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#compneurosci — Public Fediverse posts

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  1. Dendrites are what machine learning is missing. (And inhibitory neurons, but hey, one at a time.)

    "What can a neuron compute", by Ido Aizenbud, David Beniaguev, Noam Pnueli, Idan Segev, Michael London, 2026
    biorxiv.org/content/10.64898/2

    All about dendrites. Extending prior work by Beniaguev et al. 2021 and Jones and Kording 2021.

    #neuroscience #CompNeurosci

  2. Dendrites are what machine learning is missing. (And inhibitory neurons, but hey, one at a time.)

    "What can a neuron compute", by Ido Aizenbud, David Beniaguev, Noam Pnueli, Idan Segev, Michael London, 2026
    biorxiv.org/content/10.64898/2

    All about dendrites. Extending prior work by Beniaguev et al. 2021 and Jones and Kording 2021.

    #neuroscience #CompNeurosci

  3. Dendrites are what machine learning is missing. (And inhibitory neurons, but hey, one at a time.)

    "What can a neuron compute", by Ido Aizenbud, David Beniaguev, Noam Pnueli, Idan Segev, Michael London, 2026
    biorxiv.org/content/10.64898/2

    All about dendrites. Extending prior work by Beniaguev et al. 2021 and Jones and Kording 2021.

    #neuroscience #CompNeurosci

  4. Dendrites are what machine learning is missing. (And inhibitory neurons, but hey, one at a time.)

    "What can a neuron compute", by Ido Aizenbud, David Beniaguev, Noam Pnueli, Idan Segev, Michael London, 2026
    biorxiv.org/content/10.64898/2

    All about dendrites. Extending prior work by Beniaguev et al. 2021 and Jones and Kording 2021.

    #neuroscience #CompNeurosci

  5. Dendrites are what machine learning is missing. (And inhibitory neurons, but hey, one at a time.)

    "What can a neuron compute", by Ido Aizenbud, David Beniaguev, Noam Pnueli, Idan Segev, Michael London, 2026
    biorxiv.org/content/10.64898/2

    All about dendrites. Extending prior work by Beniaguev et al. 2021 and Jones and Kording 2021.

    #neuroscience #CompNeurosci

  6. Junior Theoretical Neuroscientists Workshop, at the Flatiron Institute in New York, in July 21st to 24th, 2026.

    simonsfoundation.org/event/jrw

    Apply until April 15th.

    "Admitted participants will be provided with travel, lodging, and meals for the duration of the workshop."

    #neuroscience #CompNeurosci

  7. Junior Theoretical Neuroscientists Workshop, at the Flatiron Institute in New York, in July 21st to 24th, 2026.

    simonsfoundation.org/event/jrw

    Apply until April 15th.

    "Admitted participants will be provided with travel, lodging, and meals for the duration of the workshop."

    #neuroscience #CompNeurosci

  8. Junior Theoretical Neuroscientists Workshop, at the Flatiron Institute in New York, in July 21st to 24th, 2026.

    simonsfoundation.org/event/jrw

    Apply until April 15th.

    "Admitted participants will be provided with travel, lodging, and meals for the duration of the workshop."

    #neuroscience #CompNeurosci

  9. Junior Theoretical Neuroscientists Workshop, at the Flatiron Institute in New York, in July 21st to 24th, 2026.

    simonsfoundation.org/event/jrw

    Apply until April 15th.

    "Admitted participants will be provided with travel, lodging, and meals for the duration of the workshop."

    #neuroscience #CompNeurosci

  10. Junior Theoretical Neuroscientists Workshop, at the Flatiron Institute in New York, in July 21st to 24th, 2026.

    simonsfoundation.org/event/jrw

    Apply until April 15th.

    "Admitted participants will be provided with travel, lodging, and meals for the duration of the workshop."

    #neuroscience #CompNeurosci

  11. "Sketch of a novel approach to a neural model", by Gabriele Scheler 2026.
    arxiv.org/abs/2209.06865

    "traditional synapse-centric, weight-based models of memorization are not sufficient or adequate to capture the real complexity of neuroplasticity. [...] We propose a paradigm switch from a synapse-centric model (each synapse learns independently, based on associative coupling) to a neuron-centric model (each neuron uses its intracellular pathways to express plasticity at its synapses and dendritic membrane)."

