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Fabrizio MusacchioToday, we had a great <a href="https://sigmoid.social/tags/iBehave" class="mention hashtag" rel="nofollow noopener" target="_blank">#iBehave</a> lecture by Yaniv Ziv at the <a href="https://sigmoid.social/tags/DZNE" class="mention hashtag" rel="nofollow noopener" target="_blank">#DZNE</a>. He presented his work on <a href="https://sigmoid.social/tags/RepresentationalDrift" class="mention hashtag" rel="nofollow noopener" target="_blank">#RepresentationalDrift</a> in the <a href="https://sigmoid.social/tags/hippocampus" class="mention hashtag" rel="nofollow noopener" target="_blank">#hippocampus</a> and entorhinal <a href="https://sigmoid.social/tags/cortex" class="mention hashtag" rel="nofollow noopener" target="_blank">#cortex</a>, studying <a href="https://sigmoid.social/tags/PopulationDynamics" class="mention hashtag" rel="nofollow noopener" target="_blank">#PopulationDynamics</a> of freely moving mice 💪 Great insights into how neuronal codes evolve across time! <a href="https://sigmoid.social/tags/neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#neuroscience</a> 🧪 <a href="https://sigmoid.social/tags/CompNeuro" class="mention hashtag" rel="nofollow noopener" target="_blank">#CompNeuro</a>

Fabrizio Musacchio🧠 New preprint by Ruff, Markman, Kim & Cohen (2025): <a href="https://sigmoid.social/tags/NeuralPopulation" class="mention hashtag" rel="nofollow noopener" target="_blank">#NeuralPopulation</a> formatting matters for function. In monkeys combining motion and reward, both middle temporal area (<a href="https://sigmoid.social/tags/MT" class="mention hashtag" rel="nofollow noopener" target="_blank">#MT</a>) & dorsolateral prefrontal <a href="https://sigmoid.social/tags/cortex" class="mention hashtag" rel="nofollow noopener" target="_blank">#cortex</a> (<a href="https://sigmoid.social/tags/dlPFC" class="mention hashtag" rel="nofollow noopener" target="_blank">#dlPFC</a>) encode both signals. But MT formats them separately, dlPFC integrates them. A recurrent <a href="https://sigmoid.social/tags/RNN" class="mention hashtag" rel="nofollow noopener" target="_blank">#RNN</a> model predicted, and microstimulation confirmed, distinct <a href="https://sigmoid.social/tags/behavioral" class="mention hashtag" rel="nofollow noopener" target="_blank">#behavioral</a> impacts.🌍 <a href="https://www.biorxiv.org/content/10.1101/2025.01.03.631242v1" rel="nofollow noopener" translate="no" target="_blank">https://www.biorxiv.org/content/10.1101/2025.01.03.631242v1</a><a href="https://sigmoid.social/tags/Neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#Neuroscience</a> <a href="https://sigmoid.social/tags/CompNeuro" class="mention hashtag" rel="nofollow noopener" target="_blank">#CompNeuro</a> <a href="https://sigmoid.social/tags/DecisionMaking" class="mention hashtag" rel="nofollow noopener" target="_blank">#DecisionMaking</a> <a href="https://sigmoid.social/tags/NeuralCoding" class="mention hashtag" rel="nofollow noopener" target="_blank">#NeuralCoding</a>

