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Random matrix theory applied to sparse neuronal networks with heterogeneous timescales

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a random matrix ensemble to analyze the Jacobian matrices of sparse neuronal networks with heterogeneous timescales. This ensemble accurately captures the spectra of trained Jacobian matrices, which are characterized by a specific inhibitory core-excitatory periphery motif. The study utilizes statistical field theory and supersymmetry methods to provide an analytic description of the spectral edge, linking network parameters like sparsity and weight variances to critical features essential for robust working memory computation. AI

IMPACT Provides theoretical insights into the dynamics of complex neuronal networks, potentially informing future AI architectures.

RANK_REASON Academic paper published on arXiv detailing theoretical advancements in understanding neuronal networks. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Random matrix theory applied to sparse neuronal networks with heterogeneous timescales

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Academic paper published on arXiv detailing theoretical advancements in understanding neuronal networks. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Thiparat Chotibut, Oleg Evnin, Weerawit Horinouchi ·

    Random matrix theory of sparse neuronal networks with heterogeneous timescales

    arXiv:2512.12767v2 Announce Type: replace-cross Abstract: Training recurrent neuronal networks consisting of excitatory (E) and inhibitory (I) units with additive noise for working memory computation slows and diversifies inhibitory timescales, leading to improved task performanc…

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