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#computationalneuroscience

2 posts2 participants0 posts today
Dan Goodman<p>New preprint! What happens if you add neuromodulation to spiking neural networks and let them go wild with it? TLDR: it can improve performance especially in challenging sensory processing tasks.</p><p>Preprint:</p><p><a href="https://www.biorxiv.org/content/10.1101/2025.07.25.666748v1" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">biorxiv.org/content/10.1101/20</span><span class="invisible">25.07.25.666748v1</span></a></p><p>Short explainer thread on Bluesky:</p><p><a href="https://bsky.app/profile/neural-reckoning.org/post/3lz4rihm2622e" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">bsky.app/profile/neural-reckon</span><span class="invisible">ing.org/post/3lz4rihm2622e</span></a></p><p><a href="https://neuromatch.social/tags/neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>neuroscience</span></a> <a href="https://neuromatch.social/tags/ComputationalNeuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ComputationalNeuroscience</span></a> <a href="https://neuromatch.social/tags/SpikingNeuralNetwork" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>SpikingNeuralNetwork</span></a></p>
Dan Goodman<p>If you're interested in spiking neural networks, you should know about surrogate gradient descent and @fzenke.bsky.social's SPyTorch tutorial which shows just how easy it is to apply this method that's changing the whole field. Check out my paean below, published in <span class="h-card" translate="no"><a href="https://mastodon.social/@thetransmitter" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>thetransmitter</span></a></span></p><p><a href="https://www.thetransmitter.org/this-paper-changed-my-life/this-paper-changed-my-life-dan-goodman-on-a-paper-that-reignited-the-field-of-spiking-neural-networks/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">thetransmitter.org/this-paper-</span><span class="invisible">changed-my-life/this-paper-changed-my-life-dan-goodman-on-a-paper-that-reignited-the-field-of-spiking-neural-networks/</span></a> </p><p><a href="https://neuromatch.social/tags/SpikingNeuralNetworks" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>SpikingNeuralNetworks</span></a> <a href="https://neuromatch.social/tags/Neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Neuroscience</span></a> <a href="https://neuromatch.social/tags/ComputationalNeuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ComputationalNeuroscience</span></a></p>
Redish Lab<p>New preprint available!</p><p>Henri S. Chastain, A. D. Redish (2025). Multiple Decision-Making<br>Systems and the Common Currency Hypothesis. psyArxiv.<br><a href="https://osf.io/preprints/psyarxiv/75k6d_v1" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">osf.io/preprints/psyarxiv/75k6</span><span class="invisible">d_v1</span></a></p><p>In this just-released new preprint, we analyze the consequences of taking both the <a href="https://neuromatch.social/tags/neuroeconomic" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>neuroeconomic</span></a> theory of "Common Currency" and the <a href="https://neuromatch.social/tags/DecisionMaking" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DecisionMaking</span></a> theory of "Multiple decision systems" seriously from a <a href="https://neuromatch.social/tags/computationalNeuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>computationalNeuroscience</span></a> perspective and find that these theories interact and lead to a fascinating set of testable predictions.</p><p><a href="https://neuromatch.social/tags/psyarxiv" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>psyarxiv</span></a> <a href="https://neuromatch.social/tags/neuroecononomics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>neuroecononomics</span></a> <a href="https://neuromatch.social/tags/neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>neuroscience</span></a> <a href="https://neuromatch.social/tags/DecisionMaking" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DecisionMaking</span></a></p>
jobRxiv<p>Computational modeling and high-density microelectrode array analysis forne<br>UC Davis Health </p><p>See the full job description on jobRxiv: <a href="https://jobrxiv.org/job/uc-davis-health-27778-computational-modeling-and-high-density-microelectrode-array-analysis-forne/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">jobrxiv.org/job/uc-davis-healt</span><span class="invisible">h-27778-computational-modeling-and-high-density-microelectrode-array-analysis-forne/</span></a></p><p><a href="https://mas.to/tags/3Dcellculture" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>3Dcellculture</span></a> <a href="https://mas.to/tags/Cellularneuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Cellularneuroscience</span></a> <a href="https://mas.to/tags/computationalneuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>computationalneuroscience</span></a> <a href="https://mas.to/tags/ScienceJobs" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ScienceJobs</span></a> <a href="https://mas.to/tags/hiring" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>hiring</span></a> <a href="https://mas.to/tags/research" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>research</span></a><br><a href="https://jobrxiv.org/job/uc-davis-health-27778-computational-modeling-and-high-density-microelectrode-array-analysis-forne/?fsp_sid=2314" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">jobrxiv.org/job/uc-davis-healt</span><span class="invisible">h-27778-computational-modeling-and-high-density-microelectrode-array-analysis-forne/?fsp_sid=2314</span></a></p>
Dan Goodman<p>Is anarchist science possible?</p><p>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.</p><p>The project started as a tutorial on a new technique at the <span class="h-card" translate="no"><a href="https://neuromatch.social/@CosyneMeeting" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>CosyneMeeting</span></a></span> 2022. We realised that the technique was easy and cheap for anyone to use with a lot of low hanging fruit.</p><p><a href="https://neural-reckoning.github.io/cosyne-tutorial-2022/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">neural-reckoning.github.io/cos</span><span class="invisible">yne-tutorial-2022/</span></a></p><p>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.</p><p><a href="https://en.wikipedia.org/wiki/Polymath_Project" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">en.wikipedia.org/wiki/Polymath</span><span class="invisible">_Project</span></a></p><p>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"><span class="invisible">https://</span><span class="ellipsis">comob-project.github.io/snn-so</span><span class="invisible">und-localization/paper</span></a></p><p>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: <span class="h-card" translate="no"><a href="https://neuromatch.social/@marcusghosh" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>marcusghosh</span></a></span> <span class="h-card" translate="no"><a href="https://fediscience.org/@TomasFiers" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>TomasFiers</span></a></span> <br><span class="h-card" translate="no"><a href="https://neuromatch.social/@GabrielBena" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>GabrielBena</span></a></span> <span class="h-card" translate="no"><a href="https://sigmoid.social/@rory" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>rory</span></a></span> </p><p>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 <span class="h-card" translate="no"><a href="https://fosstodon.org/@mystmarkdown" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>mystmarkdown</span></a></span> and GitHub actions to automate this.</p><p><a href="https://comob-project.github.io/snn-sound-localization/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">comob-project.github.io/snn-so</span><span class="invisible">und-localization/</span></a></p><p>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:</p><p><a href="https://www.eneuro.org/content/12/7/ENEURO.0383-24.2025" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">eneuro.org/content/12/7/ENEURO</span><span class="invisible">.0383-24.2025</span></a></p><p>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.</p><p>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!</p><p>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!</p><p><a href="https://neuromatch.social/tags/neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>neuroscience</span></a> <a href="https://neuromatch.social/tags/computationalneuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>computationalneuroscience</span></a> <a href="https://neuromatch.social/tags/compneuro" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>compneuro</span></a> <a href="https://neuromatch.social/tags/anarchism" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>anarchism</span></a> <a href="https://neuromatch.social/tags/science" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>science</span></a></p>
Laurent Perrinet<p>🚀 Excited to share our new paper: </p><blockquote><p>"DynTex: A real-time generative model of dynamic naturalistic luminance textures"</p></blockquote><p>...now published in Journal of Vision!</p><p>🔹 Why it matters: Dynamic textures (e.g., fire, water, foliage) are everywhere, but modeling them in real-time has been a challenge. DynTex bridges this gap with a biologically inspired, efficient approach. </p><p>🔹 Key innovation: A generative model that captures the spatiotemporal statistics of natural scenes while running in real-time. </p><p>🔹 Applications: Computer vision, neuroscience, VR/AR, and more.📖 </p><p>Read it here: <a href="https://doi.org/10.1167/jov.25.11.2" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">doi.org/10.1167/jov.25.11.2</span><span class="invisible"></span></a> </p><p>with Andrew Meso, Nikos Gekas, Jonathan Vacher, Pascal Mamassian and Guillaume Masson</p><p>More on: <a href="https://laurentperrinet.github.io/publication/meso-25/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">laurentperrinet.github.io/publ</span><span class="invisible">ication/meso-25/</span></a> </p><p><a href="https://neuromatch.social/tags/DynamicTextures" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DynamicTextures</span></a> <a href="https://neuromatch.social/tags/ComputationalNeuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ComputationalNeuroscience</span></a> <a href="https://neuromatch.social/tags/ComputerVision" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ComputerVision</span></a> <a href="https://neuromatch.social/tags/GenerativeModels" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>GenerativeModels</span></a> <a href="https://neuromatch.social/tags/OpenScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>OpenScience</span></a></p>
Laurent Perrinet<p>🧠 Excited to share our latest research presented at <a href="https://neuromatch.social/tags/CNS2025" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>CNS2025</span></a> in beautiful Firenze, Italy!</p><p>"Population decoding of visual motion direction in V1 marmoset monkey: effects of uncertainty"</p><p>Our work explores how populations of neurons in the primary visual cortex (V1) of marmoset monkeys encode visual motion direction, with a particular focus on understanding how uncertainty influences this neural decoding process.<br>Key highlights:</p><ul><li>Advanced population-level analysis of V1 neural responses to motion stimuli</li><li>Novel insights into how the brain handles uncertainty in visual motion processing</li><li>Marmoset model providing crucial translational insights for visual neuroscience</li></ul><p>This research contributes to our fundamental understanding of how the visual system processes motion information at the earliest stages of cortical processing, with important implications for both basic neuroscience and potential clinical applications.<br>Thank you to the CNS organizing committee for hosting such an inspiring conference in the stunning venue of Palazzo dei Congressi in Villa Vittoria! 🇮🇹</p><p><a href="https://neuromatch.social/tags/ComputationalNeuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ComputationalNeuroscience</span></a> <a href="https://neuromatch.social/tags/VisualNeuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>VisualNeuroscience</span></a> <a href="https://neuromatch.social/tags/MotionProcessing" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MotionProcessing</span></a> <a href="https://neuromatch.social/tags/CNS2025" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>CNS2025</span></a> <a href="https://neuromatch.social/tags/Neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Neuroscience</span></a> <a href="https://neuromatch.social/tags/Research" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Research</span></a> <a href="https://neuromatch.social/tags/MarmosetModel" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MarmosetModel</span></a> <a href="https://neuromatch.social/tags/V1" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>V1</span></a> <a href="https://neuromatch.social/tags/PopulationDecoding" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>PopulationDecoding</span></a></p><p>Link to publication: <a href="https://laurentperrinet.github.io/publication/laine-25-cns/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">laurentperrinet.github.io/publ</span><span class="invisible">ication/laine-25-cns/</span></a></p><p>Want to learn more about uncertainty in the visual cortex? Check out our recent work published in Nature Communications Biology: <a href="https://laurentperrinet.github.io/publication//ladret-23" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">laurentperrinet.github.io/publ</span><span class="invisible">ication//ladret-23</span></a></p>
Neurofrontiers<p>I finally had the chance to add a new computational modeling tutorial. Using the Hodgkin-Huxley model previously implemented, I'm talking about how to summarize information from simulated traces, and giving some small insights I've picked up along the way:</p><p><a href="https://neurofrontiers.blog/analyzing-a-virtual-neuron-part-1/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">neurofrontiers.blog/analyzing-</span><span class="invisible">a-virtual-neuron-part-1/</span></a></p><p>Not so sure if anyone has use for it, but I've found it quite cathartic to write down what I wish I knew when I first started learning.</p><p><a href="https://neuromatch.social/tags/neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>neuroscience</span></a> <a href="https://neuromatch.social/tags/ComputationalNeuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ComputationalNeuroscience</span></a> <a href="https://neuromatch.social/tags/modeling" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modeling</span></a> <a href="https://neuromatch.social/tags/data" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>data</span></a> <a href="https://neuromatch.social/tags/DataAnalysis" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DataAnalysis</span></a> <a href="https://neuromatch.social/tags/learning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>learning</span></a> <a href="https://neuromatch.social/tags/blaugust" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>blaugust</span></a> <a href="https://neuromatch.social/tags/blaugust2025" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>blaugust2025</span></a></p>
Laurent Perrinet<p>🧠 TODAY at <a href="https://neuromatch.social/tags/CCN2025" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>CCN2025</span></a> ! Poster A145, 1:30-4:30pm at de Brug &amp; E‑Hall. We've developed a bio-inspired "What-Where" CNN that mimics primate visual pathways - achieving better classification with less computation. Come chat! 🎯</p><p>Presented by main author Jean-Nicolas JÉRÉMIE and in cosupervision with Emmanuel Daucé</p><p><a href="https://laurentperrinet.github.io/publication/jeremie-25-ccn/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">laurentperrinet.github.io/publ</span><span class="invisible">ication/jeremie-25-ccn/</span></a></p><p>Our research introduces a novel "What-Where" approach to CNN categorization, inspired by the dual pathways of the primate visual system:</p><ul><li><p>The ventral "What" pathway for object recognition</p></li><li><p>The dorsal "Where" pathway for spatial localization</p></li></ul><p>Key innovations:</p><p>✅ Bio-inspired selective attention mechanism</p><p>✅ Improved classification performance with reduced computational cost</p><p>✅ Smart visual sensor that samples only relevant image regions</p><p>✅ Likelihood mapping for targeted processing</p><p>The results? </p><p>Better accuracy while using fewer resources - proving that nature's designs can still teach us valuable lessons about efficient AI.</p><p>Come find us this afternoon for great discussions!</p><p><a href="https://neuromatch.social/tags/CCN2025" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>CCN2025</span></a> <a href="https://neuromatch.social/tags/ComputationalNeuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ComputationalNeuroscience</span></a> <a href="https://neuromatch.social/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AI</span></a> <a href="https://neuromatch.social/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MachineLearning</span></a> <a href="https://neuromatch.social/tags/BioinspiredAI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BioinspiredAI</span></a> <a href="https://neuromatch.social/tags/ComputerVision" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ComputerVision</span></a> <a href="https://neuromatch.social/tags/Research" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Research</span></a></p>
Elias MB Rau<p>For everyone who can not attend the CCN Conference this year in amsterdam, all keynote lectures can be streamed here:</p><p><a href="https://2025.ccneuro.org/keynote-lectures/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">2025.ccneuro.org/keynote-lectu</span><span class="invisible">res/</span></a></p><p>Full schedule with livestream links here:<br><a href="https://2025.ccneuro.org/schedule-of-events/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">2025.ccneuro.org/schedule-of-e</span><span class="invisible">vents/</span></a></p><p>First off, Nancy Kanwisher at 11.30 am (CET)</p><p>Edit: Not only keynotes but also symposia can be live streamed 🙂 </p><p><a href="https://synapse.cafe/tags/ccn2025" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ccn2025</span></a> <a href="https://synapse.cafe/tags/neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>neuroscience</span></a> <a href="https://synapse.cafe/tags/cognitivescience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>cognitivescience</span></a> <a href="https://synapse.cafe/tags/computationalneuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>computationalneuroscience</span></a> <a href="https://synapse.cafe/tags/CompNeuro" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>CompNeuro</span></a></p>
Dan Goodman<p>Spiking neural networks people, this message is for you! </p><p>The annual SNUFA workshop is now open for abstract submission (deadline Sept 26) and (free) registration. This year's speakers include Elisabetta Chicca, Jason Eshraghian, Tomoki Fukai, Chengcheng Huang, and... you?</p><p><a href="https://snufa.net/2025/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">snufa.net/2025/</span><span class="invisible"></span></a></p><p>Please boost!</p><p><a href="https://neuromatch.social/tags/neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>neuroscience</span></a> <a href="https://neuromatch.social/tags/computationalneuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>computationalneuroscience</span></a> <a href="https://neuromatch.social/tags/SpikingNeuralNetworks" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>SpikingNeuralNetworks</span></a></p>
Ankur Sinha "FranciscoD"<p>The <a href="https://fosstodon.org/tags/NeuroFedora" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>NeuroFedora</span></a> team has changed how it packages software for users. We now prioritise software that cannot easily be installed from upstream forges (like PyPi) for inclusion as <a href="https://fosstodon.org/tags/rpm" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>rpm</span></a> packages into <span class="h-card" translate="no"><a href="https://fosstodon.org/@fedora" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>fedora</span></a></span> . Software that can be easily installed is tested to ensure that it functions on all the <a href="https://fosstodon.org/tags/Python" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Python</span></a> versions supported by any Fedora release.</p><p>Read more here:</p><p><a href="https://neuroblog.fedoraproject.org/2025/08/02/packaging-changes-at-neurofedora.html" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">neuroblog.fedoraproject.org/20</span><span class="invisible">25/08/02/packaging-changes-at-neurofedora.html</span></a></p><p>The Comp Neuro Lab has also been dropped.</p><p><a href="https://fosstodon.org/tags/Neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Neuroscience</span></a> <a href="https://fosstodon.org/tags/ComputationalNeuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ComputationalNeuroscience</span></a> <a href="https://fosstodon.org/tags/FOSS" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>FOSS</span></a> <a href="https://fosstodon.org/tags/Linux" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Linux</span></a> <a href="https://fosstodon.org/tags/Distributions" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Distributions</span></a></p>
