ArticleScience advances2025
Neural heterogeneity enhances reliable neural information processing: Local sensitivity and globally input-slaved transient dynamics.
Article in Science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A Reconfigurable Memristive Spiking Neuron Enabling Advanced Neuromorphic Computing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Spatially structured heterogeneity shapes large-scale cortical dynamics in a model of the human cortex.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Representation Transfer via Invariant Input-driven Continuous Attractors for Fast Domain Adaptation.Communications biology · 2026Article
- Data-driven ANN-based visual decoding enables unsupervised functional alignment.Communications biology · 2026Article
- Exploring the singularity of human neurons: keep calm and carry on.Frontiers in synaptic neuroscience · 2025Review
- Neural heterogeneity as a unifying mechanism for efficient learning in spiking neural networks.Frontiers in computational neuroscience · 2025Article
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Authors and funding
6 authors.
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Abstract
Cortical neuronal activity varies over time and across repeated trials, yet consistently represents stimulus features. The dynamical mechanism underlying this reliable representation and computation remains elusive. This study uncovers a mechanism for reliable neural information processing, leveraging a biologically plausible network model incorporating neural heterogeneity. First, we investigate neuronal timescale diversity, revealing that it disrupts intrinsic coherent spatiotemporal patterns, induces firing rate heterogeneity, enhances local responsive sensitivity, and aligns network activity closely with input. The system exhibits globally input-slaved transient dynamics, essential for reliable neural information processing. Other neural heterogeneities, such as nonuniform input connections, spike threshold heterogeneity, and network in-degree heterogeneity, play similar roles, highlighting the importance of neural heterogeneity in shaping consistent stimulus representation. This mechanism offers a potentially general framework for understanding neural heterogeneity in reliable computation and informs the design of reservoir computing models endowed with liquid wave reservoirs for neuromorphic computing.
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Registered trials
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