Daniel S. Levine is Professor of Psychology at the University of Texas at Arlington. He is a Fellow and former President of the International Neural Network Society. His research involves computational modeling of brain processes in decision making and cognitive-emotional interactions.

‘Newly updated with advances in cognitive neuroscience and modeling, this introductory textbook provides a remarkable overview of the whole field of neural and cognitive modeling, from its inception nearly a century ago to the most recent advances. The beginner can easily gain an overview of the principles of modeling, while the advanced student will find ample mathematical detail and practical simulation exercises. This new edition will be my go-to text for advanced undergraduates and graduate students looking for an introduction to the subject.’ Professor Joshua W. Brown, Indiana University, USA‘Levine's book achieves an impressive synthesis of historical trends and current research results in both biological and artificial neural network research. This synthesis clarifies that the currently popular Deep Learning is just one contribution to this burgeoning field, and one that does not incorporate many of the most powerful properties of biological learning. Levine's book provides an accessible introduction to many of these properties, while also reviewing important properties of neural models of vision and visual attention, sequence learning and performance, executive function, and decision-making, among its other expository accomplishments.’ Stephen Grossberg, Boston University, USA

• ContentsPart I: Foundations of Neural Network TheoryChapter 1: Neural Networks for Modeling BehaviorChapter 2: Historical OutlineChapter 3: Associative Learning and Synaptic PlasticityChapter 4: Competition, Lateral Inhibition, and Short-Term MemoryPart II: Computational Cognitive NeuroscienceChapter 5: Progress in Cognitive NeuroscienceChapter 6: Models of Conditioning and Reinforcement LearningChapter 7: Models of Coding, Categorization, and Unsupervised LearningChapter 8: Models of Supervised Pattern and Category LearningChapter 9: Models of Complex Mental FunctionsAppendicesAppendix 1: Mathematical Techniques for Neural NetworksAppendix 2: Basic Facts of NeurobiologyReferences

Daniel S. Levine is Professor of Psychology at the University of Texas at Arlington. He is a Fellow and former President of the International Neural Network Society. His research involves computational modeling of brain processes in decision making and cognitive-emotional interactions.

‘Newly updated with advances in cognitive neuroscience and modeling, this introductory textbook provides a remarkable overview of the whole field of neural and cognitive modeling, from its inception nearly a century ago to the most recent advances. The beginner can easily gain an overview of the principles of modeling, while the advanced student will find ample mathematical detail and practical simulation exercises. This new edition will be my go-to text for advanced undergraduates and graduate students looking for an introduction to the subject.’ Professor Joshua W. Brown, Indiana University, USA‘Levine's book achieves an impressive synthesis of historical trends and current research results in both biological and artificial neural network research. This synthesis clarifies that the currently popular Deep Learning is just one contribution to this burgeoning field, and one that does not incorporate many of the most powerful properties of biological learning. Levine's book provides an accessible introduction to many of these properties, while also reviewing important properties of neural models of vision and visual attention, sequence learning and performance, executive function, and decision-making, among its other expository accomplishments.’ Stephen Grossberg, Boston University, USA

• ContentsPart I: Foundations of Neural Network TheoryChapter 1: Neural Networks for Modeling BehaviorChapter 2: Historical OutlineChapter 3: Associative Learning and Synaptic PlasticityChapter 4: Competition, Lateral Inhibition, and Short-Term MemoryPart II: Computational Cognitive NeuroscienceChapter 5: Progress in Cognitive NeuroscienceChapter 6: Models of Conditioning and Reinforcement LearningChapter 7: Models of Coding, Categorization, and Unsupervised LearningChapter 8: Models of Supervised Pattern and Category LearningChapter 9: Models of Complex Mental FunctionsAppendicesAppendix 1: Mathematical Techniques for Neural NetworksAppendix 2: Basic Facts of NeurobiologyReferences

Daniel S. Levine is Professor of Psychology at the University of Texas at Arlington. He is a Fellow and former President of the International Neural Network Society. His research involves computational modeling of brain processes in decision making and cognitive-emotional interactions.

‘Newly updated with advances in cognitive neuroscience and modeling, this introductory textbook provides a remarkable overview of the whole field of neural and cognitive modeling, from its inception nearly a century ago to the most recent advances. The beginner can easily gain an overview of the principles of modeling, while the advanced student will find ample mathematical detail and practical simulation exercises. This new edition will be my go-to text for advanced undergraduates and graduate students looking for an introduction to the subject.’ Professor Joshua W. Brown, Indiana University, USA‘Levine's book achieves an impressive synthesis of historical trends and current research results in both biological and artificial neural network research. This synthesis clarifies that the currently popular Deep Learning is just one contribution to this burgeoning field, and one that does not incorporate many of the most powerful properties of biological learning. Levine's book provides an accessible introduction to many of these properties, while also reviewing important properties of neural models of vision and visual attention, sequence learning and performance, executive function, and decision-making, among its other expository accomplishments.’ Stephen Grossberg, Boston University, USA

• ContentsPart I: Foundations of Neural Network TheoryChapter 1: Neural Networks for Modeling BehaviorChapter 2: Historical OutlineChapter 3: Associative Learning and Synaptic PlasticityChapter 4: Competition, Lateral Inhibition, and Short-Term MemoryPart II: Computational Cognitive NeuroscienceChapter 5: Progress in Cognitive NeuroscienceChapter 6: Models of Conditioning and Reinforcement LearningChapter 7: Models of Coding, Categorization, and Unsupervised LearningChapter 8: Models of Supervised Pattern and Category LearningChapter 9: Models of Complex Mental FunctionsAppendicesAppendix 1: Mathematical Techniques for Neural NetworksAppendix 2: Basic Facts of NeurobiologyReferences