Dan Goodman<p>New preprint with <span class="h-card" translate="no"><a href="https://neuromatch.social/@marcusghosh" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>marcusghosh</span></a></span> 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. </p><p><a href="https://www.biorxiv.org/content/10.1101/2025.07.28.667142v1" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">biorxiv.org/content/10.1101/20</span><span class="invisible">25.07.28.667142v1</span></a></p><p><a href="https://neuromatch.social/tags/neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>neuroscience</span></a> <a href="https://neuromatch.social/tags/compneuro" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>compneuro</span></a> <a href="https://neuromatch.social/tags/computationalneuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>computationalneuroscience</span></a></p>
Neuromatch<p>Rito <span class="h-card" translate="no"><a href="https://mathstodon.xyz/@rg" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>rg</span></a></span> joined <a href="https://neuromatch.social/tags/NeuromatchAcademy" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>NeuromatchAcademy</span></a> as a student...then came back as a TA to give back!</p><p>What keeps him coming back?<br>✨ The global, diverse learning pods<br>🧠 The high-quality, interdisciplinary content<br>🤝 The chance to learn and teach</p><p>Read his story: <a href="https://www.linkedin.com/feed/update/urn:li:activity:7356037435849428994" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">linkedin.com/feed/update/urn:l</span><span class="invisible">i:activity:7356037435849428994</span></a></p><p><a href="https://neuromatch.social/tags/Neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Neuroscience</span></a> <a href="https://neuromatch.social/tags/ComputationalNeuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ComputationalNeuroscience</span></a> <a href="https://neuromatch.social/tags/NeuroAI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>NeuroAI</span></a> <a href="https://neuromatch.social/tags/STEMeducation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>STEMeducation</span></a></p>
Tanguy Fardet<p>I just discovered the ARC-AGI initiative and the associated test to estimate how close "AI" models are from <a href="https://fediscience.org/tags/AGI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AGI</span></a></p><p><a href="https://arcprize.org/arc-agi" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">arcprize.org/arc-agi</span><span class="invisible"></span></a></p><p>While I found the initiative interesting, I'm not sure I understand what in this test really guarantees that the model is capable of some form of generalization and problem-solving.<br>Wouldn't it be possible for specialized pattern-matching/discovering algorithms to solve such problems?<br>I imagine some computer scientists, mathematicians or computational neuroscientists have already had a look at this, so would anyone knows of some articles/blogs on the topic?</p><p>Maybe <span class="h-card" translate="no"><a href="https://scholar.social/@wim_v12e" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>wim_v12e</span></a></span>? Is this something you already looked at?</p><p><a href="https://fediscience.org/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AI</span></a> <a href="https://fediscience.org/tags/machineLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>machineLearning</span></a> <a href="https://fediscience.org/tags/neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>neuroscience</span></a> <a href="https://fediscience.org/tags/cognition" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>cognition</span></a> <a href="https://fediscience.org/tags/computationalNeuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>computationalNeuroscience</span></a> <a href="https://fediscience.org/tags/neuralNets" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>neuralNets</span></a> <a href="https://fediscience.org/tags/lazyWeb" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>lazyWeb</span></a></p>
United States News Beep<p>Deep adaptive learning predicts and diagnoses CSVD-related cognitive decline using radiomics from T2-FLAIR: a multi-centre study</p><p>Patient enrolment and baseline characteristics A total of 783 su…<br><a href="https://newsbeep.org/tags/NewsBeep" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>NewsBeep</span></a> <a href="https://newsbeep.org/tags/News" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>News</span></a> <a href="https://newsbeep.org/tags/US" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>US</span></a> <a href="https://newsbeep.org/tags/USA" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>USA</span></a> <a href="https://newsbeep.org/tags/UnitedStates" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>UnitedStates</span></a> <a href="https://newsbeep.org/tags/UnitedStatesOfAmerica" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>UnitedStatesOfAmerica</span></a> <a href="https://newsbeep.org/tags/Artificialintelligence" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Artificialintelligence</span></a> <a href="https://newsbeep.org/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AI</span></a> <a href="https://newsbeep.org/tags/ArtificialIntelligence" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ArtificialIntelligence</span></a> <a