    #neuroscience #CompNeurosci

    1/2

  12. "Sketch of a novel approach to a neural model", by Gabriele Scheler 2026.
    arxiv.org/abs/2209.06865

    "traditional synapse-centric, weight-based models of memorization are not sufficient or adequate to capture the real complexity of neuroplasticity. [...] We propose a paradigm switch from a synapse-centric model (each synapse learns independently, based on associative coupling) to a neuron-centric model (each neuron uses its intracellular pathways to express plasticity at its synapses and dendritic membrane)."

    #neuroscience #CompNeurosci

    1/2

  13. "Sketch of a novel approach to a neural model", by Gabriele Scheler 2026.
    arxiv.org/abs/2209.06865

    "traditional synapse-centric, weight-based models of memorization are not sufficient or adequate to capture the real complexity of neuroplasticity. [...] We propose a paradigm switch from a synapse-centric model (each synapse learns independently, based on associative coupling) to a neuron-centric model (each neuron uses its intracellular pathways to express plasticity at its synapses and dendritic membrane)."

    #neuroscience #CompNeurosci

    1/2

  14. "Sketch of a novel approach to a neural model", by Gabriele Scheler 2026.
    arxiv.org/abs/2209.06865

    "traditional synapse-centric, weight-based models of memorization are not sufficient or adequate to capture the real complexity of neuroplasticity. [...] We propose a paradigm switch from a synapse-centric model (each synapse learns independently, based on associative coupling) to a neuron-centric model (each neuron uses its intracellular pathways to express plasticity at its synapses and dendritic membrane)."

    #neuroscience #CompNeurosci

    1/2

  15. "Sketch of a novel approach to a neural model", by Gabriele Scheler 2026.
    arxiv.org/abs/2209.06865

    "traditional synapse-centric, weight-based models of memorization are not sufficient or adequate to capture the real complexity of neuroplasticity. [...] We propose a paradigm switch from a synapse-centric model (each synapse learns independently, based on associative coupling) to a neuron-centric model (each neuron uses its intracellular pathways to express plasticity at its synapses and dendritic membrane)."

    #neuroscience #CompNeurosci

    1/2

  16. The lab of Mitya Chklovskii introduces the #ReSU: Rectified Spectral Units, as a replacement for ReLU.

    "A Network of Biologically Inspired Rectified Spectral Units (ReSUs) Learns Hierarchical Features Without Error Backpropagation", Qin et al. 2025
    arxiv.org/abs/2512.23146

    #neuroscience #CompNeurosci

  17. The lab of Mitya Chklovskii introduces the #ReSU: Rectified Spectral Units, as a replacement for ReLU.

    "A Network of Biologically Inspired Rectified Spectral Units (ReSUs) Learns Hierarchical Features Without Error Backpropagation", Qin et al. 2025
    arxiv.org/abs/2512.23146

    #neuroscience #CompNeurosci

  18. The lab of Mitya Chklovskii introduces the #ReSU: Rectified Spectral Units, as a replacement for ReLU.

    "A Network of Biologically Inspired Rectified Spectral Units (ReSUs) Learns Hierarchical Features Without Error Backpropagation", Qin et al. 2025
    arxiv.org/abs/2512.23146

    #neuroscience #CompNeurosci

  19. The lab of Mitya Chklovskii introduces the #ReSU: Rectified Spectral Units, as a replacement for ReLU.

    "A Network of Biologically Inspired Rectified Spectral Units (ReSUs) Learns Hierarchical Features Without Error Backpropagation", Qin et al. 2025
    arxiv.org/abs/2512.23146

    #neuroscience #CompNeurosci

  20. The lab of Mitya Chklovskii introduces the #ReSU: Rectified Spectral Units, as a replacement for ReLU.

    "A Network of Biologically Inspired Rectified Spectral Units (ReSUs) Learns Hierarchical Features Without Error Backpropagation", Qin et al. 2025
    arxiv.org/abs/2512.23146

    #neuroscience #CompNeurosci

  21. #Neuroscience as a field is relatively fragmented
    ..
    We propose leveraging shared #neurodata repositories and #compneurosci modelling frameworks to benchmark methodologies, facilitating a more coherent integration of findings across #neuro subfields.

