東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Probabilistic spiking neuronal nets = neuromathematics for the computer era /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Probabilistic spiking neuronal nets/ by Antonio Galves, Eva Löcherbach, Christophe Pouzat.
其他題名:	neuromathematics for the computer era /
作者:	Galves, A.
其他作者:	Löcherbach, E.
出版者:	Cham :Springer International Publishing : : 2024.,
面頁冊數:	xv, 199 p. :ill. (some col.), digital ;24 cm.
內容註:	A Neurophysiology Primer for Mathematicians -- A Discrete Time Stochastic Neural Network Model -- Mean Field Limits for Discrete Time Stochastic Neural Network Models -- But Time is Continuous! -- Models without Reset: Hawkes Processes -- What is a Stationary State in a Potentially Infinite System? -- Statistical Estimation of the Interaction Graph -- Mean Field Limits and Short-Term Synaptic Facilitation in Continuous Time Models -- A Non-Exhaustive List of Some Open Questions -- Appendix A -- Appendix B -- Appendix C -- Appendix D -- Appendix E -- Appendix F -- References -- Index.
Contained By:	Springer Nature eBook
標題:	Stochastic models. -
電子資源:	https://doi.org/10.1007/978-3-031-68409-8
ISBN:	9783031684098

Probabilistic spiking neuronal nets = neuromathematics for the computer era /
Galves, A.

Probabilistic spiking neuronal netsneuromathematics for the computer era /[electronic resource] :by Antonio Galves, Eva Löcherbach, Christophe Pouzat. - Cham :Springer International Publishing :2024. - xv, 199 p. :ill. (some col.), digital ;24 cm. - Lecture notes on mathematical modelling in the life sciences,2193-4797. - Lecture notes on mathematical modelling in the life sciences..

A Neurophysiology Primer for Mathematicians -- A Discrete Time Stochastic Neural Network Model -- Mean Field Limits for Discrete Time Stochastic Neural Network Models -- But Time is Continuous! -- Models without Reset: Hawkes Processes -- What is a Stationary State in a Potentially Infinite System? -- Statistical Estimation of the Interaction Graph -- Mean Field Limits and Short-Term Synaptic Facilitation in Continuous Time Models -- A Non-Exhaustive List of Some Open Questions -- Appendix A -- Appendix B -- Appendix C -- Appendix D -- Appendix E -- Appendix F -- References -- Index.

This book provides a self-contained introduction to a new class of stochastic models for systems of spiking neurons. These systems have a large number of interacting components, each one evolving as a stochastic process with a memory of variable length. Several mathematical tools are put to use, such as Markov chains, stochastic chains having memory of variable length, point processes having stochastic intensity, Hawkes processes, random graphs, mean field limits, perfect sampling algorithms, the Context algorithm, and statistical model selection. The book's focus on mathematically tractable objects distinguishes it from other texts on theoretical neuroscience. The biological complexity of neurons is not ignored, but reduced to some of its main features, such as the intrinsic randomness of neuronal dynamics. This reduction in complexity aims at explaining and reproducing statistical regularities and collective phenomena that are observed in experimental data, an approach that leads to mathematically rigorous results. With an emphasis on a constructive and algorithmic point of view, this book is directed towards mathematicians interested in learning about stochastic network models and their neurobiological underpinning, and neuroscientists interested in learning how to build and prove results with mathematical models that relate to actual experimental settings.

ISBN: 9783031684098

Standard No.: 10.1007/978-3-031-68409-8doiSubjects--Topical Terms:

764002
Stochastic models.

LC Class. No.: QA274.2

Dewey Class. No.: 519.22

Probabilistic spiking neuronal nets = neuromathematics for the computer era /
LDR:03132nmm a2200349 a 4500 001 2380357
003 DE-He213
005 20241017125744.0
006 m d
007 cr nn 008maaau
008 250418s2024 sz s 0 eng d
020 $a 9783031684098 $q (electronic bk.)
020 $a 9783031684081 $q (paper)
024 7 $a 10.1007/978-3-031-68409-8 $2 doi
035 $a 978-3-031-68409-8
040 $a GP $c GP
041 0 $a eng
050 4 $a QA274.2
072 7 $a PBW $2 bicssc
072 7 $a PSA $2 bicssc
072 7 $a MAT003000 $2 bisacsh
072 7 $a PSAX $2 thema
082 0 4 $a 519.22 $2 23
090 $a QA274.2 $b .G182 2024
100 1 $a Galves, A. $3 3733752
245 1 0 $a Probabilistic spiking neuronal nets $h [electronic resource] : $b neuromathematics for the computer era / $c by Antonio Galves, Eva Löcherbach, Christophe Pouzat.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2024.
300 $a xv, 199 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Lecture notes on mathematical modelling in the life sciences, $x 2193-4797
505 0 $a A Neurophysiology Primer for Mathematicians -- A Discrete Time Stochastic Neural Network Model -- Mean Field Limits for Discrete Time Stochastic Neural Network Models -- But Time is Continuous! -- Models without Reset: Hawkes Processes -- What is a Stationary State in a Potentially Infinite System? -- Statistical Estimation of the Interaction Graph -- Mean Field Limits and Short-Term Synaptic Facilitation in Continuous Time Models -- A Non-Exhaustive List of Some Open Questions -- Appendix A -- Appendix B -- Appendix C -- Appendix D -- Appendix E -- Appendix F -- References -- Index.
520 $a This book provides a self-contained introduction to a new class of stochastic models for systems of spiking neurons. These systems have a large number of interacting components, each one evolving as a stochastic process with a memory of variable length. Several mathematical tools are put to use, such as Markov chains, stochastic chains having memory of variable length, point processes having stochastic intensity, Hawkes processes, random graphs, mean field limits, perfect sampling algorithms, the Context algorithm, and statistical model selection. The book's focus on mathematically tractable objects distinguishes it from other texts on theoretical neuroscience. The biological complexity of neurons is not ignored, but reduced to some of its main features, such as the intrinsic randomness of neuronal dynamics. This reduction in complexity aims at explaining and reproducing statistical regularities and collective phenomena that are observed in experimental data, an approach that leads to mathematically rigorous results. With an emphasis on a constructive and algorithmic point of view, this book is directed towards mathematicians interested in learning about stochastic network models and their neurobiological underpinning, and neuroscientists interested in learning how to build and prove results with mathematical models that relate to actual experimental settings.
650 0 $a Stochastic models. $3 764002
650 0 $a Neural networks (Computer science) $3 532070
650 1 4 $a Mathematical and Computational Biology. $3 1566274
650 2 4 $a Probability Theory. $3 3538789
650 2 4 $a Stochastic Processes. $3 906873
650 2 4 $a Mathematical Statistics. $3 3538791
650 2 4 $a Neural Circuits. $3 3597311
700 1 $a Löcherbach, E. $3 3733753
700 1 $a Pouzat, Christophe. $3 3733754
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Lecture notes on mathematical modelling in the life sciences. $3 2056095
856 4 0 $u https://doi.org/10.1007/978-3-031-68409-8
950 $a Mathematics and Statistics (SpringerNature-11649)