Disordered neural circuits with circular symmetry can function as continuous attractors, as shown by data-driven recurrent networks modeling rodent head-direction cells.
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Neural Networks and Applications
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Optimally designed perturbation inputs improve neural system identification accuracy by substantially outperforming conventional, intuition-based inputs in simulations.
- Open access
INSPIRE achieves in situ photonic neural network training with a relative error of 0.26%, enabling efficient, adaptive optical learning systems.
Humans prefer simpler explanations for uncertain data, aligning with formal model selection theories that penalize complexity, with a significant influence on decision-making.
- Open access
- 21 cites
Larger transformer-based language models more accurately predict neural activity, with performance peaking in earlier layers as model size increases, across brain regions.
- Open access
Hierarchical neural circuit theory reveals that feedback enhances inter-areal communication while reducing within-area interactions, with spectral signatures matching experimental data.
- Open access
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