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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Probabilistic Spiking Neuronal Nets: Neuromathematics for the Computer Era

Probabilistic Spiking Neuronal Nets: Neuromathematics for the Computer Era

Galves, Antonio
Löcherbach, Eva
Pouzat, Christophe

Paperback

Series: Lecture Notes on Mathematical Modelling in the Life Sciences

CognitionGeneral MathematicsProbability & Statistics

ISBN10: 3031684087
ISBN13: 9783031684081
Publisher: Springer
Published: Oct 17 2024
Pages: 199
Weight: 0.68
Height: 0.46 Width: 6.14 Depth: 9.21
Language: English
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.

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