The Computational Brain, 25th Anniversary EditionMIT Press, 28 жовт. 2016 р. - 568 стор. An anniversary edition of the classic work that influenced a generation of neuroscientists and cognitive neuroscientists. Before The Computational Brain was published in 1992, conceptual frameworks for brain function were based on the behavior of single neurons, applied globally. In The Computational Brain, Patricia Churchland and Terrence Sejnowski developed a different conceptual framework, based on large populations of neurons. They did this by showing that patterns of activities among the units in trained artificial neural network models had properties that resembled those recorded from populations of neurons recorded one at a time. It is one of the first books to bring together computational concepts and behavioral data within a neurobiological framework. Aimed at a broad audience of neuroscientists, computer scientists, cognitive scientists, and philosophers, The Computational Brain is written for both expert and novice. This anniversary edition offers a new preface by the authors that puts the book in the context of current research. This approach influenced a generation of researchers. Even today, when neuroscientists can routinely record from hundreds of neurons using optics rather than electricity, and the 2013 White House BRAIN initiative heralded a new era in innovative neurotechnologies, the main message of The Computational Brain is still relevant. |
Зміст
1 Introduction | 1 |
2 Neuroscience Overview | 17 |
Levels in Nervous Systems | 18 |
Structure at Various Levels of Organization | 27 |
A Short List of Brain Facts | 48 |
3 Computational Overview | 61 |
Looking Up The Answer | 69 |
Linear Associators | 77 |
Cells Circuits Brains and Behavior | 239 |
Learning and the Hippocampus | 243 |
Donald Hebb and Synaptic Plasticity | 250 |
Mechanisms of Neuronal Plasticity | 254 |
Cells and Circuits | 281 |
Decreasing Synaptic Strength | 289 |
Back to Systems and Behavior | 295 |
Being and Timing | 305 |
Hopfield Networks and Boltzmann Machines | 82 |
Learning in Neural Nets | 96 |
Competitive Learning | 102 |
Curve Fitting | 105 |
Two Examples | 107 |
Recurrent Nets | 115 |
From Toy World to Real World | 125 |
What Good Are Optimization Procedures to Neuroscience? | 130 |
Realistic and Abstract | 136 |
Concluding Remarks | 137 |
4 Representing the World | 141 |
Constructing a Visual World | 142 |
Thumbnail Sketch of the Mammalian Visual System | 148 |
What Can We Learn From the Visual System? | 157 |
What Is So Special About Distribution? | 163 |
World Enough and Time | 174 |
A Neurocomputational Study | 183 |
Stereo Vision | 188 |
Computational Models of Stereo Vision | 199 |
From Mystery to Mechanism | 221 |
Vector Averaging | 233 |
Concluding Remarks | 237 |
Development of Nervous Systems | 307 |
Modules and Networks | 316 |
6 Sensorimotor Integration | 331 |
LeechNet | 341 |
Computation and the VestibuloOcular Reflex | 353 |
Time and Time Again | 379 |
The Segmental Swimming Oscillator | 388 |
Modeling the Neuron | 399 |
Concluding Remarks | 411 |
7 Concluding and Beyond | 413 |
Afterword | 425 |
Anatomical and Physiological Techniques | 427 |
Reversible Lesions and Microlesions | 430 |
Imagine Techniques | 432 |
Gross Electrical and Magnetic Recording | 437 |
SingleUnit Recording | 440 |
Anatomical Tract Tracing | 442 |
Notes | 445 |
Glossary | 457 |
479 | |
525 | |
Інші видання - Показати все
The Computational Brain, 25th Anniversary Edition Patricia S. Churchland,Terrence J. Sejnowski Попередній перегляд недоступний - 2016 |