (Eitan Gross, "Stochastic Neural Network Classifiers", Encyclopedia of Information Science and Technology 3rd Ed., 2015)
"Is non-parametric tool that learns from the surroundings, retains the learning and uses it subsequently." (Kandarpa K Sarma, "Learning Aided Digital Image Compression Technique for Medical Application", 2016)
"A computational graph for machine learning or simulation of a biological neural network (brain)." (Hobson Lane et al, "Natural Language Processing in Action: Understanding, analyzing, and generating text with Python", 2019)
"A machine learning algorithm that is created by mimicking the information transmission and problem-solving mechanism in the human brain." (Tolga Ensari et al, "Overview of Machine Learning Approaches for Wireless Communication", 2019)
"Information elaboration system, software, or hardware that is based on the biological nervous systems, and it is composed of code units called 'nodes' or 'artificial neurons'." (Gaetano B Ronsivalle & Arianna Boldi, "Artificial Intelligence Applied: Six Actual Projects in Big Organizations", 2019)
"A predictive computer algorithm inspired by the biology of the human brain that can learn linear and non-linear functions from data. Artificial neural networks are particularly useful when the complexity of the data or the modelling task makes the design of a function that maps inputs to outputs by hand impractical." (Timofei Bogomolov et al, "Identifying Patterns in Fresh Produce Purchases: The Application of Machine Learning Techniques", 2020)
"An artificial neural network is a collection of neurons connected by weights." (Alex Thomas, "Natural Language Processing with Spark NLP", 2020)
"Is a computational model based on the structure and functions of biological neural networks." (Heorhii Kuchuk et al, "Application of Deep Learning in the Processing of the Aerospace System's Multispectral Images", 2020)
"It mimics animal neural networks and useful in taking some action by observing some example instead of being explicitly programmed." (Shouvik Chakraborty & Kalyani Mali, "An Overview of Biomedical Image Analysis From the Deep Learning Perspective", 2020)
"An artificial neural network is based on a simplification of neurons in an animal brain which is a group of interconnected neurons." (Hari K Kondaveeti et al, "Deep Learning Applications in Agriculture: The Role of Deep Learning in Smart Agriculture", 2021)
"Artificial neural networks (ANNs) are a type of computing system that is inspired by biological neural networks present in the animal brain." (Hari K Kondaveeti et al, "Deep Learning Applications in Agriculture: The Role of Deep Learning in Smart Agriculture", 2021)
"Artificial neural networks (ANNs), also simply called neural networks (NNs), are bionic systems of neurons for vaguely computing and responding like human brains. ANNs show their power in the field of prediction and classification for a long time by a black-box system. ANNs enter a new era with the assistance of GPU for deep learning nowadays." (Yuh-Wen Chen, "Social Network Analysis: Self-Organizing Map and WINGS by Multiple-Criteria Decision Making", 2021)
"It is a computing model based on the structure of the human brain with many interconnected processing nodes that model input-output relationships. The model is organized in layers of nodes that interconnect to each other." (Mário P Véstias, "Convolutional Neural Network", 2021)
"It is an information processing model inspired by the form of the brain in which biological nervous systems, such as the brain, process information." (Mehmet A Cifci, "Optimizing WSNs for CPS Using Machine Learning Techniques", 2021)
"An artificial neuron network (ANN) is a computing system patterned after the operation of neurons in the human brain." (Databricks) [source]
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