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#biophysical — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #biophysical, aggregated by home.social.

  1. 🧠 New paper by Deistler et al: #JAXLEY: differentiable #simulation for large-scale training of detailed #biophysical #models of #NeuralDynamics.

    They present a #differentiable #GPU accelerated #simulator that trains #morphologically detailed biophysical #neuron models with #GradientDescent. JAXLEY fits intracellular #voltage and #calcium data, scales to 1000s of compartments, trains biophys. #RNNs on #WorkingMemory tasks & even solves #MNIST.

    🌍 doi.org/10.1038/s41592-025-028

    #Neuroscience #CompNeuro

  2. 🧠 New paper by Deistler et al: #JAXLEY: differentiable #simulation for large-scale training of detailed #biophysical #models of #NeuralDynamics.

    They present a #differentiable #GPU accelerated #simulator that trains #morphologically detailed biophysical #neuron models with #GradientDescent. JAXLEY fits intracellular #voltage and #calcium data, scales to 1000s of compartments, trains biophys. #RNNs on #WorkingMemory tasks & even solves #MNIST.

    🌍 doi.org/10.1038/s41592-025-028

    #Neuroscience #CompNeuro

  3. 🧠 New paper by Deistler et al: #JAXLEY: differentiable #simulation for large-scale training of detailed #biophysical #models of #NeuralDynamics.

    They present a #differentiable #GPU accelerated #simulator that trains #morphologically detailed biophysical #neuron models with #GradientDescent. JAXLEY fits intracellular #voltage and #calcium data, scales to 1000s of compartments, trains biophys. #RNNs on #WorkingMemory tasks & even solves #MNIST.

    🌍 doi.org/10.1038/s41592-025-028

    #Neuroscience #CompNeuro

  4. 🧠 New paper by Deistler et al: #JAXLEY: differentiable #simulation for large-scale training of detailed #biophysical #models of #NeuralDynamics.

    They present a #differentiable #GPU accelerated #simulator that trains #morphologically detailed biophysical #neuron models with #GradientDescent. JAXLEY fits intracellular #voltage and #calcium data, scales to 1000s of compartments, trains biophys. #RNNs on #WorkingMemory tasks & even solves #MNIST.

    🌍 doi.org/10.1038/s41592-025-028

    #Neuroscience #CompNeuro

  5. 🧠 New preprint by Chintaluri et al. (2025): An ion channel #omnimodel for standardized #biophysical #neuron #modelling. A unified #HodgkinHuxley formalism applied to >3,500 ion channel models from #ModelDB. Enables cross-model comparison, #clustering, and reproducible simulation through a shared parametrization:

    🌍 doi.org/10.1101/2025.10.03.680

    #CompNeuro #Neuroscience #Reproducibility

  6. Recently, we discussed this insightful paper by Squadrani et al (2024) in our #JournalClub. It explores how #astrocytes enhance #SynapticPlasticity during #ReversalLearning by modulating D-serine levels, providing a #biophysical basis for dynamic #LTP thresholds. The findings suggest astrocytic signaling is crucial for #AdaptiveLearning, linking #glial activity to #behavioral flexibility. Here’s a summary from our JC:

    🌍 fabriziomusacchio.com/blog/202
    📝 doi.org/10.1038/s42003-024-065

    #CompNeuro #Neuroscience