ArticlePLoS computational biology2017
Robust information propagation through noisy neural circuits.
Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed, 67 citations in OpenAlex.
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- Measuring Stimulus Information Transfer Between Neural Populations Through the Communication Subspace.Neural computation · 2025Article
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- A neuromorphic multi-scale approach for real-time heart rate and state detection.Npj unconventional computing · 2025Article
- The geometry of correlated variability leads to highly suboptimal discriminative sensory coding.Journal of neurophysiology · 2025Article
- Measuring Stimulus Information Transfer Between Neural Populations through the Communication Subspace.bioRxiv : the preprint server for biology · 2024Article
- Spiking networks that efficiently process dynamic sensory features explain receptor information mixing in somatosensory cortex.bioRxiv : the preprint server for biology · 2024Article
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- Simultaneous, cortex-wide and cellular-resolution neuronal population dynamics reveal an unbounded scaling of dimensionality with neuron number.bioRxiv : the preprint server for biology · 2024Article
- Propagation of activity through the cortical hierarchy and perception are determined by neural variability.Nature neuroscience · 2023Article
- Information-Theoretic Approaches in EEG Correlates of Auditory Perceptual Awareness under Informational Masking.Biology · 2023Article
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- Computational methods to study information processing in neural circuits.Computational and structural biotechnology journal · 2023Review
- The structures and functions of correlations in neural population codes.Nature reviews. Neuroscience · 2022Review
- Internally generated population activity in cortical networks hinders information transmission.Science advances · 2022Article
Corrections and comments
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Authors and funding
4 authors at 4 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sensory neurons give highly variable responses to stimulation, which can limit the amount of stimulus information available to downstream circuits. Much work has investigated the factors that affect the amount of information encoded in these population responses, leading to insights about the role of covariability among neurons, tuning curve shape, etc. However, the informativeness of neural responses is not the only relevant feature of population codes; of potentially equal importance is how robustly that information propagates to downstream structures. For instance, to quantify the retina's performance, one must consider not only the informativeness of the optic nerve responses, but also the amount of information that survives the spike-generating nonlinearity and noise corruption in the next stage of processing, the lateral geniculate nucleus. Our study identifies the set of covariance structures for the upstream cells that optimize the ability of information to propagate through noisy, nonlinear circuits. Within this optimal family are covariances with "differential correlations", which are known to reduce the information encoded in neural population activities. Thus, covariance structures that maximize information in neural population codes, and those that maximize the ability of this information to propagate, can be very different. Moreover, redundancy is neither necessary nor sufficient to make population codes robust against corruption by noise: redundant codes can be very fragile, and synergistic codes can-in some cases-optimize robustness against noise.
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