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Anno Kurth

@ackurth.bsky.social
163 followers 625 following 8 posts

Computational Neuroscientist:Neural Circuits:Neural Data:PostDoctoral Researcher at RIKEN CBS with Toshitake Asabuki

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Asabuki Lab @asabukilab.bsky.social · 22/09/2026
How does a place cell know when to rewrite itself? Check out our new preprint on spike-history gating of BTSP and closed-loop rewriting of CA1 representations. Congrats Anno! @ackurth.bsky.social www.biorxiv.org/content/10.6...
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Alexander van Meegen @avm.bsky.social · 30/06/2026
Interested in multitasking RNNs from a theory perspective? Check out this preprint with the amazing @lpezon.bsky.social. TLDR: A low-rank / latent space approach to @lndriscoll.bsky.social & @sussillodavid.bsky.social style shared components. And a story starring solution space degeneracy.
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Asabuki Lab @asabukilab.bsky.social · 18/06/2026
How can RNNs combine independently learned computations without being trained on their combinations? Excited to share our new preprint📜 www.biorxiv.org/content/10.6...
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Sam Gershman @gershbrain.bsky.social · 19/04/2026
There seems to be a broad perception across psychology and neuroscience that work shouldn't be "too technical" in order to reach the broadest possible audience. While I think we should strive for accessibility, I feel that this attitude can also be self-defeating: why are we dumbing down?
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Aitor Morales-Gregorio @aitormg.bsky.social · 16/04/2026
Come learn some programming with a healthy helping of critical thinking; something no genAI can take away from you :) Also Prague is great fun ;)
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Aaron Milstein @neurosutras.bsky.social · 27/03/2026
Our latest publication grapples with how the brain could implement gradient descent by sending learning targets top-down, gating plasticity with dendritic inhibition, and updating synaptic weights with biologically observed learning rules like BTSP. www.cell.com/cell-reports...
cell.com
Cellular and subcellular specialization enables biology-constrained deep learning
Galloni et al. introduce “dendritic target propagation”: a Dale’s law-compliant learning algorithm for cortical microcircuits with soma- and dendrite-targeting inhibition and realistic connectivity co...
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Aitor Morales-Gregorio @aitormg.bsky.social · 14/03/2026
If you are into networks, development, and dynamics don't miss my poster [3-072] this afternoon at #cosyne2026 🤠🧠
Poster banner from cosyne 2026
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Renato Duarte @rcfduarte.bsky.social · 11/03/2026
I tracked every keyword in 22 years of Cosyne abstracts to map how computational neuroscience evolved — from Bayesian brains to neural manifolds to LLMs — and where it's heading next.
open.substack.com
22 years of Brain Science: what CoSyNe tells us about the evolution of Neuroscience
Tracking the intellectual DNA of Computational and Systems Neuroscience through its flagship meeting
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Konrad Kording @kordinglab.bsky.social · 26/02/2026
This paper on how the brain may do gradient descent is very cool: www.nature.com/articles/s41...
nature.com
Vectorized instructive signals in cortical dendrites - Nature
Mice learning a neurofeedback brain–computer interface task show neuron-specific teaching signals in cortical dendrites, consistent with a vectorized solution for credit assignment in the brain.
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Asabuki Lab @asabukilab.bsky.social · 16/02/2026
How can RNNs learn continuously without forgetting? 🧠 Our new preprint shows how a predictive learning rule organizes recurrent dynamics into orthogonal manifolds, reducing task interference. Congrats Zihan @zihan-liu.bsky.social ! www.biorxiv.org/content/10.6...
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Jesper Sjöström @pjsjostrom.bsky.social · 16/02/2026
🧪🧠 New preprint: helping resolve a decades-long debate in synaptic plasticity NMDA receptors are central to Hebbian learning. Yet for >30 years, the existence and function of presynaptic NMDA receptors have remained controversial. 📄 doi.org/10.64898/202... 1/6
doi.org
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bioRxiv Neuroscience @biorxiv-neursci.bsky.social · 16/02/2026
Learning sculpts orthogonal task manifolds for continual skill learning in recurrent networks www.biorxiv.org/content/10.64898/20…
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arXiv q-bio.NC Neurons and Cognition @qbionc-bot.bsky.social · 01/01/2026
Zajzon, Bouhadjar, Fabre, Schmidt, Ostendorf, Neftci, Morrison, Duarte: SymSeqBench: a unified framework for the generation and analysis of rule-based symbolic sequences and datasets arxiv.org/abs/2512.24977 arxiv.org/pdf/2512.24977 arxiv.org/html/2512.24977
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bioRxiv Neuroscience @biorxiv-neursci.bsky.social · 24/12/2025
Retinotopy constrains the topology of neural manifolds in macaque visual cortex www.biorxiv.org/content/10.64898/20…
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Monica H Green @monicamedhist.bsky.social · 19/12/2025
This report in Nature on the costs of competing for & administering scientific grants is shocking: "In other words, European taxpayers will have spent more on the funding process than on the funding itself, and the scientific ecosystem has been drained." www.nature.com/articles/d41... 🧪
nature.com
Point of no returns: researchers are crossing a threshold in the fight for funding
With so little money to go round, the costs of competing for grants can exceed what the grants are worth. When that happens, nobody wins.
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NEST Simulator @nest-initiative.org · 19/12/2025
SAVE THE DATE! On June 16-17, 2026, the NEST Initiative is excited to invite everyone interested in Neural Simulation Technology and the NEST Simulator to the NEST virtual Conference 2026. Registration and abstract submission will open soon! nest-simulator.org/conference
nest-simulator.org
NEST Conference 2026