Dan GoodmanIs anarchist science possible?As an experiment, we got together a large group of computational neuroscientists from around the world to work on a single project without top down direction. Read on to find out what happened.The project started as a tutorial on a new technique at the <a href="https://neuromatch.social/@CosyneMeeting" class="u-url mention" rel="nofollow noopener" target="_blank">@CosyneMeeting</a> 2022. We realised that the technique was easy and cheap for anyone to use with a lot of low hanging fruit.<a href="https://neural-reckoning.github.io/cosyne-tutorial-2022/" rel="nofollow noopener" translate="no" target="_blank">https://neural-reckoning.github.io/cosyne-tutorial-2022/</a>At the tutorial we announced a 1-2 year open research project that anyone could join, starting from the materials of the tutorial, and a few starting questions we found interesting, but with no other constraints. We were inspired by the Polymath Project in mathematics.<a href="https://en.wikipedia.org/wiki/Polymath_Project" rel="nofollow noopener" translate="no" target="_blank">https://en.wikipedia.org/wiki/Polymath_Project</a>31 people contributed to the project, joining for monthly meetings to discuss progress. All code was publicly available throughout, and when we started writing up the work in progress was also fully public. You can see that version here: <a href="https://comob-project.github.io/snn-sound-localization/paper" rel="nofollow noopener" translate="no" target="_blank">https://comob-project.github.io/snn-sound-localization/paper</a>Not everyone who was involved made it to the paper (didn't respond or couldn't find contact details), and not all are on Mastodon, but authors include: <a href="https://neuromatch.social/@marcusghosh" class="u-url mention" rel="nofollow noopener" target="_blank">@marcusghosh</a> <a href="https://fediscience.org/@TomasFiers" class="u-url mention" rel="nofollow noopener" target="_blank">@TomasFiers</a> <a href="https://neuromatch.social/@GabrielBena" class="u-url mention" rel="nofollow noopener" target="_blank">@GabrielBena</a> <a href="https://sigmoid.social/@rory" class="u-url mention" rel="nofollow noopener" target="_blank">@rory</a> We used GitHub and Jupyter notebooks to coordinate development, with a website showing everyone's current code and results to make collaboration easier. We used <a href="https://fosstodon.org/@mystmarkdown" class="u-url mention" rel="nofollow noopener" target="_blank">@mystmarkdown</a> and GitHub actions to automate this.<a href="https://comob-project.github.io/snn-sound-localization/" rel="nofollow noopener" translate="no" target="_blank">https://comob-project.github.io/snn-sound-localization/</a>So how did it work out? Well, some things went well and others not so well. We published a paper with our results and reflections on the process. If you're interested in spiking neural networks, sound localisation, or anarchist science, check it out:<a href="https://www.eneuro.org/content/12/7/ENEURO.0383-24.2025" rel="nofollow noopener" translate="no" target="_blank">https://www.eneuro.org/content/12/7/ENEURO.0383-24.2025</a>Generally, the infrastructure we built worked well, as did the monthly meetings. Starting from the tutorial was a good decision because it gave everyone a common reference and meant they could easily get started.However, the lack of direction meant that we didn't achieve very coherent results in the end. We don't think this is a catastrophic problem, but when we try again, this is something we'd like to address. If you have thoughts or would like to be involved, get in touch!Ultimately, we didn't achieve a scientific breakthrough in this project, but we did show that without top down direction or any specific funding, we could organise a large group of scientists to work together and publish their research in a good journal. We think that's a hopeful sign for the future!<a href="https://neuromatch.social/tags/neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#neuroscience</a> <a href="https://neuromatch.social/tags/computationalneuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#computationalneuroscience</a> <a href="https://neuromatch.social/tags/compneuro" class="mention hashtag" rel="nofollow noopener" target="_blank">#compneuro</a> <a href="https://neuromatch.social/tags/anarchism" class="mention hashtag" rel="nofollow noopener" target="_blank">#anarchism</a> <a href="https://neuromatch.social/tags/science" class="mention hashtag" rel="nofollow noopener" target="_blank">#science</a>

Dirk GütlinBrain–computer interface control with artificial intelligence copilots <a class="hashtag" rel="nofollow noopener" href="https://bsky.app/search?q=%23MLSky" target="_blank">#MLSky</a> <a class="hashtag" rel="nofollow noopener" href="https://bsky.app/search?q=%23CompNeuro" target="_blank">#CompNeuro</a> <a class="hashtag" rel="nofollow noopener" href="https://bsky.app/search?q=%23NeuroAI" target="_blank">#NeuroAI</a> <a href="https://www.nature.com/articles/s42256-025-01090-y" rel="nofollow noopener" target="_blank">www.nature.com/articles/s42...</a> <a href="https://www.nature.com/articles/s42256-025-01090-y" rel="nofollow noopener" target="_blank">Brain–computer interface contr...</a>

