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>
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#computationalneuroscience
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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, & 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>
Bytes Europe<p>Microsoft takes big step in AI race at Build 2025, Deutsche Bank says <a href="https://www.byteseu.com/1038124/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="">byteseu.com/1038124/</span><span class="invisible"></span></a> <a href="https://pubeurope.com/tags/AgenticAi" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AgenticAi</span></a> <a href="https://pubeurope.com/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AI</span></a> <a href="https://pubeurope.com/tags/ArtificialIntelligence" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ArtificialIntelligence</span></a> <a href="https://pubeurope.com/tags/ArtificialIntelligenceArmsRace" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ArtificialIntelligenceArmsRace</span></a> <a href="https://pubeurope.com/tags/business" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>business</span></a> <a href="https://pubeurope.com/tags/ComputationalNeuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ComputationalNeuroscience</span></a> <a href="https://pubeurope.com/tags/Cybernetics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Cybernetics</span></a> <a href="https://pubeurope.com/tags/Finance" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Finance</span></a> <a href="https://pubeurope.com/tags/GenerativeArtificialIntelligence" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>GenerativeArtificialIntelligence</span></a> <a href="https://pubeurope.com/tags/IntelligentAgent" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>IntelligentAgent</span></a> <a href="https://pubeurope.com/tags/Internet" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Internet</span></a> <a href="https://pubeurope.com/tags/microsoft" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>microsoft</span></a> <a href="https://pubeurope.com/tags/Quartz" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Quartz</span></a> <a href="https://pubeurope.com/tags/SatyaNadella" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>SatyaNadella</span></a> <a href="https://pubeurope.com/tags/Technology" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Technology</span></a> <a href="https://pubeurope.com/tags/TheMicrosoft" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>TheMicrosoft</span></a></p>
Laurent Perrinet<p>Researchers in France are working on creating a French network of researchers to organize interaction, communication and training in <a href="https://neuromatch.social/tags/computationalneuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>computationalneuroscience</span></a> </p><p>If you are a CompNeuro working in France, consider joining, and registering to our mailing list: <a href="https://listes.services.cnrs.fr/wws/subscribe/rt_neurocomp" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">listes.services.cnrs.fr/wws/su</span><span class="invisible">bscribe/rt_neurocomp</span></a></p><p><a href="https://bsky.app/profile/lauradugue.bsky.social/post/3lo6rrtvb3k2v" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">bsky.app/profile/lauradugue.bs</span><span class="invisible">ky.social/post/3lo6rrtvb3k2v</span></a><br><a href="https://www.linkedin.com/posts/laura-dugu%C3%A9-59964756_rtneurocomp-r%C3%A9seau-fran%C3%A7ais-de-neurosciences-activity-7324039702427090944-LKsN" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">linkedin.com/posts/laura-dugu%</span><span class="invisible">C3%A9-59964756_rtneurocomp-r%C3%A9seau-fran%C3%A7ais-de-neurosciences-activity-7324039702427090944-LKsN</span></a></p>
Laurent Perrinet<p>🧠 Nouveau Réseau Français De Neurosciences Computationnelles !🧠</p><p>Une initiative passionnante est en cours en France ! Des chercheurs établissent un réseau national dédié aux <a href="https://neuromatch.social/tags/NeurosciencesComputationnelles" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>NeurosciencesComputationnelles</span></a> pour faciliter :</p><ul><li>La collaboration entre équipes de recherche</li><li>Le partage de connaissances et de ressources</li><li>La formation de la prochaine génération de chercheurs</li><li>La visibilité de la recherche française dans ce domaine en pleine expansion</li></ul><p>🔍 Vous travaillez en <a href="https://neuromatch.social/tags/ComputationalNeuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ComputationalNeuroscience</span></a> sur le territoire français ? Que vous soyez chercheur·e confirmé·e, post-doctorant·e, doctorant·e ou étudiant·e, rejoignez notre communauté !