The NEST Initiative invites everyone interested in Neural Simulation Technology and the NEST Simulator to the annual virtual NEST Conference. The NEST Conference provides an opportunity for the NEST Community to meet, exchange success stories, swap advice, learn about current developments in and around NEST spiking network simulation and its application. Take the opportunity to advance your skills in using NEST at our workshops! We particularly encourage young scientists to participate in...
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Ulises Pereira-Obilinovic @ulisespereirao.bsky.social · 02/12/2025
0/10 Thanks for the interest in our preprint. Some takes say it negates or fully supports the “manifold hypothesis”, neither quite right. Our results show that if you only focus on the manifold capturing most of task-related variance, you could miss important dynamics that actually drive behavior.
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Joao Barbosa @jbarbosa.org · 25/11/2025
Y’all are reading this paper in the wrong way. We love to trash dominant hypothesis, but we need to look for evidence against the manifold hypothesis elsewhere: This elegant work doesn't show neural dynamics are high D, nor that we should stop using PCA It’s quite the opposite! (thread)
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Aaron Milstein @neurosutras.bsky.social · 20/11/2025
BTSP in V1 www.biorxiv.org/content/10.1...
biorxiv.org
Plateau potentials are instructive signals for behavioral timescale synaptic plasticity in the neocortex
Learning occurs via the adjustment of synaptic weights across a variety of timescales. The mechanisms supporting these processes, from single-shot to iterative learning, are unclear. The prevailing mo...
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Tatiana Engel @engeltatiana.bsky.social · 12/10/2025
A study led by Cina Aghamohammadi is now out in ‪@natcomms.nature.com‬! We developed a mathematical framework for partitioning spiking variability, which revealed that spiking irregularity is nearly invariant for each neuron and decreases along the cortical hierarchy. www.nature.com/articles/s41...
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Aitor Morales-Gregorio @aitormg.bsky.social · 28/09/2025
Undecided which workshop to attend at #BernsteinConference ? We put together a stellar lineup for a satellite workshop on the effects of top-down signals on neuronal dynamics! 🧠✨📣 Join us tomorrow in room 0.101 ! bernstein-network.de/bernstein-co... With @ackurth.bsky.social
bernstein-network.de
(SW2025) Top-down control of neural dynamics – Bernstein Netzwerk Computational Neuroscience
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Joao Barbosa @jbarbosa.org · 26/09/2025
On the first workshop talk (Monday), I'll make the case that everything might be everywhere when you decode, but that is not the end of the story: different regions have very different dynamics and encoding geometries. This workshop is organized by @aitormg.bsky.social and @ackurth.bsky.social
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arXiv q-bio.NC Neurons and Cognition @qbionc-bot.bsky.social · 19/08/2025
Jonas Oberste-Frielinghaus, Anno C. Kurth, Julian G\"oltz, Laura Kriener, Junji Ito, Mihai A. Petrovici, Sonja Gr\"un: Synchronization and semantization in deep spiking networks arxiv.org/abs/2508.12975 arxiv.org/pdf/2508.12975 arxiv.org/html/2508.12975
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arXiv q-bio.NC Neurons and Cognition @qbionc-bot.bsky.social · 20/08/2025
Jakob Stubenrauch, Naomi Auer, Richard Kempter, Benjamin Lindner: Stochastic synaptic dynamics under learning arxiv.org/abs/2508.13846 arxiv.org/pdf/2508.13846 arxiv.org/html/2508.13846
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The Transmitter @thetransmitter.bsky.social · 11/08/2025
Our current approach to defining neural populations is largely arbitrary. We need new methods for grouping cells, ideally by their dynamics, writes @markdhumphries.bsky.social #neuroskyence www.thetransmitter.org/systems-neur...
thetransmitter.org
The challenge of defining a neural population
Our current approach is largely arbitrary. We need new methods for grouping cells, ideally by their dynamics.
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Blake Richards @tyrellturing.bsky.social · 11/07/2025
1/3) This may be a very important paper, it suggests that there are no prediction error encoding neurons in sensory areas of cortex: www.biorxiv.org/content/10.1... I personally am a big fan of the idea that cortical regions (allo and neo) are doing sequence prediction. But... 🧠📈 🧪
biorxiv.org
Sensory responses of visual cortical neurons are not prediction errors
Predictive coding is theorized to be a ubiquitous cortical process to explain sensory responses. It asserts that the brain continuously predicts sensory information and imposes those predictions on lo...
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Aitor Morales-Gregorio @aitormg.bsky.social · 10/07/2025
Undecided whether to attend the #BernsteinConference2025 ? @ackurth.bsky.social and myself put together a stellar lineup for a satellite workshop on the effects of top-down signals on neuronal dynamics! 🧠✨📣 bernstein-network.de/bernstein-co...
bernstein-network.de
(SW2025) Top-down control of neural dynamics – Bernstein Netzwerk Computational Neuroscience
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Aitor Morales-Gregorio @aitormg.bsky.social · 15/05/2025
New preprint alert 📣📣📣 New results showing how strong the link between brain dynamics and structure is 💫👀🧪 "Spontaneous spiking statistics form unique area-specific fingerprints and reflect the hierarchy of cerebral cortex" 🐾🧠 www.biorxiv.org/content/10.1...
biorxiv.org
Spontaneous spiking statistics form unique area-specific fingerprints and reflect the hierarchy of cerebral cortex
The cerebral cortex, from sensory to higher cognitive areas, is hierarchically organised Several dynamical and anatomical measures, such as timescales and neurotransmitter receptor expression, have in...
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Aitor Morales-Gregorio @aitormg.bsky.social · 06/05/2025
📣 New & better method to compare matrices just dropped! 📣 Ever wanted to compare things like connectivity matrices or large matrices of neuron responses in trials? There is a new way to do so in town ✨🧪 Singular Angle Similarity (SAS) journals.aps.org/prxlife/abst... 🧵
Geometric interpretation of Singular Angle Similarity (SAS). 