Fabrizio Musacchio🧠 New comprehensive review on <a href="https://sigmoid.social/tags/LowDimensional" class="mention hashtag" rel="nofollow noopener" target="_blank">#LowDimensional</a> <a href="https://sigmoid.social/tags/embeddings" class="mention hashtag" rel="nofollow noopener" target="_blank">#embeddings</a> of <a href="https://sigmoid.social/tags/HighDimensional" class="mention hashtag" rel="nofollow noopener" target="_blank">#HighDimensional</a> data. Discusses how <a href="https://sigmoid.social/tags/dimensionalityreduction" class="mention hashtag" rel="nofollow noopener" target="_blank">#dimensionalityreduction</a> helps visualizing, exploring, and <a href="https://sigmoid.social/tags/modeling" class="mention hashtag" rel="nofollow noopener" target="_blank">#modeling</a> <a href="https://sigmoid.social/tags/ComplexSystems" class="mention hashtag" rel="nofollow noopener" target="_blank">#ComplexSystems</a>. From <a href="https://sigmoid.social/tags/PCA" class="mention hashtag" rel="nofollow noopener" target="_blank">#PCA</a> to <a href="https://sigmoid.social/tags/tSNE" class="mention hashtag" rel="nofollow noopener" target="_blank">#tSNE</a>, <a href="https://sigmoid.social/tags/UMAP" class="mention hashtag" rel="nofollow noopener" target="_blank">#UMAP</a> & <a href="https://sigmoid.social/tags/NeuralNetworks" class="mention hashtag" rel="nofollow noopener" target="_blank">#NeuralNetworks</a>: Excellent overview paper👌🌍 <a href="https://arxiv.org/abs/2508.15929" rel="nofollow noopener" translate="no" target="_blank">https://arxiv.org/abs/2508.15929</a><a href="https://sigmoid.social/tags/CompNeuro" class="mention hashtag" rel="nofollow noopener" target="_blank">#CompNeuro</a> <a href="https://sigmoid.social/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#MachineLearning</a> <a href="https://sigmoid.social/tags/DataVisualization" class="mention hashtag" rel="nofollow noopener" target="_blank">#DataVisualization</a>

Fabrizio Musacchio🧠 New <a href="https://sigmoid.social/tags/preprint" class="mention hashtag" rel="nofollow noopener" target="_blank">#preprint</a> by Hosseini et al. (2025) from Graziana Gatto’s lab: <a href="https://sigmoid.social/tags/AutoGaitA" class="mention hashtag" rel="nofollow noopener" target="_blank">#AutoGaitA</a>, a <a href="https://sigmoid.social/tags/Python" class="mention hashtag" rel="nofollow noopener" target="_blank">#Python</a> toolbox for universal <a href="https://sigmoid.social/tags/kinematic" class="mention hashtag" rel="nofollow noopener" target="_blank">#kinematic</a> analysis of <a href="https://sigmoid.social/tags/motor" class="mention hashtag" rel="nofollow noopener" target="_blank">#motor</a> programs across species, perturbations and <a href="https://sigmoid.social/tags/behaviours" class="mention hashtag" rel="nofollow noopener" target="_blank">#behaviours</a>. Flies, mice and humans all show age-dependent loss of propulsive strength 🚶‍♀️A promising tool 💪 for cross-species motor program analysis in my view:🌍 <a href="https://doi.org/10.1101/2024.04.14.589409" rel="nofollow noopener" translate="no" target="_blank">https://doi.org/10.1101/2024.04.14.589409</a><a href="https://sigmoid.social/tags/Neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#Neuroscience</a> <a href="https://sigmoid.social/tags/MotorControl" class="mention hashtag" rel="nofollow noopener" target="_blank">#MotorControl</a> <a href="https://sigmoid.social/tags/CompNeuro" class="mention hashtag" rel="nofollow noopener" target="_blank">#CompNeuro</a> <a href="https://sigmoid.social/tags/OpenSource" class="mention hashtag" rel="nofollow noopener" target="_blank">#OpenSource</a>

