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D. Holcman's class. This is a continuity of the previous class

Lecture 6B Modeling Neuronal Network using synaptic property

D. Holcman Lecture on basic signal processing for time series. 1-Fourrier transform on sliding window. 2-Empirical Mode Decomposition: some examples and justification.

D. Holcman lecture 2024 Ref. U Dobramysl, D Holcman, Computational methods and diffusion theory in triangulation sensing to model neuronal navigation, Reports on Progress in Physics 85 (10), 104601 2022

D. Holcman class 2024 A-Part I : Modeling and asymptotics for data analysis Stochastic processes, Ornstein-Ulhembeck, Fokker-Planck equation, MFPT. Extreme Statistics and redundancy principle to study rare events :asymptotics, path-integral and accelerated and hybrid stochastic simulations Model of electro-diffusion, application Spectral decomposition, Spectrogram, parameter estimation from emp...

Holcman class 2024: construction of the mapping Hybrid Stochastic-analytic Algorithm to accelerate Brownian simulations. References: Computational methods and diffusion theory in triangulation sensing to model neuronal navigation U Dobramysl, D Holcman Reports on Progress in Physics 85 (10), 104601

Holcman class 2024 Hybrid simulations References: 1-Mixed analytical-stochastic simulation method for the recovery of a Brownian gradient source from probability fluxes to small windows U Dobramysl, D Holcman Journal of computational physics 355, 22-36 2-U Dobramysl, D Holcman, Reconstructing a point source from diffusion fluxes to narrow windows in three dimensions, Proceedings of the Royal So...

Holcman class 2024 Neuronal navigation computation

Lecture 4A: Modeling brain wiring and neuronal navigation References: U Dobramysl, D Holcman, Computational methods and diffusion theory in triangulation sensing to model neuronal navigation Reports on Progress in Physics 85 (10), 104601 1-J Reingruber, D Holcman, Computational and mathematical methods for morphogenetic gradient analysis, boundary formation and axonal targeting Seminars in cell...

See also reference: Asymptotics of Elliptic and Parabolic PDEs and their Applications in Statistical Physics, Computational Neuroscience, and Biophysics D Holcman, Z Schuss - Springer This is a monograph on the emerging branch of mathematical biophysics combining asymptotic analysis with numerical and stochastic methods to analyze partial differential …

Description of some examples. See references: - K Reynaud, Z Schuss, N Rouach, D Holcman, Why so many sperm cells? Communicative & integrative biology 8 (3), e1017156 -K Basnayake, D Holcman,Fastest among equals: a novel paradigm in biology. reply to comments: Redundancy principle and the role of extreme statistics in molecular and cellular biology Physics of life reviews 28, 96-99 -K Dao Duc, ...

Holcman's class 2024

Holcman class 2024

Part II/III Summary: I describe here our approach involves reconstructing functional astrocytic networks by organizing individual astrocyte activation events chronologically. This chronological order creates activity paths that enable the extraction of local astrocyte functional correlations. Ultimately, by tallying the occurrences of direct co-activations between pairs of cells along these pat...

Astrocytic Functional Connectivity Network Reconstruction: Background (Part I/III)D

Data-convergence applied to Graph Reconstruction PartIII/III

Predicting the Brain, what’s next Part II

Predicting the Brain, what’s next part1

SPTanalysis (Imaj Fiji Software) to analyse Single Particule Trajectories

Predicting Algorithms, IA for controlling the depth of anesthesia

Algorithmes temps-reel, intelligence artificielle pour optimisee le monitorage de l'anesthesie

Degoudroner les rues des grandes villes

Computational methods and diffusion theory in triangulation sensing

High-throughput super-resolution single particle trajectory and ImageJ plugin

Campagne presidentielle 2022 et la recherche scientifique?

Triangulation sensing (part IV/IV): reconstructing a source position and sensitivity from gradients

Matched asymptotic expansions: application Laplace's equation

Triangulation sensing Part III/IV: How does a cell identify a gradient source in dim2.

Morphogenetic gradient-PartIIb/IV-: principles of making boundaries: