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Fabrizio Musacchio<p>Mouse retrosplenial cortex encodes spatial hypotheses with well-behaved recurrent dynamics, which can combine these hypotheses with incoming information to resolve ambiguities</p><p><a href="https://www.nature.com/articles/s41593-025-01944-z" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">nature.com/articles/s41593-025</span><span class="invisible">-01944-z</span></a><br><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/CompNeuro" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>CompNeuro</span></a></p>
Fabrizio Musacchio<p>New <a href="https://sigmoid.social/tags/TeachingMaterial" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>TeachingMaterial</span></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">#<span>imaging</span></a> data to functional insights, including <a href="https://sigmoid.social/tags/SpikeInference" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>SpikeInference</span></a> &amp; <a href="https://sigmoid.social/tags/PopulationAnalysis" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>PopulationAnalysis</span></a>. Designed for students and for self-guided learning, with a focus on open content and reproducibility. Feel free to use and share it 🤗</p><p>🌍 <a href="https://www.fabriziomusacchio.com/blog/2025-07-13-function_image_analysis/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">fabriziomusacchio.com/blog/202</span><span class="invisible">5-07-13-function_image_analysis/</span></a> </p><p><a href="https://sigmoid.social/tags/Python" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Python</span></a> <a href="https://sigmoid.social/tags/DataScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DataScience</span></a> <a href="https://sigmoid.social/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MachineLearning</span></a> <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/OpenSource" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>OpenSource</span></a> <a href="https://sigmoid.social/tags/calciumimaging" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>calciumimaging</span></a> <a href="https://sigmoid.social/tags/CompNeuro" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>CompNeuro</span></a></p>
Dan Goodman<p>How 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?</p><p>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.</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></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>
Alicia Izquierdo, Ph.D.<p>📣 Preprint alert ✨New insights into the tradeoff of effort and delay costs! A collaboration with the Wikenheiser lab <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://www.biorxiv.org/content/10.1101/2025.06.03.657635v1" 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">25.06.03.657635v1</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>
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>
Dan Goodman<p>Low stakes pet peeve of the day: spiking neural network people stop saying SNNs are the third generation of ANNs. They predate them! <a href="https://neuromatch.social/tags/compneuro" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>compneuro</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&amp;utm-content=attendeeshare&amp;utm-medium=discovery&amp;utm-term=listing&amp;utm-source=cp&amp;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&amp;utm-content=attendeeshare&amp;utm-medium=discovery&amp;utm-term=listing&amp;utm-source=cp&amp;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>
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>
Dirk Gütlin<p>Visual cognition in multimodal large language models <a href="https://bsky.app/search?q=%23CompNeuro" rel="nofollow noopener" target="_blank">#CompNeuro</a> <a href="https://bsky.app/search?q=%23NeuroAI" rel="nofollow noopener" target="_blank">#NeuroAI</a> <a href="https://www.nature.com/articles/s42256-024-00963-y" rel="nofollow noopener" target="_blank">www.nature.com/articles/s42...</a><br><br><a href="https://www.nature.com/articles/s42256-024-00963-y" rel="nofollow noopener" target="_blank">Visual cognition in multimodal...</a></p>
Dan Goodman<p>What's the right way to think about modularity in the brain? This devilish 😈 question is a big part of my research now, and it started with this paper with <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> finally published after the first preprint in 2021! </p><p><a href="https://www.nature.com/articles/s41467-024-55188-9" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">nature.com/articles/s41467-024</span><span class="invisible">-55188-9</span></a></p><p>We know the brain is physically structured into distinct areas ("modules"?). We also know that some of these have specialised function. But is there a necessary connection between these two statements? What is the relationship - if any - between 'structural' and 'functional' modularity?</p><p>TLDR if you don't want to read the rest: there is no necessary relationship between the two, although when resources are tight, functional modularity is more likely to arise when there's structural modularity. We also found that functional modularity can change over time! Longer version follows.</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>
