Anno Christopher Kurth

Computational neuroscientist | PostDoc @RIKEN_CBS working with @TAsabuki | Cortical circuits | Neural data |

[Anno Christopher Kurth @ac_kurth]

Neurowissenschaftliche Forschung scheint durch Überinterpretation der in sehr spezifischen Kontexten erhobenen Daten anfällig dafür, für solche Ideologeme missbraucht zu werden. Gibt es hier strukturelle Parallelen zum Psychologismus(streit), mit einem biologistischen Einschlag?

[Daniel-Pascal Zorn @Fionnindy]

Die Epstein-Files zeigen übrigens auch, dass die dort involvierten Wissenschaftler unter ‚intellektuellem Austausch mit Epstein‘ vor allem ungehemmtes Incel-Gerede über grenzwertige und menschenverachtende Themen u. Ideologeme des 19. Jh. meinen. …

[Daniel-Pascal Zorn @Fionnindy]

Die Epstein-Files zeigen übrigens auch, dass die dort involvierten Wissenschaftler unter ‚intellektuellem Austausch mit Epstein‘ vor allem ungehemmtes Incel-Gerede über grenzwertige und menschenverachtende Themen u. Ideologeme des 19. Jh. meinen. …

[Anno Christopher Kurth @ac_kurth]

@Fionnindy Er mag wohl ein einflussreicher oder renommierter Kognitionswissenschaftler sein, darüber kann ich nicht urteilen. Neurowissenschaftliche Spitzenforschung macht er aber nicht.

[Daniel-Pascal Zorn @Fionnindy]

Oder vielleicht zu seinen Vorstellungen bezüglich einer – durch seine eigenen, angenommenen Fähigkeiten definierten – biologisch-kognitiven Überlegenheitsphantasien?

Das wäre schon zentral – es zeigt Ideologeme des 19. Jh. im Herz der neurowissenschaftlichen Spitzenforschung.

[RIKEN @riken_en]

Controlling #chaos in #neuralnetworks. @TAsabuki #neuroscience @RIKEN_CBS https://t.co/xjtU83X1iM https://t.co/96xRSZvKNv

[Simone Azeglio @simoneazeglio]

🧠🔬 Excited to share our #NeurIPS2025 paper: "Convolution Goes Higher-Order"!

We asked: Can shallow networks be as expressive as deep ones? Inspired by biological vision, we introduce higher-order convolutions that capture complex image patterns standard CNNs miss.

🧵👇

[RIKEN Center for Brain Science @RIKEN_CBS]

Together with #CBS_Summer, #NEURO2026 has opened applications for its annual gathering in Kobe, Japan, themed “Let Future Neuroscientists Take Off!”
Travel awards available—check the details and join a super-hot #neuroscience summer🔥
https://t.co/aV0wCXV0O5

[Quantitative Biology (Neurons and Cognition) @BioPapers]

Synchronization and semantization in deep spiking networks. https://t.co/yn7drdZiRL

[Toshitake Asabuki @TAsabuki]

📜Out in Nat. Commun!
We propose a novel framework called “predictive alignment”, which trains the chaotic RNN via a biologically plausible rule. Collaborated with @ClopathLab
https://t.co/kljmnzaZhP

[Luiz Pessoa @PessoaBrain]

𝗧𝗼𝗽-𝗱𝗼𝘄𝗻 𝗮𝗻𝗱 𝗯𝗼𝘁𝘁𝗼𝗺-𝘂𝗽 𝗻𝗲𝘂𝗿𝗼𝘀𝗰𝗶𝗲𝗻𝗰𝗲: 𝗼𝘃𝗲𝗿𝗰𝗼𝗺𝗶𝗻𝗴 𝘁𝗵𝗲 𝗰𝗹𝗮𝘀𝗵 𝗼𝗳 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗰𝘂𝗹𝘁𝘂𝗿𝗲𝘀
https://t.co/eP4b6xJaJR
Small contribution in piece by @_fernando_rosas and colleagues on how we need both types of research culture

[Toshitake Asabuki @TAsabuki]

🚀Out in PNAS!
We find that recurrent networks trained with the predictive plasticity rules explain many features of prediction errors observed in experimental studies
https://t.co/eABA7bteZv
@ClopathLab

[Toshitake Asabuki @TAsabuki]

Now in
@eLife
“Biologically plausible excitatory/inhibitory plasticity rules in recurrent spiking networks embed state-transition statistics into spontaneous activity.”
https://t.co/blT8foqGov
@ClopathLab

[PRX Life @PRX_Life]

An analytical framework based on low-rank random matrix theory shows that specific connectivity patterns in #NeuralNetworks, such as chain motifs, strongly affect excitatory and inhibitory neuron balance and the network’s response to inputs.

🔗 https://t.co/B3XK5xExjt

[PRX Life @PRX_Life]

A low-rank decomposition of the connectivity matrix reveals how local motifs — such as chain motifs — govern dynamics in excitatory-inhibitory networks, with implications for interpreting #optogenetic experiments.

Read the paper: https://t.co/B3XK5xExjt

[PRX Life @PRX_Life]

Scientists developed a measure that captures and compares the 2D structure of real, arbitrary matrices, demonstrating how it can robustly assess similarities and differences in synthetic network connectivity data and experimental data of brain activity.

https://t.co/7FNAegX0Nq

[PRX Life @PRX_Life]

Singular angle similarity measures the similarity of real, arbitrary matrices accounting for their 2D structure, outperforming standard methods in analyzing synthetic networks and neural activity patterns.

Read the paper: https://t.co/7FNAegX0Nq

[Kensuke Yoshida @kenyoshida20]

Excited to share that our paper on a biological model of nonlinear dimensionality reduction has been published in @ScienceAdvances!
https://t.co/wUnUhqzOf0

[Toshitake Asabuki @TAsabuki]

How can a biologically plausible synaptic plasticity rule tame the chaos in recurrent neural networks?
Check out our latest preprint in @ClopathLab!
https://t.co/dBxqJ62wkE

[Aitor Morales-Gregorio @AitorMoraGre]

"Software in science is ubiquitous yet overlooked"

https://t.co/7J7N89etyY

[Aitor Morales-Gregorio @AitorMoraGre]

Happy to share that our paper "Neural manifolds in V1 change with top-down signals from V4 targeting the foveal region" is now published in @CellReports, together with @ac_kurth @sachavanalbada et al. 🧠🙈💫

https://t.co/z6pCn8OWKr