Fabrizio MusacchioI recently played around with <a href="https://sigmoid.social/tags/RateModels" class="mention hashtag" rel="nofollow noopener" target="_blank">#RateModels</a> using <a href="https://sigmoid.social/tags/NESTsimulator" class="mention hashtag" rel="nofollow noopener" target="_blank">#NESTsimulator</a>. Compared to <a href="https://sigmoid.social/tags/SNN" class="mention hashtag" rel="nofollow noopener" target="_blank">#SNN</a>, RM focus on average firing rates of <a href="https://sigmoid.social/tags/NeuronPopulations" class="mention hashtag" rel="nofollow noopener" target="_blank">#NeuronPopulations</a>, simplifying analysis of large networks. They effectively capture collective dynamics like <a href="https://sigmoid.social/tags/oscillations" class="mention hashtag" rel="nofollow noopener" target="_blank">#oscillations</a> and <a href="https://sigmoid.social/tags/synchronization" class="mention hashtag" rel="nofollow noopener" target="_blank">#synchronization</a>, though they miss precise spike timing details. Thus, both approaches have their merits. Here is a brief overview:🌍 <a href="https://www.fabriziomusacchio.com/blog/2025-08-28-rate_models/" rel="nofollow noopener" translate="no" target="_blank">https://www.fabriziomusacchio.com/blog/2025-08-28-rate_models/</a><a href="https://sigmoid.social/tags/CompNeuro" class="mention hashtag" rel="nofollow noopener" target="_blank">#CompNeuro</a> <a href="https://sigmoid.social/tags/Neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#Neuroscience</a> <a href="https://sigmoid.social/tags/Python" class="mention hashtag" rel="nofollow noopener" target="_blank">#Python</a> <a href="https://sigmoid.social/tags/PythonTutorial" class="mention hashtag" rel="nofollow noopener" target="_blank">#PythonTutorial</a> <a href="https://sigmoid.social/tags/SpikingNeuralNetwork" class="mention hashtag" rel="nofollow noopener" target="_blank">#SpikingNeuralNetwork</a>

Fabrizio Musacchio📝 New blog post: <a href="https://sigmoid.social/tags/GapJunctions" class="mention hashtag" rel="nofollow noopener" target="_blank">#GapJunctions</a> (<a href="https://sigmoid.social/tags/ElectricalSynapses" class="mention hashtag" rel="nofollow noopener" target="_blank">#ElectricalSynapses</a>) enable direct electrical and chemical communication between <a href="https://sigmoid.social/tags/neurons" class="mention hashtag" rel="nofollow noopener" target="_blank">#neurons</a>, synchronizing activity and supporting rapid signal propagation. Their <a href="https://sigmoid.social/tags/modeling" class="mention hashtag" rel="nofollow noopener" target="_blank">#modeling</a> is crucial for understanding <a href="https://sigmoid.social/tags/NeuralNetworkDynamics" class="mention hashtag" rel="nofollow noopener" target="_blank">#NeuralNetworkDynamics</a>, <a href="https://sigmoid.social/tags/oscillations" class="mention hashtag" rel="nofollow noopener" target="_blank">#oscillations</a>, and <a href="https://sigmoid.social/tags/brain" class="mention hashtag" rel="nofollow noopener" target="_blank">#brain</a> 🧠 function. Here is a brief summary including a small <a href="https://sigmoid.social/tags/PythonTutorial" class="mention hashtag" rel="nofollow noopener" target="_blank">#PythonTutorial</a> using the <a href="https://sigmoid.social/tags/NESTsimulator" class="mention hashtag" rel="nofollow noopener" target="_blank">#NESTsimulator</a>.🌍 <a href="https://www.fabriziomusacchio.com/blog/2025-08-15-gap_junctions/" rel="nofollow noopener" translate="no" target="_blank">https://www.fabriziomusacchio.com/blog/2025-08-15-gap_junctions/</a><a href="https://sigmoid.social/tags/CompNeuro" class="mention hashtag" rel="nofollow noopener" target="_blank">#CompNeuro</a> <a href="https://sigmoid.social/tags/Neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#Neuroscience</a> <a href="https://sigmoid.social/tags/Python" class="mention hashtag" rel="nofollow noopener" target="_blank">#Python</a> <a href="https://sigmoid.social/tags/OpenSource" class="mention hashtag" rel="nofollow noopener" target="_blank">#OpenSource</a>

