ArticleProceedings of the National Academy of Sciences of the United States of America2013
Intermediate intrinsic diversity enhances neural population coding.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 80 papers.
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Who cites it
80 citing papers in PubMed.
- Anti-NMDAR encephalitis impairs intrinsic hippocampal dynamics through neuronal hypercoupling, hub dominance, and aberrant ensembles.Molecular psychiatry · 2026Article
- Synaptic input variation enhances rate coding at the expense of temporal precision in cochlear nucleus neurons.PLoS biology · 2026Article
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- Concept transfer of synaptic diversity from biological to artificial neural networks.Nature communications · 2025Article
- Neural heterogeneity enhances reliable neural information processing: Local sensitivity and globally input-slaved transient dynamics.Science advances · 2025Article
- Ex Vivo Functional Characterization of Mouse Olfactory Bulb Projection Neurons Reveals a Heterogeneous Continuum.eNeuro · 2025Article
- Biologically inspired heterogeneous learning for accurate, efficient and low-latency neural network.National science review · 2025Article
- Quantitative analyses of how optimally heterogeneous neural codes maximize neural information in jittery transmission environments.Scientific reports · 2024Article
- Neural populations within macaque early vestibular pathways are adapted to encode natural self-motion.PLoS biology · 2024Article
- Article
- Neural heterogeneity controls computations in spiking neural networks.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Brain-inspired modular echo state network for EEG-based emotion recognition.Frontiers in neuroscience · 2024Article
- Controlling morpho-electrophysiological variability of neurons with detailed biophysical models.iScience · 2023Article
- Identification of interacting neural populations: methods and statistical considerations.Journal of neurophysiology · 2023Review
- Neuronal diversity can improve machine learning for physics and beyond.Scientific reports · 2023Article
- Adult-born granule cells improve stimulus encoding and discrimination in the dentate gyrus.eLife · 2023Article
- Article
- 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
- Degeneracy in epilepsy: multiple routes to hyperexcitable brain circuits and their repair.Communications biology · 2023Review
20 more citing papers are in PubMed but not listed here.
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4 authors.
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Abstract
Cell-to-cell variability in molecular, genetic, and physiological features is increasingly recognized as a critical feature of complex biological systems, including the brain. Although such variability has potential advantages in robustness and reliability, how and why biological circuits assemble heterogeneous cells into functional groups is poorly understood. Here, we develop analytic approaches toward answering how neuron-level variation in intrinsic biophysical properties of olfactory bulb mitral cells influences population coding of fluctuating stimuli. We capture the intrinsic diversity of recorded populations of neurons through a statistical approach based on generalized linear models. These models are flexible enough to predict the diverse responses of individual neurons yet provide a common reference frame for comparing one neuron to the next. We then use Bayesian stimulus decoding to ask how effectively different populations of mitral cells, varying in their diversity, encode a common stimulus. We show that a key advantage provided by physiological levels of intrinsic diversity is more efficient and more robust encoding of stimuli by the population as a whole. However, we find that the populations that best encode stimulus features are not simply the most heterogeneous, but those that balance diversity with the benefits of neural similarity.
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