</p><p>📝 Inscrivez-vous à notre liste de diffusion pour recevoir les actualités, événements et opportunités :</p><p>➡️ <a href="https://listes.services.cnrs.fr/wws/subscribe/rt_neurocomp" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">listes.services.cnrs.fr/wws/su</span><span class="invisible">bscribe/rt_neurocomp</span></a></p><p>Ensemble, renforçons l'excellence française en <a href="https://neuromatch.social/tags/NeurosciencesComputationnelles" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>NeurosciencesComputationnelles</span></a> !<br><a href="https://neuromatch.social/tags/Recherche" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Recherche</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/CNRS" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>CNRS</span></a> <a href="https://neuromatch.social/tags/Science" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Science</span></a> <a href="https://neuromatch.social/tags/IA" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>IA</span></a> <a href="https://neuromatch.social/tags/IntelligenceArtificielle" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>IntelligenceArtificielle</span></a></p>
Neurofrontiers<p>A few weeks ago, I shared a differential equations tutorial for beginners, written from the perspective of a neuroscientist who's had to grapple with the computational part. Following up on that, I've now tackled the first real beast encountered by most computational neuroscience students: the Hodgkin-Huxley model. </p><p>While remaining incredibly elegant to this day, this model is also a mathematically dense system of equations that can overwhelm and discourage beginners, especially those with non-mathematical backgrounds. Similar to the first tutorial, I've tried to build intuition step-by-step, starting with a simple RC circuit, layering in Na⁺ and K⁺ channels, and ending with the full spike-generation story. </p><p>Feedback is welcome, especially from fellow non-math converts.<br><a href="https://neurofrontiers.blog/building-a-virtual-neuron-2/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">neurofrontiers.blog/building-a</span><span class="invisible">-virtual-neuron-2/</span></a></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/Python" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Python</span></a> <a href="https://neuromatch.social/tags/hodgkinHuxleyModel" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>hodgkinHuxleyModel</span></a> <a href="https://neuromatch.social/tags/math" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>math</span></a> <a href="https://neuromatch.social/tags/biophysics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>biophysics</span></a> </p><p>From: <span class="h-card" translate="no"><a href="https://neuromatch.social/@neurofrontiers" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>neurofrontiers</span></a></span><br><a href="https://neuromatch.social/@neurofrontiers/114150087773204813" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">neuromatch.social/@neurofronti</span><span class="invisible">ers/114150087773204813</span></a></p>
Dan Goodman<p>How do babies and blind people learn to localise sound without labelled data? We propose that innate mechanisms can provide coarse-grained error signals to boostrap learning.</p><p>New preprint from <span class="h-card" translate="no"><a href="https://mastodon.social/@yang_chu" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>yang_chu</span></a></span>. </p><p><a href="https://arxiv.org/abs/2001.10605" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">arxiv.org/abs/2001.10605</span><span class="invisible"></span></a></p><p>Thread below 👇</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/compneurosci" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>compneurosci</span></a></p>
El Duvelle Neuro<p>I keep going back to this question about <a href="https://neuromatch.social/tags/TemporalCreditAssignment" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>TemporalCreditAssignment</span></a> and <a href="https://neuromatch.social/tags/HippocampalReplay" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>HippocampalReplay</span></a>:<br>As an "agent" you want to learn the value of places and which places are likely to lead to reward;</p><p>-1) if a place leads to higher than expected reward, you'll want to propagate back the reward info from the reward throughout the places that led to the reward. If replay does that you should see an increase of replay at a new reward site and the replay sequences should start at the reward and reflect what you just did to reach it. Right?</p><p>-2) if a place leads to lower than expected reward, you'll also want to propagate that lowered value, pretty much in the same way, so if replay does that you should see a similar replay rate and content for increased OR decreased reward sites. Right?</p><p>-3) if a place has had unchanged reward for a while and you're just in exploitation mode (just going there again and again because you know that's the best place to go to in the environment) then you shouldn't need to update anything and replay rate should be quite low at that unchanged reward side. Right?</p><p>That's not at all what replay is doing IRL, so does that mean replay is not used for temporal credit assignment? Or did I (very likely) miss something?</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/DecisionMaking" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DecisionMaking</span></a> <a href="https://neuromatch.social/tags/Hippocampus" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Hippocampus</span></a></p>