Fabrizio Musacchio📚 New article by Esparza et al. and <a href="https://fosstodon.org/@LMPrida" class="u-url mention" rel="nofollow noopener" target="_blank">@LMPrida</a> : Cell-type-specific <a href="https://sigmoid.social/tags/manifold" class="mention hashtag" rel="nofollow noopener" target="_blank">#manifold</a> analysis discloses independent parallel <a href="https://sigmoid.social/tags/SpatialMaps" class="mention hashtag" rel="nofollow noopener" target="_blank">#SpatialMaps</a> in <a href="https://sigmoid.social/tags/hippocampal" class="mention hashtag" rel="nofollow noopener" target="_blank">#hippocampal</a> <a href="https://sigmoid.social/tags/CA1" class="mention hashtag" rel="nofollow noopener" target="_blank">#CA1</a>. Using <a href="https://sigmoid.social/tags/miniscope" class="mention hashtag" rel="nofollow noopener" target="_blank">#miniscope</a> imaging, they show deep and superficial CA1 <a href="https://sigmoid.social/tags/PyramidalNeurons" class="mention hashtag" rel="nofollow noopener" target="_blank">#PyramidalNeurons</a> encode position and running direction via distinct ring manifolds, manipulable via <a href="https://sigmoid.social/tags/chemogenetics" class="mention hashtag" rel="nofollow noopener" target="_blank">#chemogenetics</a>. Fascinating for revealing parallel, cell-type–specific spatial topologies 👌🌍 <a href="https://doi.org/10.1016/j.neuron.2025.01.022" rel="nofollow noopener" translate="no" target="_blank">https://doi.org/10.1016/j.neuron.2025.01.022</a><a href="https://sigmoid.social/tags/Neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#Neuroscience</a> <a href="https://sigmoid.social/tags/CompNeuro" class="mention hashtag" rel="nofollow noopener" target="_blank">#CompNeuro</a>

Elias MB RauFor everyone who can not attend the CCN Conference this year in amsterdam, all keynote lectures can be streamed here:<a href="https://2025.ccneuro.org/keynote-lectures/" rel="nofollow noopener" translate="no" target="_blank">https://2025.ccneuro.org/keynote-lectures/</a>Full schedule with livestream links here: <a href="https://2025.ccneuro.org/schedule-of-events/" rel="nofollow noopener" translate="no" target="_blank">https://2025.ccneuro.org/schedule-of-events/</a>First off, Nancy Kanwisher at 11.30 am (CET)Edit: Not only keynotes but also symposia can be live streamed 🙂 <a href="https://synapse.cafe/tags/ccn2025" class="mention hashtag" rel="nofollow noopener" target="_blank">#ccn2025</a> <a href="https://synapse.cafe/tags/neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#neuroscience</a> <a href="https://synapse.cafe/tags/cognitivescience" class="mention hashtag" rel="nofollow noopener" target="_blank">#cognitivescience</a> <a href="https://synapse.cafe/tags/computationalneuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#computationalneuroscience</a> <a href="https://synapse.cafe/tags/CompNeuro" class="mention hashtag" rel="nofollow noopener" target="_blank">#CompNeuro</a>

Fabrizio Musacchio<a href="https://synapse.cafe/@axoaxonic" class="u-url mention" rel="nofollow noopener" target="_blank">@axoaxonic</a> Indeed! Here, for everyone else, is the link to the article I originally posted by mistake:🌍 <a href="https://www.cell.com/trends/cognitive-sciences/fulltext/S1364-6613(24)00119-0" rel="nofollow noopener" translate="no" target="_blank">https://www.cell.com/trends/cognitive-sciences/fulltext/S1364-6613(24)00119-0</a> 📝 Scott, Daniel N. et al. , Thalamocortical architectures for flexible cognition and efficient learning, 2024, Trends in Cognitive Sciences, Volume 28, Issue 8, 739 - 756<a href="https://sigmoid.social/tags/CompNeuro" class="mention hashtag" rel="nofollow noopener" target="_blank">#CompNeuro</a> <a href="https://sigmoid.social/tags/Neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#Neuroscience</a> <a href="https://sigmoid.social/tags/NeuralManifolds" class="mention hashtag" rel="nofollow noopener" target="_blank">#NeuralManifolds</a> <a href="https://sigmoid.social/tags/manifolds" class="mention hashtag" rel="nofollow noopener" target="_blank">#manifolds</a>

Fabrizio Musacchio📚 New article by Kringelbach et al.: The <a href="https://sigmoid.social/tags/Thermodynamics" class="mention hashtag" rel="nofollow noopener" target="_blank">#Thermodynamics</a> of Mind. They propose a “Thermodynamics of Mind” framework to quantify hierarchical <a href="https://sigmoid.social/tags/brain" class="mention hashtag" rel="nofollow noopener" target="_blank">#brain</a> orchestration and its underlying mechanisms. Interesting as it links cognitive function to formal physical principles 💫🌍 <a href="https://www.sciencedirect.com/science/article/pii/S1364661324000755" rel="nofollow noopener" translate="no" target="_blank">https://www.sciencedirect.com/science/article/pii/S1364661324000755</a><a href="https://sigmoid.social/tags/Neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#Neuroscience</a> <a href="https://sigmoid.social/tags/CognitiveScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#CognitiveScience</a> <a href="https://sigmoid.social/tags/Thermodynamics" class="mention hashtag" rel="nofollow noopener" target="_blank">#Thermodynamics</a> <a href="https://sigmoid.social/tags/BrainDynamics" class="mention hashtag" rel="nofollow noopener" target="_blank">#BrainDynamics</a> <a href="https://sigmoid.social/tags/CompNeuro" class="mention hashtag" rel="nofollow noopener" target="_blank">#CompNeuro</a> <a href="https://sigmoid.social/tags/Physics" class="mention hashtag" rel="nofollow noopener" target="_blank">#Physics</a> <a href="https://sigmoid.social/tags/Entropy" class="mention hashtag" rel="nofollow noopener" target="_blank">#Entropy</a>