Laurent Perrinet<p>Discovered the COSYNE 2025 workshop program is out! 🧠</p><p>Looking forward to fascinating computational neuroscience talks &amp; discussions (March 31-April 1, 2025).</p><p>Check out the program: <a href="https://www.cosyne.org/workshops-program-2025" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">cosyne.org/workshops-program-2</span><span class="invisible">025</span></a></p><p>See you there! <a href="https://neuromatch.social/tags/COSYNE2025" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>COSYNE2025</span></a> <a href="https://neuromatch.social/tags/CompNeuro" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>CompNeuro</span></a> <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> <br><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> <a href="https://neuromatch.social/tags/Cosyne" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Cosyne</span></a></p>
Dan Goodman<p>New preprint! With Swathi Anil and <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>.</p><p>If you want to get the most out of a multisensory signal, you should take it's temporal structure into account. But which neural architectures do this best? 🧵👇</p><p><a href="https://www.biorxiv.org/content/10.1101/2024.12.19.629348v1" 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">24.12.19.629348v1</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/compneuro" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>compneuro</span></a></p>
Nosrat<p>(10/n) If you’ve made it this far, you’ll definitely want to check out the full paper. Grab your copy here: <br><a href="https://www.biorxiv.org/content/10.1101/2024.12.17.628339v1" 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">24.12.17.628339v1</span></a><br>📤 Sharing is highly appreciated!<br><a href="https://masto.ai/tags/compneuro" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>compneuro</span></a> <a href="https://masto.ai/tags/neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>neuroscience</span></a> <a href="https://masto.ai/tags/NeuroAI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>NeuroAI</span></a> <a href="https://masto.ai/tags/dynamicalsystems" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dynamicalsystems</span></a></p>
Dirk Gütlin<p>A tale of two algorithms: Structured slots explain prefrontal sequence memory and are unified with hippocampal cognitive maps <a href="https://bsky.brid.gy/hashtag/neuroscience" rel="nofollow noopener" target="_blank">#neuroscience</a> <a href="https://bsky.brid.gy/hashtag/compneuro" rel="nofollow noopener" target="_blank">#compneuro</a> <a href="https://www.cell.com/neuron/fulltext/S0896-6273(24)00765-7" rel="nofollow noopener" target="_blank">www.cell.com/neuron/fullt...</a><br><br><a href="https://www.cell.com/neuron/fulltext/S0896-6273(24)00765-7" rel="nofollow noopener" target="_blank">A tale of two algorithms: Stru...</a></p>
Dirk Gütlin<p>A universal hippocampal memory code across animals and environments <a href="https://bsky.brid.gy/hashtag/neuroscience" rel="nofollow noopener" target="_blank">#neuroscience</a> <a href="https://bsky.brid.gy/hashtag/compneuro" rel="nofollow noopener" target="_blank">#compneuro</a> <a href="https://www.biorxiv.org/content/10.1101/2024.10.24.620127v4" rel="nofollow noopener" target="_blank">www.biorxiv.org/content/10.1...</a></p>
Victor Buendía<p>We have a new preprint on the emergence of orientation selectivity in layers 2/3 and 4 of the mouse. We use data from the Allen Institute's Microns project, which includes structure plus function of thousands of neurons, to constrain network models that account for the observations and hint some key features on the origin of tuning in L2/3. For any feedback, do not hesitate to contact us!</p><p><a href="https://www.biorxiv.org/content/10.1101/2024.11.18.624135v1" 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">24.11.18.624135v1</span></a></p><p><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/compneuro" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>compneuro</span></a> <a href="https://fediscience.org/tags/compneurosci" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>compneurosci</span></a></p>
Dirk Gütlin<p>Integrating brainstem and cortical functional architectures <a href="https://bsky.brid.gy/hashtag/neuroscience" rel="nofollow noopener" target="_blank">#neuroscience</a> <a href="https://bsky.brid.gy/hashtag/compneuro" rel="nofollow noopener" target="_blank">#compneuro</a> <a href="https://www.nature.com/articles/s41593-024-01787-0" rel="nofollow noopener" target="_blank">www.nature.com/articles/s41...</a><br><br><a href="https://www.nature.com/articles/s41593-024-01787-0" rel="nofollow noopener" target="_blank">Integrating brainstem and cort...</a></p>
Dirk Gütlin<p>Two-dimensional neural geometry underpins hierarchical organization of sequence in human working memory <a href="https://bsky.brid.gy/hashtag/neuroscience" rel="nofollow noopener" target="_blank">#neuroscience</a> <a href="https://bsky.brid.gy/hashtag/neuroAI" rel="nofollow noopener" target="_blank">#neuroAI</a> <a href="https://bsky.brid.gy/hashtag/compneuro" rel="nofollow noopener" target="_blank">#compneuro</a> <a href="https://www.nature.com/articles/s41562-024-02047-8" rel="nofollow noopener" target="_blank">www.nature.com/articles/s41...</a><br><br><a href="https://www.nature.com/articles/s41562-024-02047-8" rel="nofollow noopener" target="_blank">Two-dimensional neural geometr...</a></p>