Matrices Ma (red) and Mb (blue). Their eigenvectors scaled by the square root of their eigenvalues span the main axes of ellipsoids. These square matrices capture the correlation structure of along the horizontal and vertical axis, respectively (double-headed colored arrows). SAS compares the angles between the corresponding ellipsoids (dashed colored arrows).
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NEST Simulator @nest-initiative.org · 25/04/2025
The NEST virtual conference is gearing up! Join us June 17-18, 2025 for a chance to exchange ideas and learn about developments in simulation science! Interested in contributing? The abstract submission deadline has been extended to **April 30**. Visit nest-simulator.org/conference. #nestsim
nest-simulator.org
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Ken Miller @kenmiller.bsky.social · 31/01/2025
New preprint: "The geometry of the neural state space of decisions", work by Mauro Monsalve-Mercado, buff.ly/42wVHD5. Surprising results & predictions! (Thread) We analyze neuropixel population recordings in macaque area LIP during a reaction time, random-dot motion 1/
Picture of neural manifolds for the two choices in a decision-making task, depicted in 3D and in 2D
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Markus Meister @mameister4.bsky.social · 17/12/2024
The unbearable slowness of being: Humans still clock in at just 10 bits/s. Even after peer review :) Share link: authors.elsevier.com/a/1kHVa3BtfH.... ArXiv: arxiv.org/abs/2408.10234
authors.elsevier.com
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Aitor Morales-Gregorio @aitormg.bsky.social · 10/12/2024
DAY 10: Advent of Comp Neuro 🎄🤖🧠🧪 Modular spiking networks that are connected randomly usually fail to propagate signals, but there is a way to overcome this: topographic connectivity! "Passing the Message: Representation Transfer in Modular Balanced Networks" www.frontiersin.org/journals/com...
Figure 1 from Zajzon et al 