Fabrizio Musacchio📚 New preprint by Vafaii, Galor & Yates: Brain-like variational inference. They derive <a href="https://sigmoid.social/tags/SpikingNeuralNetwork" class="mention hashtag" rel="nofollow noopener" target="_blank">#SpikingNeuralNetwork</a> dynamics directly from variational free energy minimization via online natural <a href="https://sigmoid.social/tags/GradientDescent" class="mention hashtag" rel="nofollow noopener" target="_blank">#GradientDescent</a>, yielding the iterative Poisson <a href="https://sigmoid.social/tags/VAE" class="mention hashtag" rel="nofollow noopener" target="_blank">#VAE</a> (iP-VAE) with strong sparsity, reconstruction & <a href="https://sigmoid.social/tags/BiologicalPlausibility" class="mention hashtag" rel="nofollow noopener" target="_blank">#BiologicalPlausibility</a>.🌍 <a href="https://arxiv.org/abs/2410.19315" rel="nofollow noopener" translate="no" target="_blank">https://arxiv.org/abs/2410.19315</a> 🧑‍💻 <a href="https://github.com/hadivafaii/IterativeVAE" rel="nofollow noopener" translate="no" target="_blank">https://github.com/hadivafaii/IterativeVAE</a><a href="https://sigmoid.social/tags/Neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#Neuroscience</a> <a href="https://sigmoid.social/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#MachineLearning</a> <a href="https://sigmoid.social/tags/SNN" class="mention hashtag" rel="nofollow noopener" target="_blank">#SNN</a> <a href="https://sigmoid.social/tags/CompNeuro" class="mention hashtag" rel="nofollow noopener" target="_blank">#CompNeuro</a>

Fabrizio MusacchioIn our recent <a href="https://sigmoid.social/tags/JournalClub" class="mention hashtag" rel="nofollow noopener" target="_blank">#JournalClub</a>, I presented Genkin et al. (2025), who decode <a href="https://sigmoid.social/tags/DecisionMaking" class="mention hashtag" rel="nofollow noopener" target="_blank">#DecisionMaking</a> in the <a href="https://sigmoid.social/tags/PremotorCortex" class="mention hashtag" rel="nofollow noopener" target="_blank">#PremotorCortex</a> of <a href="https://sigmoid.social/tags/macaques" class="mention hashtag" rel="nofollow noopener" target="_blank">#macaques</a> as low-dimensional <a href="https://sigmoid.social/tags/latent" class="mention hashtag" rel="nofollow noopener" target="_blank">#latent</a> <a href="https://sigmoid.social/tags/dynamics" class="mention hashtag" rel="nofollow noopener" target="_blank">#dynamics</a> shared across <a href="https://sigmoid.social/tags/NeuralPopulations" class="mention hashtag" rel="nofollow noopener" target="_blank">#NeuralPopulations</a>. Their generative model links tuning curves, spike-time variability, and stimulus-dependent potential landscapes to a common internal decision variable. I summarized and discussed their findings in this blog post:📝<a href="https://doi.org/10.1038/s41586-025-09199-1" rel="nofollow noopener" translate="no" target="_blank">https://doi.org/10.1038/s41586-025-09199-1</a> 🌍<a href="https://www.fabriziomusacchio.com/blog/2025-08-01-decoding_decision_making_in_premotor_cortex/" rel="nofollow noopener" translate="no" target="_blank">https://www.fabriziomusacchio.com/blog/2025-08-01-decoding_decision_making_in_premotor_cortex/</a><a href="https://sigmoid.social/tags/CompNeuro" class="mention hashtag" rel="nofollow noopener" target="_blank">#CompNeuro</a> <a href="https://sigmoid.social/tags/Neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#Neuroscience</a> <a href="https://sigmoid.social/tags/Cortex" class="mention hashtag" rel="nofollow noopener" target="_blank">#Cortex</a>

