#pythontutorial
Gaussian Elimination in Python explained! Follow this detailed #PythonTutorial to conquer linear systems using practical code. #MatrixOperations #Coding
https://teguhteja.id/gaussian-elimination-from-scratch-tutorial/
I've added a new chapter on #VariationalAutoencoders (VAE), including an exercise that shows how to train a #VAE to predict behavior from neural data inputs.
We just completed a new course on #DimensionalityReduction in #Neuroscience, and the full teaching material 
is now freely available (CC BY 4.0 license):
https://www.fabriziomusacchio.com/blog/2024-10-24-dimensionality_reduction_in_neuroscience/
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.
Hello everyone.
In today's article, we examine the Python snytax structure in detail.
https://denizhalil.com/2024/10/19/python-syntax-guide-beginners/
The #BienenstockCooperMunro (#BCM) rule provides a comprehensive framework for understanding #SynapticPlasticity. Since its introduction in 1982, the #BCMrule has provided critical insights into the mechanisms of #learning and #memory formation. Here is a brief introduction to this rule along with a short #PythonTutorial:
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:
https://www.fabriziomusacchio.com/blog/2024-09-04-campbell_siegert_approximation/
Here’s a short #PythonTutorial on alpha-shaped post-synaptic currents, discussing their benefits and significance in #ComputationalNeuroscience:
https://www.fabriziomusacchio.com/blog/2024-08-04-alpha_shaped_input_currents/
The #NEST #simulator is a powerful software for simulating large-scale #SpikingNeuralNetworks (#SNN). I’ve composed an introductory #tutorial showing the main commands for getting started. It's applied to examples with single neurons to reduce complexity. Feel free to share:
https://www.fabriziomusacchio.com/blog/2024-06-16-nest_single_neuron_example/
Here is a #PythonTutorial on how to simulate the leaky #IntegrateAndFire model (#LIF), including an interactive #Juypter notebook to play around with 
:
https://www.fabriziomusacchio.com/blog/2023-07-03-integrate_and_fire_model/
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:
https://www.fabriziomusacchio.com/blog/2024-04-21-hodgkin_huxley_model/
Feel free to share and to experiment with the code.
From #VanDerPolOscillator to the #FitzHughNagumoModel: Explore the dynamics of a simplified #NeuronModel used to describe #ActionsPotential in neurons.
https://www.fabriziomusacchio.com/blog/2024-04-07-fitzhugh_nagumo_model/
So, my first attempt at a Python related tutorial. Your first Python project...
Here's the project brief; "Listen, I like numbers, I like guessing and I like games...do you think you could make me a number guessing game? Nice one, thanks." - Ben Goulding
Say no more, let's go.
https://fullstackprocrastinator.hashnode.dev/your-first-python-project