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#pythontutorial

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We just completed a new course on #DimensionalityReduction in #Neuroscience, and the full teaching material 🐍💻 is now freely available (CC BY 4.0 license):

🌍 fabriziomusacchio.com/blog/202

The course is designed to provide an introductory overview of the application of dimensionality reduction techniques for neuroscientists and data scientists alike, focusing on how to handle the increasingly high-dimensional datasets generated by modern neuroscience research.

The #CampbellSiegert approximation is a method used in #ComputationalNeuroscience to estimate the #firingrate of a #neuron given a certain input. This approximation is particularly useful for analyzing the firing behavior of neurons that follow a leaky #IntegrateAndFire (#LIF) model or similar models under the influence of stochastic input currents. Here is a short #tutorial that introduces the concept in more detail:

🌍 fabriziomusacchio.com/blog/202

An important step in #ComputationalNeuroscience 🧠💻 was the development of the #HodgkinHuxley model, for which Hodgkin and Huxley received the #NobelPrize in 1963. The model describes the dynamics of the #MembranePotential of a #neuron 🔬 by incorporating biophysiological properties. See here how it is derived, along with a simple implementation in #Python:

🌍 fabriziomusacchio.com/blog/202

Feel free to share and to experiment with the code.