Dan GoodmanNew preprint with <a href="https://neuromatch.social/@marcusghosh" class="u-url mention" rel="nofollow noopener" target="_blank">@marcusghosh</a> on how neural network architecture shapes function. We explored a wide range of architectures, and a family of tasks with components of navigation, decision making under uncertainty, multimodal integration and memory. Performance better explained by "computational traits" like sensitivity and memory, than by architectural features. <a href="https://www.biorxiv.org/content/10.1101/2025.07.28.667142v1" rel="nofollow noopener" translate="no" target="_blank">https://www.biorxiv.org/content/10.1101/2025.07.28.667142v1</a><a href="https://neuromatch.social/tags/neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#neuroscience</a> <a href="https://neuromatch.social/tags/compneuro" class="mention hashtag" rel="nofollow noopener" target="_blank">#compneuro</a> <a href="https://neuromatch.social/tags/computationalneuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#computationalneuroscience</a>

Fabrizio Musacchio📚 Zhou & Schapiro show in their recent study that a gradient of complementary <a href="https://sigmoid.social/tags/learning" class="mention hashtag" rel="nofollow noopener" target="_blank">#learning</a> systems can emerge through <a href="https://sigmoid.social/tags/metalearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#metalearning</a>. Their model self-organizes into subsystems that differ in timescale and representational abstraction – offering a mechanistic account of distributed <a href="https://sigmoid.social/tags/memory" class="mention hashtag" rel="nofollow noopener" target="_blank">#memory</a> architectures across <a href="https://sigmoid.social/tags/cortex" class="mention hashtag" rel="nofollow noopener" target="_blank">#cortex</a>.🌍 <a href="https://www.biorxiv.org/content/10.1101/2025.07.10.664201v1" rel="nofollow noopener" translate="no" target="_blank">https://www.biorxiv.org/content/10.1101/2025.07.10.664201v1</a><a href="https://sigmoid.social/tags/CompNeuro" class="mention hashtag" rel="nofollow noopener" target="_blank">#CompNeuro</a> <a href="https://sigmoid.social/tags/Neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#Neuroscience</a>

Fabrizio MusacchioIn their study, Morales-Gregorio et al. show that <a href="https://sigmoid.social/tags/NeuralManifolds" class="mention hashtag" rel="nofollow noopener" target="_blank">#NeuralManifolds</a> in <a href="https://sigmoid.social/tags/V1" class="mention hashtag" rel="nofollow noopener" target="_blank">#V1</a> shift dynamically under top-down influence from <a href="https://sigmoid.social/tags/V4" class="mention hashtag" rel="nofollow noopener" target="_blank">#V4</a>. They identify two distinct population activity states – eyes open vs. closed – with notably stronger V4→V1 signaling in the foveal region during eyes-open periods. A cool example of how cognitive context reshapes visual cortical dynamics.🌍 <a href="https://www.cell.com/cell-reports/fulltext/S2211-1247(24)00699-5" rel="nofollow noopener" translate="no" target="_blank">https://www.cell.com/cell-reports/fulltext/S2211-1247(24)00699-5</a><a href="https://sigmoid.social/tags/CompNeuro" class="mention hashtag" rel="nofollow noopener" target="_blank">#CompNeuro</a> <a href="https://sigmoid.social/tags/Neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#Neuroscience</a> <a href="https://sigmoid.social/tags/VisualCortex" class="mention hashtag" rel="nofollow noopener" target="_blank">#VisualCortex</a> <a href="https://sigmoid.social/tags/NeuralManifolds" class="mention hashtag" rel="nofollow noopener" target="_blank">#NeuralManifolds</a> <a href="https://sigmoid.social/tags/SystemsNeuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#SystemsNeuroscience</a>