Dan Goodman<p>Preview of the talk I'm giving on Friday. <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>
Neurofrontiers<p>When I transitioned from cognitive to computational neuroscience, I found myself in a bit of a bind. I had learned calculus, but I had progressed little beyond pattern recognition: I knew which rules to apply to find solutions to which equations, but the equations themselves lacked any sort of real meaning for me. </p><p>So I struggled with understanding how formulas could be implemented in code and why the code I was reading could be described by those formulas. Resources explaining math “for neuroscientists” were unfortunately quite useless for me, because they usually presented the necessary equations for describing various neural systems, assuming the presence of that basic understanding/intuition I lacked. </p><p>Of course, I figured things out eventually (otherwise I wouldn’t be writing about it), but I’m 85% sure I’m not the only one who’s ever struggled with this, and so I wrote the tutorial I wish I could’ve had. If you’re in a similar position, I hope you’ll find it useful. And if not, maybe it helps you get a glimpse into the struggles of the non-math people in your life. Either way, it has cats.</p><p><a href="https://neurofrontiers.blog/building-a-virtual-neuron-1/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">neurofrontiers.blog/building-a</span><span class="invisible">-virtual-neuron-1/</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/math" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>math</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/tutorial" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>tutorial</span></a> <a href="https://neuromatch.social/tags/academia" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>academia</span></a></p>
Ankur Sinha "FranciscoD"<p><a href="https://fosstodon.org/tags/NeuroML" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>NeuroML</span></a> is participating in <a href="https://fosstodon.org/tags/GSoC2025" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>GSoC2025</span></a> again this year under <span class="h-card" translate="no"><a href="https://neuromatch.social/@INCF" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>INCF</span></a></span> . We're looking for people with some experience of <a href="https://fosstodon.org/tags/ComputationalNeuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ComputationalNeuroscience</span></a> to work on developing <a href="https://fosstodon.org/tags/standardised" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>standardised</span></a> biophysically detailed computational models using <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/PyNN" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>PyNN</span></a> and <a href="https://fosstodon.org/tags/OpenSourceBrain" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>OpenSourceBrain</span></a>. </p><p>Please spread the word, especially to students interested in modelling. We will help them learn the NeuroML ecosystem so they can use its standardised pipeline in their work.</p><p><a href="https://docs.neuroml.org/NeuroMLOrg/OutreachTraining.html#google-summer-of-code" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">docs.neuroml.org/NeuroMLOrg/Ou</span><span class="invisible">treachTraining.html#google-summer-of-code</span></a> </p><p>CC <a href="https://fosstodon.org/tags/AcademicChatter" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AcademicChatter</span></a></p>
Dan Goodman<p>I'm giving an online talk starting in 15m (as part of UCL's NeuroAI series).</p><p>It's on neural architectures and our current line of research trying to figure out what they might be good for (including some philosophy: what might an answer to this question even look like?).</p><p>Sign up (free) at this link to get the zoom link:</p><p><a href="https://www.eventbrite.co.uk/e/ucl-neuroai-talk-series-tickets-1189972031379?utm-campaign=social&utm-content=attendeeshare&utm-medium=discovery&utm-term=listing&utm-source=cp&aff=ebdsshcopyurl" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">eventbrite.co.uk/e/ucl-neuroai</span><span class="invisible">-talk-series-tickets-1189972031379?utm-campaign=social&utm-content=attendeeshare&utm-medium=discovery&utm-term=listing&utm-source=cp&aff=ebdsshcopyurl</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> <a href="https://neuromatch.social/tags/neuroai" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>neuroai</span></a></p>
