New PDF release: Advances in synaptic plasticity

By Michel Baudry, Joel L. Davis, Richard F. Thompson

ISBN-10: 0262024608

ISBN-13: 9780262024600

ISBN-10: 0585165742

ISBN-13: 9780585165745

Many neurons express plasticity; that's, they could switch structurally or functionally, usually in an enduring manner. Plasticity is obvious in such diversified phenomena as studying and reminiscence, mind improvement, drug tolerance, sprouting of axon terminals after a mind lesion, and diverse mobile kinds of activity-dependent synaptic plasticity corresponding to long term potentiation and long term melancholy. This e-book, a follow-up to the editors' Synaptic Plasticity (MIT Press, 1993), stories at the most up-to-date tendencies within the box. the degrees of research variety from molecular to mobile and community, the unifying subject matter being the character of the relationships among synaptic plasticity and data processing and garage. members: Yael Amitai, Michel Baudry, Theodore W. Berger, Pierre-Alain Buchs, A. okay. Butler, Franck A. Chaillan, Gilbert A. Chauvet, Marie-Françoise Chesselet, Barry W. Connors, Taraneh Ghaffari, Jay R. Gibson, Ziv Gil, Michel Khrestchatisky, Dietmar Kuhl, Carole E. Landisman, Gilles Laurent, Jim-Shih Liaw, David J. Linden, Katrina MacLeod, Henry Markram, W. V. Morehouse, Dominique Muller, J. A. Napieralski, Santiago Rivera, François S. Roman, Bernard Soumireu-Mourat, Oswald Steward, Mark Stopfer, F. G. Szele, Richard F. Thompson, Nicolas Toni, Bernard Truchet, Misha Tsodyks, okay. Uryu, Ascher Uziel, Christopher S. Wallace, Yun Wang, Michael Wehr, Paul F. Worley, Xiaping Xie.

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C. N. (2003). Estimating a state–space model from point process observations. Neural Computation, 15, 965–991. , Frank, L. , Graybiel, A. , Suzuki, W. and Brown, E. N. (2004). Dynamic analysis of learning in behavioral experiments. J of Neuroscience, 24, 2, 447–461, 2004. N. , Scalon, J. , Suzuki, W. A. and Brown, E. N. (2010). State–space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscienc, vol. 2010, article ID 426539, 14 pages. 1155/2010/426539. , Stefani, M.

State–space algorithms for estimating spike rate functions. Computational Intelligence and Neuroscienc, vol. 2010, article ID 426539, 14 pages. 1155/2010/426539. , Stefani, M. , and Brown, E. N. (2005). Analysis and design of behavioral experiments to characterize population learning. J of Neurophysiology, 93, 1776–1792. A. N. (2007). Bayesian analysis of interleaved learning and response bias in behavioral experiments. J of Neurophysiology, 97, 3, 2516–2524. N. (2006). Selective enhancement of associative learning by microstimulation of the anterior caudate.

HMM assumes the presence of two Markov processes, each of which, by definition, depends only on the current state of the system and is independent of the history of prior states. When applied to neural spike trains, one of the Markov processes is the emission of spikes by a neuron at any point in time. The Markov assumption for spike emission assumes that spike trains follow a Poisson process at a fixed mean rate, given a particular state of the system. 1). The second Markov assumption pertains to the state of the system itself and assumes that when the system is in a specific state, its probability of transition to another state is both independent of prior states and constant in time.

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Advances in synaptic plasticity by Michel Baudry, Joel L. Davis, Richard F. Thompson


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