Fabrizio MusacchioAnd as an added bonus: Dileep George, one of the authors of the paper, just shared a <a href="https://sigmoid.social/tags/JupyterNotebook" class="mention hashtag" rel="nofollow noopener" target="_blank">#JupyterNotebook</a> demo 🐍📔. You can explore the <a href="https://sigmoid.social/tags/CSCG" class="mention hashtag" rel="nofollow noopener" target="_blank">#CSCG</a> model, visualize <a href="https://sigmoid.social/tags/PlaceFields" class="mention hashtag" rel="nofollow noopener" target="_blank">#PlaceFields</a>, and inspect the learned <a href="https://sigmoid.social/tags/latent" class="mention hashtag" rel="nofollow noopener" target="_blank">#latent</a> <a href="https://sigmoid.social/tags/graphs" class="mention hashtag" rel="nofollow noopener" target="_blank">#graphs</a> 📈 Just try it out, it's great fun 👌🌍 <a href="https://colab.research.google.com/drive/1kgjuoz_Noo7uV87StSbW7T8-IBQmPOLE?usp=sharing#scrollTo=7jnInNH7RUAX" rel="nofollow noopener" translate="no" target="_blank">https://colab.research.google.com/drive/1kgjuoz_Noo7uV87StSbW7T8-IBQmPOLE?usp=sharing#scrollTo=7jnInNH7RUAX</a><a href="https://sigmoid.social/tags/Hippocampus" class="mention hashtag" rel="nofollow noopener" target="_blank">#Hippocampus</a> <a href="https://sigmoid.social/tags/CognitiveMaps" class="mention hashtag" rel="nofollow noopener" target="_blank">#CognitiveMaps</a> <a href="https://sigmoid.social/tags/CSCG" class="mention hashtag" rel="nofollow noopener" target="_blank">#CSCG</a> <a href="https://sigmoid.social/tags/Neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#Neuroscience</a> <a href="https://sigmoid.social/tags/ComputationalModeling" class="mention hashtag" rel="nofollow noopener" target="_blank">#ComputationalModeling</a> <a href="https://sigmoid.social/tags/CompNeuro" class="mention hashtag" rel="nofollow noopener" target="_blank">#CompNeuro</a>

Fabrizio MusacchioNew <a href="https://sigmoid.social/tags/TeachingMaterial" class="mention hashtag" rel="nofollow noopener" target="_blank">#TeachingMaterial</a> available: Functional Imaging Data Analysis – From Calcium Imaging to Network Dynamics. This course covers the entire workflow from raw <a href="https://sigmoid.social/tags/imaging" class="mention hashtag" rel="nofollow noopener" target="_blank">#imaging</a> data to functional insights, including <a href="https://sigmoid.social/tags/SpikeInference" class="mention hashtag" rel="nofollow noopener" target="_blank">#SpikeInference</a> & <a href="https://sigmoid.social/tags/PopulationAnalysis" class="mention hashtag" rel="nofollow noopener" target="_blank">#PopulationAnalysis</a>. Designed for students and for self-guided learning, with a focus on open content and reproducibility. Feel free to use and share it 🤗🌍 <a href="https://www.fabriziomusacchio.com/blog/2025-07-13-function_image_analysis/" rel="nofollow noopener" translate="no" target="_blank">https://www.fabriziomusacchio.com/blog/2025-07-13-function_image_analysis/</a> <a href="https://sigmoid.social/tags/Python" class="mention hashtag" rel="nofollow noopener" target="_blank">#Python</a> <a href="https://sigmoid.social/tags/DataScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#DataScience</a> <a href="https://sigmoid.social/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#MachineLearning</a> <a href="https://sigmoid.social/tags/Neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#Neuroscience</a> <a href="https://sigmoid.social/tags/OpenSource" class="mention hashtag" rel="nofollow noopener" target="_blank">#OpenSource</a> <a href="https://sigmoid.social/tags/calciumimaging" class="mention hashtag" rel="nofollow noopener" target="_blank">#calciumimaging</a> <a href="https://sigmoid.social/tags/CompNeuro" class="mention hashtag" rel="nofollow noopener" target="_blank">#CompNeuro</a>

Dan GoodmanHow can we test theories in neuroscience? Take a variable predicted to be important by the theory. It could fail to be observed because it's represented in some nonlinear, even distributed way. Or it could be observed but not be causal because the network is a reservoir. How can we deal with this?Increasingly feel like this isn't a theoretical problem but a very practical one that comes up all the time. I'd be interested if anyone has seen anything practical that addresses this.<a href="https://neuromatch.social/tags/neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#neuroscience</a> <a href="https://neuromatch.social/tags/compneuro" class="mention hashtag" rel="nofollow noopener" target="_blank">#compneuro</a>

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