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#anns — Public Fediverse posts

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  1. "We demonstrate that multilayer ANNs comprised of separate excitatory and inhibitory cell types, and neuronal units with separate dendrite compartments, can be trained to accurately classify images using a fully biology-compatible deep learning algorithm called dendritic target propagation."

    "Cellular and subcellular specialization enables biology-constrained deep learning", Galloni et al. 2026 (Aaron Milstein's lab).
    sciencedirect.com/science/arti

    #neuroscience #BioinspiredComputing #ANNs

  2. "We demonstrate that multilayer ANNs comprised of separate excitatory and inhibitory cell types, and neuronal units with separate dendrite compartments, can be trained to accurately classify images using a fully biology-compatible deep learning algorithm called dendritic target propagation."

    "Cellular and subcellular specialization enables biology-constrained deep learning", Galloni et al. 2026 (Aaron Milstein's lab).
    sciencedirect.com/science/arti

    #neuroscience #BioinspiredComputing #ANNs

  3. "We demonstrate that multilayer ANNs comprised of separate excitatory and inhibitory cell types, and neuronal units with separate dendrite compartments, can be trained to accurately classify images using a fully biology-compatible deep learning algorithm called dendritic target propagation."

    "Cellular and subcellular specialization enables biology-constrained deep learning", Galloni et al. 2026 (Aaron Milstein's lab).
    sciencedirect.com/science/arti

    #neuroscience #BioinspiredComputing #ANNs

  4. "We demonstrate that multilayer ANNs comprised of separate excitatory and inhibitory cell types, and neuronal units with separate dendrite compartments, can be trained to accurately classify images using a fully biology-compatible deep learning algorithm called dendritic target propagation."

    "Cellular and subcellular specialization enables biology-constrained deep learning", Galloni et al. 2026 (Aaron Milstein's lab).
    sciencedirect.com/science/arti

    #neuroscience #BioinspiredComputing #ANNs

  5. "We demonstrate that multilayer ANNs comprised of separate excitatory and inhibitory cell types, and neuronal units with separate dendrite compartments, can be trained to accurately classify images using a fully biology-compatible deep learning algorithm called dendritic target propagation."

    "Cellular and subcellular specialization enables biology-constrained deep learning", Galloni et al. 2026 (Aaron Milstein's lab).
    sciencedirect.com/science/arti

    #neuroscience #BioinspiredComputing #ANNs

  6. In pyramidal neuron we trust 🧠💡
    Paper shows that dendritic inspired by connectivity reduce overfitting, use fewer parameters, and outperform traditional ANNs in image classification!
    going towards or towards cognitive ? 🔥👇
    nature.com/articles/s41467-025

  7. In pyramidal neuron we trust 🧠💡
    Paper shows that dendritic #ANNs inspired by #brain connectivity reduce overfitting, use fewer parameters, and outperform traditional ANNs in image classification!
    going towards #AI or towards cognitive #neuroscience? 🔥👇
    nature.com/articles/s41467-025

  8. In pyramidal neuron we trust 🧠💡
    Paper shows that dendritic #ANNs inspired by #brain connectivity reduce overfitting, use fewer parameters, and outperform traditional ANNs in image classification!
    going towards #AI or towards cognitive #neuroscience? 🔥👇
    nature.com/articles/s41467-025

  9. In pyramidal neuron we trust 🧠💡
    Paper shows that dendritic #ANNs inspired by #brain connectivity reduce overfitting, use fewer parameters, and outperform traditional ANNs in image classification!
    going towards #AI or towards cognitive #neuroscience? 🔥👇
    nature.com/articles/s41467-025

  10. In pyramidal neuron we trust 🧠💡
    Paper shows that dendritic #ANNs inspired by #brain connectivity reduce overfitting, use fewer parameters, and outperform traditional ANNs in image classification!
    going towards #AI or towards cognitive #neuroscience? 🔥👇
    nature.com/articles/s41467-025

  11. “Just as #research has shown that feedback from caregivers shapes infant productions of sounds, feedback from the discriminator network shapes the sound productions of the generator network"

    This is a pretty big finding. If it's accurate, we can test theories of #language development using artificial neural networks (#ANNs) instead of having to scan human brains!

    #AI

    quantamagazine.org/some-neural

  12. “Just as #research has shown that feedback from caregivers shapes infant productions of sounds, feedback from the discriminator network shapes the sound productions of the generator network"

    This is a pretty big finding. If it's accurate, we can test theories of #language development using artificial neural networks (#ANNs) instead of having to scan human brains!

    #AI

    quantamagazine.org/some-neural

  13. “Just as #research has shown that feedback from caregivers shapes infant productions of sounds, feedback from the discriminator network shapes the sound productions of the generator network"

    This is a pretty big finding. If it's accurate, we can test theories of #language development using artificial neural networks (#ANNs) instead of having to scan human brains!

    #AI

    quantamagazine.org/some-neural

  14. “Just as #research has shown that feedback from caregivers shapes infant productions of sounds, feedback from the discriminator network shapes the sound productions of the generator network"

    This is a pretty big finding. If it's accurate, we can test theories of #language development using artificial neural networks (#ANNs) instead of having to scan human brains!

    #AI

    quantamagazine.org/some-neural

  15. “Just as #research has shown that feedback from caregivers shapes infant productions of sounds, feedback from the discriminator network shapes the sound productions of the generator network"

    This is a pretty big finding. If it's accurate, we can test theories of #language development using artificial neural networks (#ANNs) instead of having to scan human brains!

    #AI

    quantamagazine.org/some-neural

  16. i predict that when #experiments show that single celled #organisms like #bacteria and #yeast can outperform overparametrized machine learning models like #ANNs on the most challenging types of decision problems, #biology will provide a strong riposte to #computer science and the mindset that biological #intelligence is easily explainable using #computational #models

    #machineLearning #statistics #mathematical #models #ML #research #engineering #science #nature #complexity #theory #math #proof

  17. i predict that when #experiments show that single celled #organisms like #bacteria and #yeast can outperform overparametrized machine learning models like #ANNs on the most challenging types of decision problems, #biology will provide a strong riposte to #computer science and the mindset that biological #intelligence is easily explainable using #computational #models

    #machineLearning #statistics #mathematical #models #ML #research #engineering #science #nature #complexity #theory #math #proof

  18. i predict that when #experiments show that single celled #organisms like #bacteria and #yeast can outperform overparametrized machine learning models like #ANNs on the most challenging types of decision problems, #biology will provide a strong riposte to #computer science and the mindset that biological #intelligence is easily explainable using #computational #models

    #machineLearning #statistics #mathematical #models #ML #research #engineering #science #nature #complexity #theory #math #proof

  19. i predict that when #experiments show that single celled #organisms like #bacteria and #yeast can outperform overparametrized machine learning models like #ANNs on the most challenging types of decision problems, #biology will provide a strong riposte to #computer science and the mindset that biological #intelligence is easily explainable using #computational #models

    #machineLearning #statistics #mathematical #models #ML #research #engineering #science #nature #complexity #theory #math #proof