Schematic overview of the sequential setup and input stimuli. Networks are composed of four modules with identical internal structure, with random (A) or topographically structured (B) feed-forward projections. Structured stimuli drive specific, randomly selected sub-populations in M0. For stimulus S1, the topographic projections (B, orange arrows) between the modules are represented explicitly in addition to the corresponding stimulus-specific sub-populations (orange ellipses), whereas for S2 only the sub-populations are depicted (blue ellipses). The black feed-forward arrows depict the remaining sparse random connections from neurons that are not part of any stimulus-specific cluster. (C) Illustrative example of the input encoding scheme: a symbolic input sequence of length T (3 in this example), containing |S| different, randomly ordered stimuli (S = {S1, S2}), is encoded into a binary matrix of dimensions |S| × T. Each stimulus is then converted into a set of 800 Poissonian spike trains of fixed duration (200 ms) and rate νstim and delivered to a subset of ϵNE excitatory and ϵNI inhibitory neurons.
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Aitor Morales-Gregorio @aitormg.bsky.social · 06/12/2024
DAY 6: Advent of Comp Neuro 🎄🤖🧠🧪 This nifty theory predicts how intrinsic timescales depend on network structure, neuron properties and input. A nontrivial task! “Microscopic theory of intrinsic timescales in spiking neural networks” tinyurl.com/43esfrrk By @avm.bsky.social and @albada.bsky.social
Figure 1 from van Meegen and van Albada 2021:

Illustration of the theory. (a) We consider populations of randomly connected neurons ( 𝛼, 𝛽) that communicate via spike trains 𝑥𝛼𝑖⁡(𝑡). The neurons of population 𝛽 are connected to those of population 𝛼 with connection probability 𝑝𝛼⁢𝛽. (b) The theory reduces a population to a single neuron driven by an effective stochastic input 𝜂𝛼. The first- and second-order statistics 𝜇𝛼𝜂 and 𝐶𝛼𝜂 of 𝜂𝛼 depend self-consistently on the output statistics, 𝜈𝛼 and 𝐶𝛼𝑥. (c) From the stationary spike train autocorrelation function 𝐶𝑥⁡(𝜏)=𝜈⁢𝛿⁡(𝜏)+ˆ𝐶𝑥⁡(𝜏), we obtain the correlation time 𝜏𝑐, the asymptotic decay 𝜏∞, and the variability of the rate across neurons, 𝜎2𝜈. (d) Instead of the stationary autocorrelation function we sometimes consider the power spectrum 𝑆𝑥⁡(𝑓), which saturates at the firing rate, 𝑆𝑥⁡(𝑓)𝑓→∞−→𝜈, and, for a renewal process, has the zero-frequency limit 𝑆𝑥⁡(𝑓)𝑓→0−→𝜈⁢CV2. Throughout, we consider the population–averaged single-unit statistics (black curve) instead of the statistics of the population-averaged activity (gray curve).
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Aitor Morales-Gregorio @aitormg.bsky.social · 03/12/2024
DAY 2: Advent of Comp Neuro 🎄🤖🧠🧪 Heterogeneous connectivity brings network dynamics closer to criticality! "Second type of criticality in the brain uncovers rich multiple-neuron dynamics" www.pnas.org/doi/full/10....
Figure 3 from Dahmen et al:

Dispersion of correlations measures stability of network dynamics. Connection strength in a sparse random network increases from top to bottom (vertical axis). (A) Connections (arrows) between a pair of observed neurons (black dots); indirect connections contribute to correlations via intermediate neurons (gray dots). (B) Bulk eigenvalues (colored dots) of the connectivity matrix in the complex plane [critical line at 1]. (C) Distributions of covariances. Enlargements are shown in Insets. (D) SD of distribution of covariances (black curve; colored symbols correspond to distributions shown in C).
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Aitor Morales-Gregorio @aitormg.bsky.social · 03/12/2024
DAY 1: Advent of Comp Neuro 🎄🤖🧠🧪 Feedback in RNNs can rotate the neural subspace to separate different source signals! "Invariant neural subspaces maintained by feedback modulation" elifesciences.org/articles/76096
Figure 8 from Naumann et al:

Schematic of the dynamic blind source separation task, context space, and the modulated feedforward network.

Information flow is indicated by black arrows, and the flow of the error during training with backpropagation through time (BPTT) is shown in yellow.
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