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#attractors — Public Fediverse posts

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

  1. 🧠⭕️ New preprint by Hulse et al: How can the #fly’s neural compass show #RingAttractor dynamics despite heterogeneous #connectomic wiring? They combine task-trained #RNNs, theory and fly #connectomes to show that “dynamical clones” can embed hidden symmetries in apparently messy connectivity, preserving stable head-direction #attractors and angular velocity integration.

    🌍 doi.org/10.64898/2026.05.18.72

    #Neuroscience #CompNeuro #NeuralDynamics #NeuralManifolds #RingAttractor

  2. 🧠⭕️ New preprint by Hulse et al: How can the #fly’s neural compass show #RingAttractor dynamics despite heterogeneous #connectomic wiring? They combine task-trained #RNNs, theory and fly #connectomes to show that “dynamical clones” can embed hidden symmetries in apparently messy connectivity, preserving stable head-direction #attractors and angular velocity integration.

    🌍 doi.org/10.64898/2026.05.18.72

    #Neuroscience #CompNeuro #NeuralDynamics #NeuralManifolds #RingAttractor

  3. 🧠⭕️ New preprint by Hulse et al: How can the #fly’s neural compass show #RingAttractor dynamics despite heterogeneous #connectomic wiring? They combine task-trained #RNNs, theory and fly #connectomes to show that “dynamical clones” can embed hidden symmetries in apparently messy connectivity, preserving stable head-direction #attractors and angular velocity integration.

    🌍 doi.org/10.64898/2026.05.18.72

    #Neuroscience #CompNeuro #NeuralDynamics #NeuralManifolds #RingAttractor

  4. 🧠⭕️ New preprint by Hulse et al: How can the #fly’s neural compass show #RingAttractor dynamics despite heterogeneous #connectomic wiring? They combine task-trained #RNNs, theory and fly #connectomes to show that “dynamical clones” can embed hidden symmetries in apparently messy connectivity, preserving stable head-direction #attractors and angular velocity integration.

    🌍 doi.org/10.64898/2026.05.18.72

    #Neuroscience #CompNeuro #NeuralDynamics #NeuralManifolds #RingAttractor

  5. 🧠⭕️ New preprint by Hulse et al: How can the #fly’s neural compass show #RingAttractor dynamics despite heterogeneous #connectomic wiring? They combine task-trained #RNNs, theory and fly #connectomes to show that “dynamical clones” can embed hidden symmetries in apparently messy connectivity, preserving stable head-direction #attractors and angular velocity integration.

    🌍 doi.org/10.64898/2026.05.18.72

    #Neuroscience #CompNeuro #NeuralDynamics #NeuralManifolds #RingAttractor

  6. #NeuralPlasticity & #learning are distinct but interrelated processes. #Plasticity denotes biological change in #NeuralSystems, while learning is its functional expression in #NetworkDynamics & #behavior. Learning arises from coordinated plastic processes, reshaping #NeuralStateSpace & #attractors to support stable yet flexible representations. Here's a new post on these concepts & their implications for #ComputationalNeuroscience:

    🌍fabriziomusacchio.com/blog/202

    #CompNeuro #Neuroscience

  7. #NeuralPlasticity & #learning are distinct but interrelated processes. #Plasticity denotes biological change in #NeuralSystems, while learning is its functional expression in #NetworkDynamics & #behavior. Learning arises from coordinated plastic processes, reshaping #NeuralStateSpace & #attractors to support stable yet flexible representations. Here's a new post on these concepts & their implications for #ComputationalNeuroscience:

    🌍fabriziomusacchio.com/blog/202

    #CompNeuro #Neuroscience

  8. #NeuralPlasticity & #learning are distinct but interrelated processes. #Plasticity denotes biological change in #NeuralSystems, while learning is its functional expression in #NetworkDynamics & #behavior. Learning arises from coordinated plastic processes, reshaping #NeuralStateSpace & #attractors to support stable yet flexible representations. Here's a new post on these concepts & their implications for #ComputationalNeuroscience:

    🌍fabriziomusacchio.com/blog/202

    #CompNeuro #Neuroscience

  9. #NeuralPlasticity & #learning are distinct but interrelated processes. #Plasticity denotes biological change in #NeuralSystems, while learning is its functional expression in #NetworkDynamics & #behavior. Learning arises from coordinated plastic processes, reshaping #NeuralStateSpace & #attractors to support stable yet flexible representations. Here's a new post on these concepts & their implications for #ComputationalNeuroscience:

    🌍fabriziomusacchio.com/blog/202

    #CompNeuro #Neuroscience

  10. #NeuralPlasticity & #learning are distinct but interrelated processes. #Plasticity denotes biological change in #NeuralSystems, while learning is its functional expression in #NetworkDynamics & #behavior. Learning arises from coordinated plastic processes, reshaping #NeuralStateSpace & #attractors to support stable yet flexible representations. Here's a new post on these concepts & their implications for #ComputationalNeuroscience:

    🌍fabriziomusacchio.com/blog/202

    #CompNeuro #Neuroscience

  11. @axoaxonic @adredish Fully agree 👍 Horner's framework really begs for a formal dynamical model: defining trajectories, #attractors, and #manifolds within that 3D space. Something that could turn his conceptual #StateSpace into a genuine #computational theory of #memory dynamics.

    I didn’t know Redish's book ("Beyond the Cognitive Map") before your comment! Sounds highly relevant and I’ll definitely put it on my reading list 👌

  12. @axoaxonic @adredish Fully agree 👍 Horner's framework really begs for a formal dynamical model: defining trajectories, #attractors, and #manifolds within that 3D space. Something that could turn his conceptual #StateSpace into a genuine #computational theory of #memory dynamics.

    I didn’t know Redish's book ("Beyond the Cognitive Map") before your comment! Sounds highly relevant and I’ll definitely put it on my reading list 👌

  13. I'm doing an essay on asynchronous Kauffman boolean networks (using N=9, K=3). I seem to not be able to locate any attractors, at least not anything that I can recognise (i.e. a specific set of states that when they appear the system will stay in these states or cycle through them). Also there is nothing consisten when starting from the same initial condition. Am I missing something here?

    Thanks for the help in advance!!

    #networks #BooleanNetworks #asynchronousnetworks #attractors

  14. (4) How explore ideas without idealism? My approach to #ideologies focuses on #attractors deployed by powerful actors, whether political leaders, influencers, or scholars such as #brunolatour (from Down to Earth, 2018).

  15. (4) How explore ideas without idealism? My approach to #ideologies focuses on #attractors deployed by powerful actors, whether political leaders, influencers, or scholars such as #brunolatour (from Down to Earth, 2018).