Galves A Probabilistic Spiking Neuronal Nets Neuromathematics Computer 2024
Category: eBooks
- views: 108
- date: 19 October 2024
- posted by: BeMyLove
Galves A Probabilistic Spiking Neuronal Nets Neuromathematics Computer 2024 | 5.96 MB
Title: Probabilistic Spiking Neuronal Nets
Author: Antonio Galves
Description:
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.
DOWNLOAD:
https://rapidgator.net/file/b0a2e19b1218577c62aa2972917a4c05/Galves_A._Probabilistic_Spiking_Neuronal_Nets._Neuromathematics...Computer..2024.rar
https://ddownload.com/zp6zvr84atbw/Galves_A._Probabilistic_Spiking_Neuronal_Nets._Neuromathematics...Computer..2024.rar
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.
DOWNLOAD:
https://rapidgator.net/file/b0a2e19b1218577c62aa2972917a4c05/Galves_A._Probabilistic_Spiking_Neuronal_Nets._Neuromathematics...Computer..2024.rar
https://ddownload.com/zp6zvr84atbw/Galves_A._Probabilistic_Spiking_Neuronal_Nets._Neuromathematics...Computer..2024.rar
We need your support!
Make a donation to help us stay online
Bitcoin (BTC)
bc1q08g9d22cxkawsjlf8etuek2pc9n2a3hs4cdrld
Bitcoin Cash (BCH)
qqvwexzhvgauxq2apgc4j0ewvcak6hh6lsnzmvtkem
Ethereum (ETH)
0xb55513D2c91A6e3c497621644ec99e206CDaf239
Litecoin (LTC)
ltc1qt6g2trfv9tjs4qj68sqc4uf0ukvc9jpnsyt59u
USDT (ERC20)
0xb55513D2c91A6e3c497621644ec99e206CDaf239
USDT (TRC20)
TYdPNrz7v1P9riWBWZ317oBgJueheGjATm
Information |
|||
 |
Users of GUESTS are not allowed to comment this publication. | ||