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008			250603b |||||||| |||| 00| 0 eng d
020			_a9780262541855
082			_a612.8 _bDAY
100			_aPeter Dayan
245			_aTheoretical Neuroscience _bComputational and Mathematical Modeling of Neural Systems _cPeter Dayan and LF Abbott
260			_bMIT Press _cSeptember 1, 2005
300			_a480 pages
505			_tPart I: Neural Encoding and Decoding 1 Neural encoding I: Firing rates and spike statistics 2 Neural encoding II: Reverse correlation and visual receptive fields 3 Neural decoding 4 Information theory Part II: Neurons and Neural Circuits 5 Model neurons I: Neuroelectronics 6 Model neurons II: Conductances and morphology 7 Network models pdf ps.gz Part III: Adaptation and Learning 8 Plasticity and learning 9 Classical conditioning and reinforcement learing 10 Representational learning Mathematical appendix
520			_aTheoretical neuroscience provides a quantitative basis for describing what nervous systems do, determining how they function, and uncovering the general principles by which they operate. This text introduces the basic mathematical and computational methods of theoretical neuroscience and presents applications in a variety of areas including vision, sensory-motor integration, development, learning, and memory. The book is divided into three parts. Part I discusses the relationship between sensory stimuli and neural responses, focusing on the representation of information by the spiking activity of neurons. Part II discusses the modeling of neurons and neural circuits on the basis of cellular and synaptic biophysics. Part III analyzes the role of plasticity in development and learning. An appendix covers the mathematical methods used, and exercises are available on the book's Web site.
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