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ARXIV:2605.12049 · NEURAL NETWORK ARCHITECTURES · SUBMITTED 13 MAY · 21:04 UTC · FRESHNESS STALE
ARXIV:2605.12049NEURAL NETWORK ARCHITECTURESSUBMITTED 13 MAY · 21:04 UTCFRESHNESS STALEAaron Spieler · Georg Martius · Anna Levina · arXiv
A new recurrent network architecture with expressive neurons explores the trade-offs between neuron complexity, width, and connectivity for sequence modeling.
Opportunity summary
Pain A new recurrent network architecture with expressive neurons explores the trade-offs between neuron complexity, width, and connectivity for sequence modeling.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A new recurrent network architecture with expressive neurons explores the trade-offs between neuron complexity, width, and connectivity for sequence modeling. Mainstream machine learning, by contrast, predominantly builds models from extremely simple units, a default…
Cortical neurons are complex, multi-timescale processors wired into recurrent circuits, shaped by long evolutionary pressure under stringent biological constraints. Mainstream machine learning, by contrast, predominantly builds models from extremely simple units, a default inherited…
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Performance improves monotonically along each of the three axes individually. Code availability is flagged in the production record; the public repository link still needs…
Neural Network Architectures moved forward this cycle; last verified May 2026. Public score 3.0/10. Production flags indicate code availability.
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A new recurrent network architecture with expressive neurons explores the trade-offs between neuron complexity, width, and connectivity for sequence modeling.
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10.48550/arXiv.2605.12049A new recurrent network architecture with expressive neurons explores the trade-offs between neuron complexity, width, and connectivity for sequence modeling.
Abstract
Cortical neurons are complex, multi-timescale processors wired into recurrent circuits, shaped by long evolutionary pressure under stringent biological constraints. Mainstream machine learning, by contrast, predominantly builds models from extremely simple units, a default inherited from early neural-network theory. We treat this as a normative architectural question. How should one split a fixed parameter budget $P$ between the number of units $N$, per-unit effective complexity $k_e$, and per-unit connectivity $k_c$? What controls the optimal allocation? This calls for a model in which per-unit complexity can be tuned independently of width and connectivity. Accordingly, we introduce the ELM Network, whose recurrent layer is built from Expressive Leaky Memory (ELM) neurons, chosen to mirror functional components of cortical neurons. The architecture allows for individually adjusting $N$, $k_e$, and $k_c$ and trains stably across orders of magnitude in scale. We evaluate the model on two qualitatively different sequence benchmarks: the neuromorphic SHD-Adding task and Enwik8 character-level language modeling. Performance improves monotonically along each of the three axes individually. Under a fixed budget, a clear non-trivial optimum emerges in their tradeoff, and larger budgets favor both more and more complex neurons. A closed-form information-theoretic model captures these tradeoffs and attributes the diminishing returns at two ends to: per-neuron signal-to-noise saturation and across-neuron redundancy. A hyperparameter sweep spanning three orders of magnitude in trainable parameters traces a near-Pareto-frontier scaling law consistent with the framework. This suggests that the simple-unit default in ML is not obviously optimal once this tradeoff surface is probed, and offers a normative lens on cortex's reliance on complex spatio-temporal integrators.
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PROBLEM
A new recurrent network architecture with expressive neurons explores the trade-offs between neuron complexity, width, and connectivity for sequence modeling. Mainstream machine learning, by contrast, predominantly builds models from extremely simple units, a default inherited f...
METHOD
Cortical neurons are complex, multi-timescale processors wired into recurrent circuits, shaped by long evolutionary pressure under stringent biological constraints. Mainstream machine learning, by contrast, predominantly builds models from extremely simple units, a default inher...
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Performance improves monotonically along each of the three axes individually. Code availability is flagged in the production record; the public repository link still needs proof alignment.
WHY NOW
Neural Network Architectures moved forward this cycle; last verified May 2026. Public score 3.0/10. Production flags indicate code availability.
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A new recurrent network architecture with expressive neurons explores the trade-offs between neuron complexity, width, and connectivity for sequence modeling. Mainstream machine learning, by contrast, predominantly builds models from extremely simple units, a default inherited from early neural-network theory.
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partial
Cortical neurons are complex, multi-timescale processors wired into recurrent circuits, shaped by long evolutionary pressure under stringent biological constraints. Mainstream machine learning, by contrast, predominantly builds models from extremely simple units, a default inherited from early neural-network theory.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Performance improves monotonically along each of the three axes individually. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Neural Network Architectures moved forward this cycle; last verified May 2026. Public score 3.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A new recurrent network architecture with expressive neurons explores the trade-offs between neuron complexity, width, and connectivity for sequence modeling.
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