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#Izhikevichmodel

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Fabrizio Musacchio<p>Due to its computational efficiency and biological plausibility, the <a href="https://sigmoid.social/tags/IzhikevichModel" class="mention hashtag" rel="tag">#<span>IzhikevichModel</span></a> is an exceptional tool for understanding <a href="https://sigmoid.social/tags/neuronal" class="mention hashtag" rel="tag">#<span>neuronal</span></a> interactions within <a href="https://sigmoid.social/tags/SpikingNeuralNetworks" class="mention hashtag" rel="tag">#<span>SpikingNeuralNetworks</span></a> (<a href="https://sigmoid.social/tags/SNN" class="mention hashtag" rel="tag">#<span>SNN</span></a>). Here’s a quick <a href="https://sigmoid.social/tags/Python" class="mention hashtag" rel="tag">#<span>Python</span></a> implementation of Izhikevich&#39;s original <a href="https://sigmoid.social/tags/Matlab" class="mention hashtag" rel="tag">#<span>Matlab</span></a> code along with examples using different synaptic weights and neuron types, each leading to diverse spiking behaviors and network dynamics:</p><p>🌍<a href="https://www.fabriziomusacchio.com/posts/izhikevich_network_model/" target="_blank" rel="nofollow noopener" translate="no"><span class="invisible">https://www.</span><span class="ellipsis">fabriziomusacchio.com/posts/iz</span><span class="invisible">hikevich_network_model/</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/ComputationalScience" class="mention hashtag" rel="tag">#<span>ComputationalScience</span></a> <a href="https://sigmoid.social/tags/NeuralNetworks" class="mention hashtag" rel="tag">#<span>NeuralNetworks</span></a> <a href="https://sigmoid.social/tags/modeling" class="mention hashtag" rel="tag">#<span>modeling</span></a></p>
Fabrizio Musacchio<p>The <a href="https://sigmoid.social/tags/Izhikevichmodel" class="mention hashtag" rel="tag">#<span>Izhikevichmodel</span></a> is a powerful tool for simulating the <a href="https://sigmoid.social/tags/spiking" class="mention hashtag" rel="tag">#<span>spiking</span></a> and bursting behavior of <a href="https://sigmoid.social/tags/neurons" class="mention hashtag" rel="tag">#<span>neurons</span></a> with a remarkable balance between biological relevance and computational efficiency 💫 Here is a short introduction along with a <a href="https://sigmoid.social/tags/Python" class="mention hashtag" rel="tag">#<span>Python</span></a> implementation to simulate various types of <a href="https://sigmoid.social/tags/cortical" class="mention hashtag" rel="tag">#<span>cortical</span></a> neurons, including regular spiking, fast spiking, and bursting neurons:</p><p>🌍 <a href="https://www.fabriziomusacchio.com/blog/2024-04-29-izhikevich_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">4-04-29-izhikevich_model/</span></a></p><p>Feel free to share and experiment with it ☺️</p><p><a href="https://sigmoid.social/tags/CompNeuro" class="mention hashtag" rel="tag">#<span>CompNeuro</span></a> <a href="https://sigmoid.social/tags/ComputationalModelling" class="mention hashtag" rel="tag">#<span>ComputationalModelling</span></a> <a href="https://sigmoid.social/tags/ComputationalNeuroscience" class="mention hashtag" rel="tag">#<span>ComputationalNeuroscience</span></a></p>
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