Fabrizio Musacchio<p>The <a href="https://sigmoid.social/tags/CampbellSiegert" class="mention hashtag" rel="tag">#<span>CampbellSiegert</span></a> approximation is a method used in <a href="https://sigmoid.social/tags/ComputationalNeuroscience" class="mention hashtag" rel="tag">#<span>ComputationalNeuroscience</span></a> to estimate the <a href="https://sigmoid.social/tags/firingrate" class="mention hashtag" rel="tag">#<span>firingrate</span></a> of a <a href="https://sigmoid.social/tags/neuron" class="mention hashtag" rel="tag">#<span>neuron</span></a> given a certain input. This approximation is particularly useful for analyzing the firing behavior of neurons that follow a leaky <a href="https://sigmoid.social/tags/IntegrateAndFire" class="mention hashtag" rel="tag">#<span>IntegrateAndFire</span></a> (<a href="https://sigmoid.social/tags/LIF" class="mention hashtag" rel="tag">#<span>LIF</span></a>) model or similar models under the influence of stochastic input currents. Here is a short <a href="https://sigmoid.social/tags/tutorial" class="mention hashtag" rel="tag">#<span>tutorial</span></a> that introduces the concept in more detail:</p><p>🌍 <a href="https://www.fabriziomusacchio.com/blog/2024-09-04-campbell_siegert_approximation/" target="_blank" rel="nofollow noopener" translate="no"><span class="invisible">https://www.</span><span class="ellipsis">fabriziomusacchio.com/blog/202</span><span class="invisible">4-09-04-campbell_siegert_approximation/</span></a></p><p><a href="https://sigmoid.social/tags/CompNeuro" class="mention hashtag" rel="tag">#<span>CompNeuro</span></a> <a href="https://sigmoid.social/tags/neuroscience" class="mention hashtag" rel="tag">#<span>neuroscience</span></a> <a href="https://sigmoid.social/tags/PythonTutorial" class="mention hashtag" rel="tag">#<span>PythonTutorial</span></a></p>
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#IntegrateAndFire
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Fabrizio Musacchio<p>The exponential <a href="https://sigmoid.social/tags/IntegrateAndFire" class="mention hashtag" rel="tag">#<span>IntegrateAndFire</span></a> model (<a href="https://sigmoid.social/tags/EIF" class="mention hashtag" rel="tag">#<span>EIF</span></a>) is a simplified neuronal model that captures the essential dynamics of <a href="https://sigmoid.social/tags/ActionPotential" class="mention hashtag" rel="tag">#<span>ActionPotential</span></a> generation. The adaptive exponential Integrate-and-Fire model (<a href="https://sigmoid.social/tags/AdEx" class="mention hashtag" rel="tag">#<span>AdEx</span></a> or <a href="https://sigmoid.social/tags/AIF" class="mention hashtag" rel="tag">#<span>AIF</span></a>) 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 <a href="https://sigmoid.social/tags/CompNeuro" class="mention hashtag" rel="tag">#<span>CompNeuro</span></a>:</p><p>🌍 <a href="https://www.fabriziomusacchio.com/blog/2024-08-25-EIF_and_AdEx_model/" target="_blank" rel="nofollow noopener" translate="no"><span class="invisible">https://www.</span><span class="ellipsis">fabriziomusacchio.com/blog/202</span><span class="invisible">4-08-25-EIF_and_AdEx_model/</span></a></p>
Fabrizio Musacchio<p>This <a href="https://sigmoid.social/tags/tutorial" class="mention hashtag" rel="tag">#<span>tutorial</span></a> explores the oscillatory <a href="https://sigmoid.social/tags/PopulationDynamics" class="mention hashtag" rel="tag">#<span>PopulationDynamics</span></a> of generalized <a href="https://sigmoid.social/tags/IntegrateAndFire" class="mention hashtag" rel="tag">#<span>IntegrateAndFire</span></a> (GIF) neurons simulated with <a href="https://sigmoid.social/tags/NESTSimulator" class="mention hashtag" rel="tag">#<span>NESTSimulator</span></a>. The GIF <a href="https://sigmoid.social/tags/NeuronModel" class="mention hashtag" rel="tag">#<span>NeuronModel</span></a> is a biophysically detailed model that captures the essential features of spiking neurons, including <a href="https://sigmoid.social/tags/SpikeFrequencyAdaptation" class="mention hashtag" rel="tag">#<span>SpikeFrequencyAdaptation</span></a> and <a href="https://sigmoid.social/tags/DynamicThreshold" class="mention hashtag" rel="tag">#<span>DynamicThreshold</span></a> behavior:</p><p>🌍 <a href="https://www.fabriziomusacchio.com/blog/2024-07-14-oscillating_gif_neuron_population/" target="_blank" rel="nofollow noopener" translate="no"><span class="invisible">https://www.</span><span class="ellipsis">fabriziomusacchio.com/blog/202</span><span class="invisible">4-07-14-oscillating_gif_neuron_population/</span></a></p><p><a href="https://sigmoid.social/tags/ComputationalNeuroscience" class="mention hashtag" rel="tag">#<span>ComputationalNeuroscience</span></a> <a href="https://sigmoid.social/tags/Python" class="mention hashtag" rel="tag">#<span>Python</span></a> <a href="https://sigmoid.social/tags/Neuroscience" class="mention hashtag" rel="tag">#<span>Neuroscience</span></a></p>
Fabrizio Musacchio<p>Here is a <a href="https://sigmoid.social/tags/PythonTutorial" class="mention hashtag" rel="tag">#<span>PythonTutorial</span></a> 🐍 on how to simulate the leaky <a href="https://sigmoid.social/tags/IntegrateAndFire" class="mention hashtag" rel="tag">#<span>IntegrateAndFire</span></a> model (<a href="https://sigmoid.social/tags/LIF" class="mention hashtag" rel="tag">#<span>LIF</span></a>), including an interactive <a href="https://sigmoid.social/tags/Juypter" class="mention hashtag" rel="tag">#<span>Juypter</span></a> notebook to play around with ✌️:</p><p>🌍 <a href="https://www.fabriziomusacchio.com/blog/2023-07-03-integrate_and_fire_model/" target="_blank" rel="nofollow noopener" translate="no"><span class="invisible">https://www.</span><span class="ellipsis">fabriziomusacchio.com/blog/202</span><span class="invisible">3-07-03-integrate_and_fire_model/</span></a></p><p><a href="https://sigmoid.social/tags/Neuroscience" class="mention hashtag" rel="tag">#<span>Neuroscience</span></a> <a href="https://sigmoid.social/tags/ComputationalNeuroscience" class="mention hashtag" rel="tag">#<span>ComputationalNeuroscience</span></a> <a href="https://sigmoid.social/tags/CompNeuro" class="mention hashtag" rel="tag">#<span>CompNeuro</span></a> <a href="https://sigmoid.social/tags/modeling" class="mention hashtag" rel="tag">#<span>modeling</span></a> <a href="https://sigmoid.social/tags/python" class="mention hashtag" rel="tag">#<span>python</span></a></p>
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