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/

#CompNeuro #neuroscience #PythonTutorial

The exponential #IntegrateAndFire model (#EIF) is a simplified neuronal model that captures the essential dynamics of #ActionPotential generation. The adaptive exponential Integrate-and-Fire model (#AdEx or #AIF) is a variant of the EIF, including an adaptation current to account for spike-frequency adaptation observed in real neurons. Here's a short tutorial, exploring the key features of the EIF and AdEx models and their applications in #CompNeuro:

🌍 https://www.fabriziomusacchio.com/blog/2024-08-25-EIF_and_AdEx_model/

This #tutorial explores the oscillatory #PopulationDynamics of generalized #IntegrateAndFire (GIF) neurons simulated with #NESTSimulator. The GIF #NeuronModel is a biophysically detailed model that captures the essential features of spiking neurons, including #SpikeFrequencyAdaptation and #DynamicThreshold behavior:

🌍 https://www.fabriziomusacchio.com/blog/2024-07-14-oscillating_gif_neuron_population/

#ComputationalNeuroscience #Python #Neuroscience

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/

#Neuroscience #ComputationalNeuroscience #CompNeuro #modeling #python