aiqbench analysis
This arXiv preprint tests whether randomly varied neuronal membrane time constants, a task-agnostic form of "intrinsic heterogeneity," can improve recurrent neural networks across hundreds of temporal tasks. The reported gains are in robustness and in matching large recurrent networks with much smaller ones, which matters for efficiency-focused ML and neuromorphic hardware. Readers should weigh these as preprint benchmark results, with the energy and cost claims still needing validation on real hardware.
Robust and efficient enriched representation with intrinsic neuronal heterogeneity
arXiv:2412.05126v3 Announce Type: replace Abstract: Heterogeneity is a ubiquitous property of many biological systems with profound computational implications. Integrating this heterogeneity into artificial computing systems has recently gained attention. While optimizing neuronal and synaptic heterogeneity for a specific task can achieve state-of-the-art performance, such top-down optimization is biologically implausible, prone to catastrophic forgetting, and both data- and energy-intensive. In contrast, biological organisms possess inherent, task-unspecific, stable heterogeneity and can perform many tasks with minimal metabolic cost. Inspired by this, we study task-agnostic heterogeneity in recurrent neural networks solving hundreds of diverse temporal tasks. Heterogeneity is introduced as intrinsic disorder in neuronal membrane time constants. Our results show that intrinsic heterogeneity markedly improves overall performance and robustness in both rate- and spike-coded networks. Small heterogeneous networks, even without recurrence, match the performance of large, highly recurrent, homogeneous networks at a fraction of the cost and complexity. We suggest that, despite being task-agnostic, heterogeneity enhances representational capacity by diversifying neuronal responses, demonstrated on two machine-learning and one neuroscience benchmark. Our work highlights the utility of intrinsic task-agnostic heterogeneity for versatile, energy-efficient computing--a highly relevant property for the neuromorphic community, where device-to-device variability could be exploited to offset limited training budgets and architectural constraints.
Abstract:Heterogeneity is a ubiquitous property of many biological systems with profound computational implications. Integrating this heterogeneity into artificial computing systems has recently gained attention. While optimizing neuronal and synaptic heterogeneity for a specific task can achieve state-of-the-art performance, such top-down optimization is biologically implausible, prone to catastrophic forgetting, and both data- and energy-intensive. In contrast, biological organisms possess inherent, task-unspecific, stable heterogeneity and can perform many tasks with minimal metabolic cost. Inspired by this, we study task-agnostic heterogeneity in recurrent neural networks solving hundreds of diverse temporal tasks. Heterogeneity is introduced as intrinsic disorder in neuronal membrane time constants. Our results show that intrinsic heterogeneity markedly improves overall performance and robustness in both rate- and spike-coded networks. Small heterogeneous networks, even without recurrence, match the performance of large, highly recurrent, homogeneous networks at a fraction of the cost and complexity. We suggest that, despite being task-agnostic, heterogeneity enhances representational capacity by diversifying neuronal responses, demonstrated on two machine-learning and one neuroscience benchmark. Our work highlights the utility of intrinsic task-agnostic heterogeneity for versatile, energy-efficient computing--a highly relevant property for the neuromorphic community, where device-to-device variability could be exploited to offset limited training budgets and architectural constraints.
Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
arXiv:2412.05126 [cs.LG]
(or arXiv:2412.05126v3 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2412.05126 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 293 words
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