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Published **November 1989**
by Academic Pr .

Written in English

- Psychology

**Edition Notes**

Contributions | Gordon H. Bower (Editor) |

The Physical Object | |
---|---|

Format | Paperback |

Number of Pages | 305 |

ID Numbers | |

Open Library | OL9559972M |

ISBN 10 | 0125319584 |

ISBN 10 | 9780125319584 |

Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems (Computational Neuroscience Series) - Kindle edition by Abbott, Laurence F., Dayan, Peter. Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and highlighting while reading Theoretical Neuroscience: Computational and Mathematical Modeling Cited by: Gluck, M. A. and Thompson, R. F. () Modeling the neural substrates of associative learning and memory: a computational approach. Psychological Review, 94(2) [optional] Glanzman, D. L. () The cellular basis of classical conditioning in Aplysia californica -it's less simple . ! 1! Computational!Modelingof!Neural!Systems! Martin!Torres! CaliforniaState!University!Stanislaus! One!University!Circle! Turlock,!CA!! [email protected]! The goal of this new book is to make these tools accessible. It is written specifically for students in neuroscience, cognitive science, and related areas who want to learn about neural systems modeling but lack extensive background in mathematics and computer programming. The book opens with an introduction to computer by:

Specifically, it discusses models that span different brain regions (hippocampus, amygdala, basal ganglia, visual cortex), different species (humans, rats, fruit flies), and different modeling methods (neural network, Bayesian, reinforcement learning, data fitting, and Hodgkin-Huxley models, among others). Computational Models of Brain and Behavior is divided into four sections: (a) Models of brain disorders. Author(s) Summary. Learning and Computational Neuroscience presents recent advances in understanding the brain processes underlying learning and memory, including neural systems analyses of dynamic circuit interactions in the brain and computational models capable of describing simple forms of learning and performance. The goal of this new book is to make these tools accessible. It is written specifically for students in neuroscience, cognitive science, and related areas who want to learn about neural systems modeling but lack extensive background in mathematics and computer book opens with an introduction to computer programming. intelligent programs. Inspired by biological neural networks, ANNs are massively parallel computing systems consisting of an exremely large num- ber of simple processors with many interconnections. ANN models attempt to use some “organizational” principles believed to be used in the human /96/$ IEEE March File Size: 3MB.

Designing a Computational Model of Learning: /ch What would a game or simulation need to have in order to teach a teacher how people learn? This chapter uses a four-part framework of knowledge, learnerCited by: 3. Computational models, that is, mathematical and computational descriptions of component systems, aim to capture the mapping of sensory input to neural responses and furthermore to explain representational transformations, neuronal dynamics, Cited by: • Not to teach you computational modeling • Demystifying computational models • Central message: Computational models are not as complicated (nor as fancy) as they sound, and with a little bit of work, everyone can incorporate it into their research. The first three chapters of the book introduce the loading model of learning and compare it with two other formal models of learning, viz., the ones proposed by Gold () and by Valiant ().

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