El Duvelle Neuro<p>With the current situation in the <a href="https://neuromatch.social/tags/US" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>US</span></a>, several of my former colleagues there are looking for a <a href="https://neuromatch.social/tags/PostDocJob" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>PostDocJob</span></a> in <a href="https://neuromatch.social/tags/Europe" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Europe</span></a>, to do <a href="https://neuromatch.social/tags/BehaviouralNeuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BehaviouralNeuroscience</span></a> or <a href="https://neuromatch.social/tags/ComputationalNeuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ComputationalNeuroscience</span></a> in <a href="https://neuromatch.social/tags/SpatialCognition" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>SpatialCognition</span></a> (or adjacent). <br>Lots of hashtags I know..</p><p>Do you know a <a href="https://neuromatch.social/tags/EU" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>EU</span></a> or <a href="https://neuromatch.social/tags/UK" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>UK</span></a> <a href="https://neuromatch.social/tags/Neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Neuroscience</span></a> lab looking to hire a postdoc in these fields? Let me know and I'll pass it on to them!</p><p>Edit: adding <a href="https://neuromatch.social/tags/RodentResearch" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>RodentResearch</span></a> and <a href="https://neuromatch.social/tags/humanresearch" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>humanresearch</span></a> for the species concerned (in this case)</p>
Dan Goodman<p>Come along to my (free, online) UCL NeuroAI talk next week on neural architectures. What are they good for? All will finally be revealed and you'll never have to think about that question again afterwards. Yep. Definitely that.</p><p>🗓️ Wed 12 Feb 2025 <br>⏰ 2-3pm GMT<br>ℹ️ Details and registration: <a href="https://www.eventbrite.co.uk/e/ucl-neuroai-talk-series-tickets-1216638381149" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">eventbrite.co.uk/e/ucl-neuroai</span><span class="invisible">-talk-series-tickets-1216638381149</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> <a href="https://neuromatch.social/tags/NeuroAI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>NeuroAI</span></a></p>
Neuromatch<p>🚀 Neuromatch Academy 2025 is coming! 🚀</p><p>📢 Key Dates:<br>📍 Feb 24 – Applications Open!<br>📍 Mar 23 – Deadline (Midnight in your timezone)<br>📍 Mid-April – Decisions Announced<br>📍 Early May – Enrollment Deadline</p><p>Join a global community & dive into Computational Neuroscience, Deep Learning, Comp Tools for Climate Science, or NeuroAI! Don’t miss out—apply & tag a friend! 🌍✨</p><p><a href="https://neuromatch.social/tags/NMA2025" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>NMA2025</span></a> <a href="https://neuromatch.social/tags/AIForAll" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AIForAll</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/DeepLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DeepLearning</span></a></p>
Ankur Sinha "FranciscoD"<p>We are very happy to provide a consolidated update on the <a href="https://fosstodon.org/tags/NeuroML" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>NeuroML</span></a> ecosystem in our <span class="h-card" translate="no"><a href="https://fediscience.org/@eLife" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>eLife</span></a></span> paper, “The NeuroML ecosystem for standardized multi-scale modeling in neuroscience”: <a href="https://doi.org/10.7554/eLife.95135.3" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">doi.org/10.7554/eLife.95135.3</span><span class="invisible"></span></a></p><p><a href="https://fosstodon.org/tags/NeuroML" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>NeuroML</span></a> is a standard and software ecosystem for data-driven biophysically detailed <a href="https://fosstodon.org/tags/ComputationalModelling" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ComputationalModelling</span></a> endorsed by the <span class="h-card" translate="no"><a href="https://neuromatch.social/@INCF" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>INCF</span></a></span> and CoMBINE, and includes a large community of users and software developers. </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/ComputationalModelling" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ComputationalModelling</span></a> 1/x</p>
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