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#pythontutorial

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Fabrizio Musacchio<p>I've added a new chapter on <a href="https://sigmoid.social/tags/VariationalAutoencoders" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>VariationalAutoencoders</span></a> (VAE), including an exercise that shows how to train a <a href="https://sigmoid.social/tags/VAE" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>VAE</span></a> to predict behavior from neural data inputs.</p><p><a href="https://sigmoid.social/tags/CompNeuro" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>CompNeuro</span></a> <a href="https://sigmoid.social/tags/Neuroscience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Neuroscience</span></a> <a href="https://sigmoid.social/tags/PythonTutorial" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>PythonTutorial</span></a></p>
Fabrizio Musacchio<p>We just completed a new course on <a href="https://sigmoid.social/tags/DimensionalityReduction" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DimensionalityReduction</span></a> in <a href="https://sigmoid.social/tags/Neuroscience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Neuroscience</span></a>, and the full teaching material 🐍💻 is now freely available (CC BY 4.0 license):</p><p>🌍 <a href="https://www.fabriziomusacchio.com/blog/2024-10-24-dimensionality_reduction_in_neuroscience/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">fabriziomusacchio.com/blog/202</span><span class="invisible">4-10-24-dimensionality_reduction_in_neuroscience/</span></a></p><p>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.</p><p><a href="https://sigmoid.social/tags/PythonTutorial" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>PythonTutorial</span></a> <a href="https://sigmoid.social/tags/CompNeuro" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>CompNeuro</span></a></p>
Fabrizio Musacchio<p>The <a href="https://sigmoid.social/tags/BienenstockCooperMunro" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>BienenstockCooperMunro</span></a> (<a href="https://sigmoid.social/tags/BCM" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>BCM</span></a>) rule provides a comprehensive framework for understanding <a href="https://sigmoid.social/tags/SynapticPlasticity" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>SynapticPlasticity</span></a>. Since its introduction in 1982, the <a href="https://sigmoid.social/tags/BCMrule" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>BCMrule</span></a> has provided critical insights into the mechanisms of <a href="https://sigmoid.social/tags/learning" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>learning</span></a> and <a href="https://sigmoid.social/tags/memory" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>memory</span></a> formation. Here is a brief introduction to this rule along with a short <a href="https://sigmoid.social/tags/PythonTutorial" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>PythonTutorial</span></a>:</p><p>🌍 <a href="https://www.fabriziomusacchio.com/blog/2024-09-08-bcm_rule/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">fabriziomusacchio.com/blog/202</span><span class="invisible">4-09-08-bcm_rule/</span></a></p><p><a href="https://sigmoid.social/tags/CompNeuro" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>CompNeuro</span></a> <a href="https://sigmoid.social/tags/ComputationalNeuroscience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ComputationalNeuroscience</span></a> <a href="https://sigmoid.social/tags/Neuroscience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Neuroscience</span></a></p>
Fabrizio Musacchio<p>The <a href="https://sigmoid.social/tags/CampbellSiegert" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>CampbellSiegert</span></a> approximation is a method used in <a href="https://sigmoid.social/tags/ComputationalNeuroscience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ComputationalNeuroscience</span></a> to estimate the <a href="https://sigmoid.social/tags/firingrate" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>firingrate</span></a> of a <a href="https://sigmoid.social/tags/neuron" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<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="nofollow noopener noreferrer" target="_blank">#<span>IntegrateAndFire</span></a> (<a href="https://sigmoid.social/tags/LIF" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<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="nofollow noopener noreferrer" target="_blank">#<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/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><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="nofollow noopener noreferrer" target="_blank">#<span>CompNeuro</span></a> <a href="https://sigmoid.social/tags/neuroscience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>neuroscience</span></a> <a href="https://sigmoid.social/tags/PythonTutorial" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>PythonTutorial</span></a></p>
Fabrizio Musacchio<p>Here’s a short <a href="https://sigmoid.social/tags/PythonTutorial" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>PythonTutorial</span></a> on alpha-shaped post-synaptic currents, discussing their benefits and significance in <a href="https://sigmoid.social/tags/ComputationalNeuroscience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ComputationalNeuroscience</span></a>:</p><p>🌍 <a href="https://www.fabriziomusacchio.com/blog/2024-08-04-alpha_shaped_input_currents/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">fabriziomusacchio.com/blog/202</span><span class="invisible">4-08-04-alpha_shaped_input_currents/</span></a></p><p><a href="https://sigmoid.social/tags/CompNeuro" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>CompNeuro</span></a> <a href="https://sigmoid.social/tags/Neuroscience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Neuroscience</span></a></p>
