JiTCODE (just-in-time compilation for ordinary differential equations) is an extension of SciPy’s ODE (scipy.integrate.ode) or Solve IVP (scipy.integrate.solve_ivp). Where the latter take a Python function as an argument, JiTCODE takes an iterable (or generator function or dictionary) of symbolic expressions, which it translates to C code, compiles on the fly, and uses as the function to feed into SciPy’s ODE or Solve IVP. Symbolic expressions are mostly handled by SymEngine, SymPy’s compiled-backend-to-be (see SymPy vs. SymEngine for details).

https://jitcode.readthedocs.io/en/v1.6.0/#

JiTCDDE (just-in-time compilation for delay differential equations) is a standalone Python implementation of the DDE integration method proposed by Shampine and Thompson [ST01], which in turn employs the Bogacki–Shampine Runge–Kutta pair [BS89]. JiTCDDE is designed in analogy to JiTCODE (which is handled very similarly to SciPy’s ODE (scipy.integrate.ode)): It takes an iterable (or generator function or dictionary) of symbolic expressions, translates them to C code, compiles them and an integrator wrapped around them on the fly, and allows you to operate this integrator from Python. Symbolic expressions are mostly handled by SymEngine, SymPy’s compiled-backend-to-be (see SymPy vs. SymEngine for details).
https://jitcdde.readthedocs.io/en/stable/

Visual introduction to optimization in Deep Learning, ranging from 1st-order methods, 2nd-order and Natural Gradient (and approximations of it such as K-FAC). Sharing the PDF (easier to download): https://t.co/pGP3mLNElt https://t.co/cs3SdQ4TNm

JiTCSDE (just-in-time compilation for stochastic differential equations) is a standalone Python implementation of the adaptive integration method proposed by Rackauckas and Nie [RN17], which in turn employs Rößler-type stochastic Runge–Kutta methods [R10]. It can handle both Itō and Stratonovich SDEs, converting the latter internally. JiTCSDE is designed in analogy to JiTCODE (which is handled very similarly to SciPy’s ODE (scipy.integrate.ode)): It takes iterables (or generator functions or dictionaries) of symbolic expressions, translates them to C code, compiles them and an integrator wrapped around them on the fly, and allows you to operate this integrator from Python. Symbolic expressions are mostly handled by SymEngine, SymPy’s compiled-backend-to-be (see SymPy vs. SymEngine for details).
https://jitcsde.readthedocs.io/en/v1.6.0/

cmake simple tutorial (youtube)
cmake-cookbook (github)

#cmake

HEADER FILES only C++ library for analysis of neurophysiological and simulated data.

https://github.com/Ziaeemehr/neuro_toolbox

Any Feedback is welcomed.
#new_project
#neuro_toolbox

#neuro_toolbox #documentation
Online documents

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Machine Learning & Deep Learning in Python & R

Brainhack New York 2020
https://brainhack-ny.github.io/

Neuromatch Tutorials
Link to google drive (51 PDF files)

Simply #parallel jobs with #bash script

#Kuramoto model various implementation using #Python and #jitcode:
GitHub

#bash scripting
GitHub

#BrainPy is a lightweight framework based on the latest Just-In-Time (JIT) compilers. The goal of BrainPy is to provide a unified simulation and analysis framework for neuronal dynamics with the feature of high flexibility and efficiency.

https://brainpy.readthedocs.io/en/latest/

BrainPy-Models is based on BrainPy neuronal dynamics simulation framework. Here you can find neurons, synapses models and topological networks implemented with BrainPy.

https://brainpy-models.readthedocs.io/en/latest/