ArticlePloS one2013
Reliable neuronal systems: the importance of heterogeneity.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- A novel framework for expanding RNNs with biophysical detail to solve cognitive tasks.bioRxiv : the preprint server for biology · 2026Article
- Neural heterogeneity enhances reliable neural information processing: Local sensitivity and globally input-slaved transient dynamics.Science advances · 2025Article
- Neural heterogeneity as a unifying mechanism for efficient learning in spiking neural networks.Frontiers in computational neuroscience · 2025Article
- The effects of chronic, continuous β-funaltrexamine pre-treatment on lipopolysaccharide-induced inflammation and behavioral deficits in C57BL/6J mice.Journal of inflammation (London, England) · 2024Article
- Analyzing top-down visual attention in the context of gamma oscillations: a layer- dependent network-of- networks approach.Frontiers in computational neuroscience · 2024Article
- Bilateral widefield calcium imaging reveals circuit asymmetries and lateralized functional activation of the mouse auditory cortex.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Descending pathways increase sensory neural response heterogeneity to facilitate decoding and behavior.iScience · 2023Article
- Intrinsic neural diversity quenches the dynamic volatility of neural networks.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Extreme multistability and phase synchronization in a heterogeneous bi-neuron Rulkov network with memristive electromagnetic induction.Cognitive neurodynamics · 2023Article
- Efficient parameter calibration and real-time simulation of large-scale spiking neural networks with GeNN and NEST.Frontiers in neuroinformatics · 2023Article
- Distinctive biophysical features of human cell-types: insights from studies of neurosurgically resected brain tissue.Frontiers in synaptic neuroscience · 2023Review
- Neural heterogeneity promotes robust learning.Nature communications · 2021Article
- On the Use of a Multimodal Optimizer for Fitting Neuron Models. Application to the Cerebellar Granule Cell.Frontiers in neuroinformatics · 2021Article
- A Systematic Framework for Olfactory Bulb Signal Transformations.Frontiers in computational neuroscience · 2020Article
- Enabling Large-Scale Simulations With the GENESIS Neuronal Simulator.Frontiers in neuroinformatics · 2019Article
- Enhanced Peptide Detection Toward Single-Neuron Proteomics by Reversed-Phase Fractionation Capillary Electrophoresis Mass Spectrometry.Journal of the American Society for Mass Spectrometry · 2018Article
- Firing rate dynamics in recurrent spiking neural networks with intrinsic and network heterogeneity.Journal of computational neuroscience · 2015Article
- A dynamic architecture of life.F1000Research · 2015Review
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Authors and funding
3 authors.
Funding
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Abstract
For every engineer it goes without saying: in order to build a reliable system we need components that consistently behave precisely as they should. It is also well known that neurons, the building blocks of brains, do not satisfy this constraint. Even neurons of the same type come with huge variances in their properties and these properties also vary over time. Synapses, the connections between neurons, are highly unreliable in forwarding signals. In this paper we argue that both these fact add variance to neuronal processes, and that this variance is not a handicap of neural systems, but that instead predictable and reliable functional behavior of neural systems depends crucially on this variability. In particular, we show that higher variance allows a recurrently connected neural population to react more sensitively to incoming signals, and processes them faster and more energy efficient. This, for example, challenges the general assumption that the intrinsic variability of neurons in the brain is a defect that has to be overcome by synaptic plasticity in the process of learning.
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