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#synaptic — Public Fediverse posts

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

  1. Was ich bei Fedora so vermisse ist ein passabler GUI-Paketmanager wie Synaptic unter Debian/Ubuntu. Damit bin ich viele Jahre "groß geworden", seit dem Umstieg auf Fedora (KDE Plasma) vermisse ich das am meisten.

    Discover finde ich mehr schlecht als recht, passt für Flatpaks und Updates aber sonst furchtbar.

    Dnfdragora, das eigentlich Synaptic recht nahe kommt, funktioniert die halbe Zeit nicht - instabil ohne Ende, entweder lädt es die Liste gar nicht oder stürzt ab, mal früher mal später. Und wenn es mal funktioniert zeigt es mir nicht an was schon installiert ist und was nicht.
    Selbiges auch wenn ich es aus der Quelle kompilliere. Damit wird es auch nicht besser. 🤷‍♂️

    #Fedora #FedoraLinux #Paketmanager #Synaptic #Dnfdragora #Linux

  2. When synaptic #plasticity depends on more than spike timing alone, the #ClopathRule offers a biologically plausible model incorporating postsynaptic voltage dynamics. This voltage based #STDP model captures #synaptic change features such as frequency dependence and homeostatic stabilization making it useful for simulating #learning and #memory in #NeuralNetworks. Here's a brief introduction to that rule and its applications in #CompNeuro:

    🌍 fabriziomusacchio.com/blog/202

    #Neuroscience

  3. When synaptic #plasticity depends on more than spike timing alone, the #ClopathRule offers a biologically plausible model incorporating postsynaptic voltage dynamics. This voltage based #STDP model captures #synaptic change features such as frequency dependence and homeostatic stabilization making it useful for simulating #learning and #memory in #NeuralNetworks. Here's a brief introduction to that rule and its applications in #CompNeuro:

    🌍 fabriziomusacchio.com/blog/202

    #Neuroscience

  4. When synaptic #plasticity depends on more than spike timing alone, the #ClopathRule offers a biologically plausible model incorporating postsynaptic voltage dynamics. This voltage based #STDP model captures #synaptic change features such as frequency dependence and homeostatic stabilization making it useful for simulating #learning and #memory in #NeuralNetworks. Here's a brief introduction to that rule and its applications in #CompNeuro:

    🌍 fabriziomusacchio.com/blog/202

    #Neuroscience

  5. When synaptic #plasticity depends on more than spike timing alone, the #ClopathRule offers a biologically plausible model incorporating postsynaptic voltage dynamics. This voltage based #STDP model captures #synaptic change features such as frequency dependence and homeostatic stabilization making it useful for simulating #learning and #memory in #NeuralNetworks. Here's a brief introduction to that rule and its applications in #CompNeuro:

    🌍 fabriziomusacchio.com/blog/202

    #Neuroscience

  6. 🧠 New publication from our lab 🥳! Nala ( @MaFu55 Lab, @dzne) & Naya (Corentin Le Magueresse Lab) show that #schizophrenia-associated complement C4 impairs #synaptic connectivity and alters #microglia–synapse interactions via #CR3 signaling.

    📝 doi.org/10.1016/j.celrep.2026.

    #Neuroscience 🧪 #2pImaging

  7. Different setup for a bit due to home repairs, but the work continues.

    New Roll Out just landed.

    This week: an egg in every basket, how choice and flexibility build resilience in software and business.

    Plus:
    - Digital sovereignty
    - Basket map exercise (1 main lane, 2 support lanes)
    - It’s FOSS: Ubuntu hate, and a modern Synaptic-style package manager.

    Read: rolandixor.pro/blog/post/the-r

    #adaptability #strategy #business #ubuntu #open-source #opensource #synaptic #debian #linux

  8. Spike-timing-dependent #plasticity (#STDP) is a core rule in #ComputationalNeuroscience that adjusts #synaptic strength based on precise pre- vs. postsynaptic #spike timing, enabling #TemporalCoding and #learning in #SNN. In this post, I summarize its mathematical formulation, functional consequences for learning and #memory along with a simple #Python example:

    🌍 fabriziomusacchio.com/blog/202

    #CompNeuro #Neuroscience #SNN #NeuralDynamics #NeuralPlasticity

  9. Spike-timing-dependent #plasticity (#STDP) is a core rule in #ComputationalNeuroscience that adjusts #synaptic strength based on precise pre- vs. postsynaptic #spike timing, enabling #TemporalCoding and #learning in #SNN. In this post, I summarize its mathematical formulation, functional consequences for learning and #memory along with a simple #Python example:

    🌍 fabriziomusacchio.com/blog/202

    #CompNeuro #Neuroscience #SNN #NeuralDynamics #NeuralPlasticity

  10. Spike-timing-dependent #plasticity (#STDP) is a core rule in #ComputationalNeuroscience that adjusts #synaptic strength based on precise pre- vs. postsynaptic #spike timing, enabling #TemporalCoding and #learning in #SNN. In this post, I summarize its mathematical formulation, functional consequences for learning and #memory along with a simple #Python example:

    🌍 fabriziomusacchio.com/blog/202

    #CompNeuro #Neuroscience #SNN #NeuralDynamics #NeuralPlasticity

  11. Spike-timing-dependent #plasticity (#STDP) is a core rule in #ComputationalNeuroscience that adjusts #synaptic strength based on precise pre- vs. postsynaptic #spike timing, enabling #TemporalCoding and #learning in #SNN. In this post, I summarize its mathematical formulation, functional consequences for learning and #memory along with a simple #Python example:

    🌍 fabriziomusacchio.com/blog/202

    #CompNeuro #Neuroscience #SNN #NeuralDynamics #NeuralPlasticity

  12. Spike-timing-dependent #plasticity (#STDP) is a core rule in #ComputationalNeuroscience that adjusts #synaptic strength based on precise pre- vs. postsynaptic #spike timing, enabling #TemporalCoding and #learning in #SNN. In this post, I summarize its mathematical formulation, functional consequences for learning and #memory along with a simple #Python example:

    🌍 fabriziomusacchio.com/blog/202

    #CompNeuro #Neuroscience #SNN #NeuralDynamics #NeuralPlasticity

  13. Incorporating structural #plasticity in #SpikingNeuralNetworks (#SNN) enables dynamic #synaptic connectivity, reflecting the #brain's adaptability. By modeling synaptic growth and pruning based on #calcium concentration, we can simulate processes such as #learning and #MemoryFormation. In this post, I reproduce the #NESTSimulator tutorial on structural plasticity, demonstrating its impact on network stability and #homeostasis:

    🌍 fabriziomusacchio.com/blog/202

    #CompNeuro #Neuroscience #NeuralNetworks

  14. Incorporating structural #plasticity in #SpikingNeuralNetworks (#SNN) enables dynamic #synaptic connectivity, reflecting the #brain's adaptability. By modeling synaptic growth and pruning based on #calcium concentration, we can simulate processes such as #learning and #MemoryFormation. In this post, I reproduce the #NESTSimulator tutorial on structural plasticity, demonstrating its impact on network stability and #homeostasis:

    🌍 fabriziomusacchio.com/blog/202

    #CompNeuro #Neuroscience #NeuralNetworks

  15. Incorporating structural #plasticity in #SpikingNeuralNetworks (#SNN) enables dynamic #synaptic connectivity, reflecting the #brain's adaptability. By modeling synaptic growth and pruning based on #calcium concentration, we can simulate processes such as #learning and #MemoryFormation. In this post, I reproduce the #NESTSimulator tutorial on structural plasticity, demonstrating its impact on network stability and #homeostasis:

    🌍 fabriziomusacchio.com/blog/202

    #CompNeuro #Neuroscience #NeuralNetworks

  16. Incorporating structural #plasticity in #SpikingNeuralNetworks (#SNN) enables dynamic #synaptic connectivity, reflecting the #brain's adaptability. By modeling synaptic growth and pruning based on #calcium concentration, we can simulate processes such as #learning and #MemoryFormation. In this post, I reproduce the #NESTSimulator tutorial on structural plasticity, demonstrating its impact on network stability and #homeostasis:

    🌍 fabriziomusacchio.com/blog/202

    #CompNeuro #Neuroscience #NeuralNetworks

  17. Incorporating structural #plasticity in #SpikingNeuralNetworks (#SNN) enables dynamic #synaptic connectivity, reflecting the #brain's adaptability. By modeling synaptic growth and pruning based on #calcium concentration, we can simulate processes such as #learning and #MemoryFormation. In this post, I reproduce the #NESTSimulator tutorial on structural plasticity, demonstrating its impact on network stability and #homeostasis:

    🌍 fabriziomusacchio.com/blog/202

    #CompNeuro #Neuroscience #NeuralNetworks

  18. 🧠 New preprint by Zhong et al. proposes a #synaptic mechanism for #chunking in #WorkingMemory.

    Using short-term #plasticity and synaptic augmentation, their model shows how items can be temporarily suppressed and later retrieved as chunks, increasing effective capacity w/o increasing simultaneous activity.

    🌍 doi.org/10.7554/eLife.109538.1

    #Neuroscience #CompNeuro #SynapticPlasticity

  19. 🧠 New pre-print by Wiesner et al. (2025) shows non-#synaptic #exocytosis directly from the #axon shaft, regulated by the submembrane periodic skeleton. Using #superresolution #imaging and live assays (#HiLo (VAMP2-pHluorin), #SIM, and correlative two-color #SMLM/ #STORM) they reveal that #axons can release vesicles outside classical #synapses, expanding how we understand #neuronal communication and #AxonalSignaling.

    🌍 doi.org/10.1101/2025.09.17.676

    #Neuroscience

  20. There will be a talk by Peter Jonas (ISTAustria) on "#Synaptic mechanisms of #patterncompletion in the #hippocampal #CA3 region":

    ⏰ Thu July 27 at 4.15pm CEST
    🌎 t.co/tlk4CorzwE
    📍 University of #Tübingen, Hertie-Institut für klinische Hirnforschung (HIH), Host: Ulrike Hedrich

    #neuroscience

  21. Gradient-adjusted Incremental Target Propagation Provides Effective Credit Assignment in Deep Neu...

    Sander Dalm, Nasir Ahmad, Luca Ambrogioni, Marcel van Gerven

    openreview.net/forum?id=Lx19Ey

    #backpropagation #synaptic #trained