Fabrizio Musacchio<p>The <a href="https://sigmoid.social/tags/NEST" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>NEST</span></a> <a href="https://sigmoid.social/tags/simulator" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>simulator</span></a> is a powerful software for simulating large-scale <a href="https://sigmoid.social/tags/SpikingNeuralNetworks" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>SpikingNeuralNetworks</span></a> (<a href="https://sigmoid.social/tags/SNN" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>SNN</span></a>). I’ve composed an introductory <a href="https://sigmoid.social/tags/tutorial" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>tutorial</span></a> showing the main commands for getting started. It's applied to examples with single neurons to reduce complexity. Feel free to share:</p><p>🌍 <a href="https://www.fabriziomusacchio.com/blog/2024-06-16-nest_single_neuron_example/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">fabriziomusacchio.com/blog/202</span><span class="invisible">4-06-16-nest_single_neuron_example/</span></a></p><p><a href="https://sigmoid.social/tags/CompNeuro" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>CompNeuro</span></a> <a href="https://sigmoid.social/tags/ComputationalNeuroscience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ComputationalNeuroscience</span></a> <a href="https://sigmoid.social/tags/Python" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Python</span></a> <a href="https://sigmoid.social/tags/PythonTutorial" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>PythonTutorial</span></a> <a href="https://sigmoid.social/tags/NESTSimulator" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>NESTSimulator</span></a></p>
Fabrizio Musacchio<p>Here is a <a href="https://sigmoid.social/tags/PythonTutorial" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>PythonTutorial</span></a> 🐍 on how to simulate the leaky <a href="https://sigmoid.social/tags/IntegrateAndFire" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>IntegrateAndFire</span></a> model (<a href="https://sigmoid.social/tags/LIF" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>LIF</span></a>), including an interactive <a href="https://sigmoid.social/tags/Juypter" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<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/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><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="nofollow noopener noreferrer" target="_blank">#<span>Neuroscience</span></a> <a href="https://sigmoid.social/tags/ComputationalNeuroscience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ComputationalNeuroscience</span></a> <a href="https://sigmoid.social/tags/CompNeuro" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>CompNeuro</span></a> <a href="https://sigmoid.social/tags/modeling" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>modeling</span></a> <a href="https://sigmoid.social/tags/python" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>python</span></a></p>
Fabrizio Musacchio<p>An important step in <a href="https://sigmoid.social/tags/ComputationalNeuroscience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ComputationalNeuroscience</span></a> 🧠💻 was the development of the <a href="https://sigmoid.social/tags/HodgkinHuxley" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>HodgkinHuxley</span></a> model, for which Hodgkin and Huxley received the <a href="https://sigmoid.social/tags/NobelPrize" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>NobelPrize</span></a> in 1963. The model describes the dynamics of the <a href="https://sigmoid.social/tags/MembranePotential" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>MembranePotential</span></a> of a <a href="https://sigmoid.social/tags/neuron" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>neuron</span></a> 🔬 by incorporating biophysiological properties. See here how it is derived, along with a simple implementation in <a href="https://sigmoid.social/tags/Python" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Python</span></a>: </p><p>🌍 <a href="https://www.fabriziomusacchio.com/blog/2024-04-21-hodgkin_huxley_model/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">fabriziomusacchio.com/blog/202</span><span class="invisible">4-04-21-hodgkin_huxley_model/</span></a></p><p>Feel free to share and to experiment with the code.</p><p><a href="https://sigmoid.social/tags/CompNeuro" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>CompNeuro</span></a> <a href="https://sigmoid.social/tags/PythonTutorial" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>PythonTutorial</span></a> <a href="https://sigmoid.social/tags/NeuralDynamics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>NeuralDynamics</span></a> <a href="https://sigmoid.social/tags/DynamicalSystem" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DynamicalSystem</span></a></p>
Fabrizio Musacchio<p>From <a href="https://sigmoid.social/tags/VanDerPolOscillator" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>VanDerPolOscillator</span></a> to the <a href="https://sigmoid.social/tags/FitzHughNagumoModel" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>FitzHughNagumoModel</span></a>: Explore the dynamics of a simplified <a href="https://sigmoid.social/tags/NeuronModel" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>NeuronModel</span></a> used to describe <a href="https://sigmoid.social/tags/ActionsPotential" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>ActionsPotential</span></a> in neurons.</p><p>🌍 <a href="https://www.fabriziomusacchio.com/blog/2024-04-07-fitzhugh_nagumo_model/" rel="nofollow noopener noreferrer" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">fabriziomusacchio.com/blog/202</span><span class="invisible">4-04-07-fitzhugh_nagumo_model/</span></a></p><p><a href="https://sigmoid.social/tags/CompNeuro" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>CompNeuro</span></a> <a href="https://sigmoid.social/tags/computationalneuroscience" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>computationalneuroscience</span></a> <a href="https://sigmoid.social/tags/computationalmodeling" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>computationalmodeling</span></a> <a href="https://sigmoid.social/tags/PythonTutorial" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>PythonTutorial</span></a> <a href="https://sigmoid.social/tags/PhasePlaneAnalysis" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>PhasePlaneAnalysis</span></a></p>
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