ArticleNature communications2021
Neural heterogeneity promotes robust learning.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 102 papers.
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Who cites it
102 citing papers in PubMed.
- Review
- Temporal prediction captures retinal spiking responses across animal species.Nature communications · 2026Article
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- A Reconfigurable Memristive Spiking Neuron Enabling Advanced Neuromorphic Computing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Anion-Engineered Organic Electrochemical Transistors With Multi-Timescale Synaptic Dynamics for Task-Adaptive Spiking Neural Networks.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Article
- DuBois-Reymond prize lecture 2024: Multi-neuron patch-clamp to uncover the neuronal and synaptic physiology in the human neocortex.Pflugers Archiv : European journal of physiology · 2026Article
- Neural timescales from a computational perspective.Nature neuroscience · 2026Review
- Neuromorphic hierarchical modular reservoirs.Nature communications · 2026Article
- A computational theory of short-term synaptic plasticity: synapses learn to tell time.Research square · 2026Article
- On the molecular basis of enduring memory in neurons, and cell fate in fibroblasts.Nucleic acids research · 2026Review
- Glomerulus-Specific Inhomogeneity of the Basal Activity Map in the Olfactory Bulb.International journal of molecular sciences · 2026Article
- Label-Free Optical Differentiation of Single Diffusing Amino Acids at Picomolar Concentrations.Research square · 2026Article
- A novel framework for expanding RNNs with biophysical detail to solve cognitive tasks.bioRxiv : the preprint server for biology · 2026Article
- Electrophysiological classification of human layer 2-3 pyramidal neurons reveals subtype-specific synaptic interactions.Nature neuroscience · 2026Article
- Enhancing time scalability of spiking neural networks with dynamic time constants.Scientific reports · 2026Article
- Neuronal heterogeneity of normalization strength in a circuit model.Science advances · 2026Article
- Algorithm-hardware co-design of neuromorphic networks with dual memory pathways.Nature machine intelligence · 2026Article
- Conductance-based reversal potentials in spiking recurrent neural networks enhance energy efficiency and task performance.Frontiers in computational neuroscience · 2026Article
- Synaptic input variation enhances rate coding at the expense of temporal precision in cochlear nucleus neurons.PLoS biology · 2026Article
42 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The brain is a hugely diverse, heterogeneous structure. Whether or not heterogeneity at the neural level plays a functional role remains unclear, and has been relatively little explored in models which are often highly homogeneous. We compared the performance of spiking neural networks trained to carry out tasks of real-world difficulty, with varying degrees of heterogeneity, and found that heterogeneity substantially improved task performance. Learning with heterogeneity was more stable and robust, particularly for tasks with a rich temporal structure. In addition, the distribution of neuronal parameters in the trained networks is similar to those observed experimentally. We suggest that the heterogeneity observed in the brain may be more than just the byproduct of noisy processes, but rather may serve an active and important role in allowing animals to learn in changing environments.
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