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>