    Additionally, we advocate for the creation of a structured “map” of neuroscience, charting relationships between domains to enhance conceptual clarity.

    doi.org/10.52294/001c.138841

    #philosophyofneuroscience #theoreticalneuroscience #neuropsy #cogsci

  22. #Neuroscience as a field is relatively fragmented
    ..
    We propose leveraging shared #neurodata repositories and #compneurosci modelling frameworks to benchmark methodologies, facilitating a more coherent integration of findings across #neuro subfields.

    Additionally, we advocate for the creation of a structured “map” of neuroscience, charting relationships between domains to enhance conceptual clarity.

    doi.org/10.52294/001c.138841

    #philosophyofneuroscience #theoreticalneuroscience #neuropsy #cogsci

  23. #Neuroscience as a field is relatively fragmented
    ..
    We propose leveraging shared #neurodata repositories and #compneurosci modelling frameworks to benchmark methodologies, facilitating a more coherent integration of findings across #neuro subfields.

    Additionally, we advocate for the creation of a structured “map” of neuroscience, charting relationships between domains to enhance conceptual clarity.

    doi.org/10.52294/001c.138841

    #philosophyofneuroscience #theoreticalneuroscience #neuropsy #cogsci

  24. #Neuroscience as a field is relatively fragmented
    ..
    We propose leveraging shared #neurodata repositories and #compneurosci modelling frameworks to benchmark methodologies, facilitating a more coherent integration of findings across #neuro subfields.

    Additionally, we advocate for the creation of a structured “map” of neuroscience, charting relationships between domains to enhance conceptual clarity.

    doi.org/10.52294/001c.138841

    #philosophyofneuroscience #theoreticalneuroscience #neuropsy #cogsci

  25. #Neuroscience as a field is relatively fragmented
    ..
    We propose leveraging shared #neurodata repositories and #compneurosci modelling frameworks to benchmark methodologies, facilitating a more coherent integration of findings across #neuro subfields.

    Additionally, we advocate for the creation of a structured “map” of neuroscience, charting relationships between domains to enhance conceptual clarity.

    doi.org/10.52294/001c.138841

    #philosophyofneuroscience #theoreticalneuroscience #neuropsy #cogsci

  26. The lab of Mitya Chklovskii is hiring, at the Flatiron Institute – Simons Foundation, in Manhattan, New York:
    apply.interfolio.com/173400

    #PhDJobs #neuroscience #CompNeurosci

  27. The lab of Mitya Chklovskii is hiring, at the Flatiron Institute – Simons Foundation, in Manhattan, New York:
    apply.interfolio.com/173400

    #PhDJobs #neuroscience #CompNeurosci

  28. The lab of Mitya Chklovskii is hiring, at the Flatiron Institute – Simons Foundation, in Manhattan, New York:
    apply.interfolio.com/173400

    #PhDJobs #neuroscience #CompNeurosci

  29. The lab of Mitya Chklovskii is hiring, at the Flatiron Institute – Simons Foundation, in Manhattan, New York:
    apply.interfolio.com/173400

    #PhDJobs #neuroscience #CompNeurosci

  30. The lab of Mitya Chklovskii is hiring, at the Flatiron Institute – Simons Foundation, in Manhattan, New York:
    apply.interfolio.com/173400

    #PhDJobs #neuroscience #CompNeurosci

  31. 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.

    New preprint from @yang_chu.

    arxiv.org/abs/2001.10605

    Thread below 👇

    #neuroscience #computationalneuroscience #compneuro #compneurosci

  32. 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.

    New preprint from @yang_chu.

    arxiv.org/abs/2001.10605

    Thread below 👇

    #neuroscience #computationalneuroscience #compneuro #compneurosci

  33. 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.