href="https://newsbeep.org/tags/Biomedicine" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Biomedicine</span></a> <a href="https://newsbeep.org/tags/biotechnology" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>biotechnology</span></a> <a href="https://newsbeep.org/tags/Cognitiveageing" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Cognitiveageing</span></a> <a href="https://newsbeep.org/tags/Cognitiveneuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Cognitiveneuroscience</span></a> <a href="https://newsbeep.org/tags/Computationalneuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Computationalneuroscience</span></a> <a href="https://newsbeep.org/tags/general" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>general</span></a> <a href="https://newsbeep.org/tags/Imageprocessing" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Imageprocessing</span></a> <a href="https://newsbeep.org/tags/Medicine" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Medicine</span></a>/PublicHealth <a href="https://newsbeep.org/tags/Technology" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Technology</span></a><br><a href="https://www.newsbeep.com/us/12439/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="">newsbeep.com/us/12439/</span><span class="invisible"></span></a></p>
Dan Goodman<p>Almost last call to register for UK neural computation conference in London July 10-11. Registration deadline is July 1st. We have some great talks and posters as well as a session on funding with ARIA.</p><p>Look forward to seeing you all there. Now click here 👇</p><p><a href="https://neuralcomputation.uk/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">neuralcomputation.uk/</span><span class="invisible"></span></a></p><p><a href="https://neuromatch.social/tags/neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>neuroscience</span></a> <a href="https://neuromatch.social/tags/compneuro" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>compneuro</span></a> <a href="https://neuromatch.social/tags/computationalneuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>computationalneuroscience</span></a></p>
Ankur Sinha "FranciscoD"<p>A new release of <a href="https://fosstodon.org/tags/PyNeuroML" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>PyNeuroML</span></a> is available. Please update to get the latest fixes and features. <a href="https://fosstodon.org/tags/NeuroML" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>NeuroML</span></a> <a href="https://fosstodon.org/tags/ComputationalModelling" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ComputationalModelling</span></a> <a href="https://fosstodon.org/tags/ComputationalNeuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ComputationalNeuroscience</span></a> </p><p>```<br>pip install --upgrade pyneuroml<br>```</p>
Fabrizio Musacchio<p>🧠 New <a href="https://sigmoid.social/tags/preprint" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>preprint</span></a>! Confavreux et al. use meta-learning to uncover thousands of diverse, local <a href="https://sigmoid.social/tags/plasticity" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>plasticity</span></a> rule quadruplets that stabilize <a href="https://sigmoid.social/tags/RecurrentSpikingNetworks" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>RecurrentSpikingNetworks</span></a> — and incidentally support <a href="https://sigmoid.social/tags/memory" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>memory</span></a> functions like novelty detection, replay, &amp; contextual prediction. A striking case of function emerging from stability.</p><p>📄 <a href="https://doi.org/10.1101/2025.05.28.656584" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">doi.org/10.1101/2025.05.28.656</span><span class="invisible">584</span></a></p><p><a href="https://sigmoid.social/tags/Neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Neuroscience</span></a> <a href="https://sigmoid.social/tags/Plasticity" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Plasticity</span></a> <a href="https://sigmoid.social/tags/ComputationalNeuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ComputationalNeuroscience</span></a> <a href="https://sigmoid.social/tags/CompNeuro" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>CompNeuro</span></a> <a href="https://sigmoid.social/tags/SNN" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>SNN</span></a> <a href="https://sigmoid.social/tags/SpikingNeurons" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>SpikingNeurons</span></a></p>
Fabrizio Musacchio<p>🧠 The <a href="https://sigmoid.social/tags/Italian" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Italian</span></a> Network of <a href="https://sigmoid.social/tags/ComputationalNeuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ComputationalNeuroscience</span></a> announced its 2025 conference:</p><p>📍 Palazzo della Salute, Padova, Italy 🇮🇹 <br>📅 September 22–24, 2025<br>⏰ Submission deadline: June 7, 2025<br>🌍 <a href="https://www.incn.it/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="">incn.it/</span><span class="invisible"></span></a></p><p>A 3-day deep dive into the brain — from models to data, theory to technology.<br><a href="https://sigmoid.social/tags/CompNeuro" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>CompNeuro</span></a> <a href="https://sigmoid.social/tags/Neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Neuroscience</span></a></p>
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