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Where generalization capacity--the ability to extract context-invariant relational structures--first emerges remains a central question in AI and neuroscience. The foundation for this capacity lies up...
arXiv:2610.07962v1 Announce Type: cross Abstract: Where generalization capacity--the ability to extract context-invariant relational structures--first emerges remains a central question in AI and neu...
arXiv:2608. 06963v1 Announce Type: new Abstract: Biologically plausible learning models aim to explain how neural circuits can implement effective learning under the constraints of real neurons.
BrainDyn is a graph ordinary differential equation (ODE) network that models brain activity as coordinated patterns across connected regions. It uses a cellular sheaf with dynamic restriction maps to mediate region-to-region communication through learned linear transformations, rather than the standard shared feature space and scalar summation of message‑passing networks. Evaluated on resting‑state fMRI, EEG, and simulated NEST data, BrainDyn outperforms competing models in multi‑region forecasting, autoregressive generation of brain transients, and prediction of altered transients after controlled perturbations, while its learned features are expressive, interpretable, and biologically aligned.
arXiv:2606. 31700v1 Announce Type: new Abstract: Biological neural circuits obey Dale's principle: each neuron's synapses are uniformly excitatory or inhibitory.
arXiv:2511. 14555v4 Announce Type: replace-cross Abstract: Decoded Neurofeedback (DecNef) is a promising non-invasive approach to brain modulation with wide-ranging applications in neuromedicine and cognitive neuroscience.