    New preprint from @yang_chu.

    arxiv.org/abs/2001.10605

    Thread below 👇

    #neuroscience #computationalneuroscience #compneuro #compneurosci

  34. 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.

    New preprint from @yang_chu.

    arxiv.org/abs/2001.10605

    Thread below 👇

    #neuroscience #computationalneuroscience #compneuro #compneurosci

  35. 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.

    New preprint from @yang_chu.

    arxiv.org/abs/2001.10605

    Thread below 👇

    #neuroscience #computationalneuroscience #compneuro #compneurosci

  36. Latest from Kathy Nagel's lab:

    "Inhibitory control explains locomotor statistics in walking Drosophila", Gattuso et al. 2025
    pnas.org/doi/abs/10.1073/pnas.

    "we measure and analyze trajectories evoked by attractive odor in walking Drosophila and develop a biologically plausible computational model of trajectory generation and modulation by sensory input. Our model provides a link between neural architectures and locomotor behavior and highlights the potential role of inhibition in shaping the curvature and speed of trajectories. Inspired by this model, we experimentally identify single neurons and populations that modulate either curvature or speed in the manner predicted by our model."

    #neuroscience #Drosophila #locomotion #CompNeurosci #SystemsNeuroscience

  37. Latest from Kathy Nagel's lab:

    "Inhibitory control explains locomotor statistics in walking Drosophila", Gattuso et al. 2025
    pnas.org/doi/abs/10.1073/pnas.

    "we measure and analyze trajectories evoked by attractive odor in walking Drosophila and develop a biologically plausible computational model of trajectory generation and modulation by sensory input. Our model provides a link between neural architectures and locomotor behavior and highlights the potential role of inhibition in shaping the curvature and speed of trajectories. Inspired by this model, we experimentally identify single neurons and populations that modulate either curvature or speed in the manner predicted by our model."

    #neuroscience #Drosophila #locomotion #CompNeurosci #SystemsNeuroscience

  38. Latest from Kathy Nagel's lab:

    "Inhibitory control explains locomotor statistics in walking Drosophila", Gattuso et al. 2025
    pnas.org/doi/abs/10.1073/pnas.

    "we measure and analyze trajectories evoked by attractive odor in walking Drosophila and develop a biologically plausible computational model of trajectory generation and modulation by sensory input. Our model provides a link between neural architectures and locomotor behavior and highlights the potential role of inhibition in shaping the curvature and speed of trajectories. Inspired by this model, we experimentally identify single neurons and populations that modulate either curvature or speed in the manner predicted by our model."

    #neuroscience #Drosophila #locomotion #CompNeurosci #SystemsNeuroscience

  39. Latest from Kathy Nagel's lab:

    "Inhibitory control explains locomotor statistics in walking Drosophila", Gattuso et al. 2025
    pnas.org/doi/abs/10.1073/pnas.

    "we measure and analyze trajectories evoked by attractive odor in walking Drosophila and develop a biologically plausible computational model of trajectory generation and modulation by sensory input. Our model provides a link between neural architectures and locomotor behavior and highlights the potential role of inhibition in shaping the curvature and speed of trajectories. Inspired by this model, we experimentally identify single neurons and populations that modulate either curvature or speed in the manner predicted by our model."

    #neuroscience #Drosophila #locomotion #CompNeurosci #SystemsNeuroscience

  40. Latest from Kathy Nagel's lab:

    "Inhibitory control explains locomotor statistics in walking Drosophila", Gattuso et al. 2025
    pnas.org/doi/abs/10.1073/pnas.

    "we measure and analyze trajectories evoked by attractive odor in walking Drosophila and develop a biologically plausible computational model of trajectory generation and modulation by sensory input. Our model provides a link between neural architectures and locomotor behavior and highlights the potential role of inhibition in shaping the curvature and speed of trajectories. Inspired by this model, we experimentally identify single neurons and populations that modulate either curvature or speed in the manner predicted by our model."

    #neuroscience #Drosophila #locomotion #CompNeurosci #SystemsNeuroscience