Local information processing capacity: anatomy of computation in neuromorphic systems
Abstract
Many neuromorphic systems possess rich internal computational structures shaped by their physical dynamics. However, existing metrics characterize only global computation, leaving these local structures unresolved despite their importance for system interpretation, optimization and miniaturization. Here, we introduce local information processing capacity (L-IPC), which represents individual dynamical states as vectors in an infinite-dimensional Hilbert space and decomposes their computational properties through subspace projections onto nonlinearity orders and memory steps, with the residual quantifying computational irrelevance. Applied to three systems with comparable global capacities (an echo state network, an ion-gating reservoir and a spin-wave reservoir), L-IPC reveals markedly different distributions of computation, ranging from homogeneous to spatially, temporally and spectrally structured patterns. These local profiles further guide task-specific pruning, outperforming standard approaches at the same size in the structured systems. These results establish L-IPC as a general framework for anatomizing computation and guiding task-adaptive design in neuromorphic systems.
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BibTeX
@misc{chen2026local,
title = {{Local information processing capacity: anatomy of computation in neuromorphic systems}},
author = {Jiaxuan Chen and Daiki Nishioka and Yicheng Song and Ryo Iguchi and Sota Hikasa and Hina Kitano and Akira Hirose and Takashi Tsuchiya},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.05959},
eprint = {2611.05959},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.05959},
}
RIS
TY - UNPB
TI - Local information processing capacity: anatomy of computation in neuromorphic systems
AU - Jiaxuan Chen
AU - Daiki Nishioka
AU - Yicheng Song
AU - Ryo Iguchi
AU - Sota Hikasa
AU - Hina Kitano
AU - Akira Hirose
AU - Takashi Tsuchiya
PY - 2026
DA - 2026/11/04
PB - arXiv
DO - 10.48550/arxiv.2611.05959
UR - https://arxiv.org/abs/2611.05959
AB - Many neuromorphic systems possess rich internal computational structures shaped by their physical dynamics. However, existing metrics characterize only global computation, leaving these local structures unresolved despite their importance for system interpretation, optimization and miniaturization. Here, we introduce local information processing capacity (L-IPC), which represents individual dynamical states as vectors in an infinite-dimensional Hilbert space and decomposes their computational properties through subspace projections onto nonlinearity orders and memory steps, with the residual quantifying computational irrelevance. Applied to three systems with comparable global capacities (an echo state network, an ion-gating reservoir and a spin-wave reservoir), L-IPC reveals markedly different distributions of computation, ranging from homogeneous to spatially, temporally and spectrally structured patterns. These local profiles further guide task-specific pruning, outperforming standard approaches at the same size in the structured systems. These results establish L-IPC as a general framework for anatomizing computation and guiding task-adaptive design in neuromorphic systems.
ER -