ArticleNeuron2023
The centrality of population-level factors to network computation is demonstrated by a versatile approach for training spiking networks.
Article in Neuron, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed.
- Making models disagree to learn how brains compute.Nature reviews. Neuroscience · 2026Review
- Sparse component analysis: A method that uncovers separable computations within neural population activity.Neuron · 2026Article
- Predictive pursuit emerges in high-dimensional recurrent neural networks.bioRxiv : the preprint server for biology · 2026Article
- Neural dynamics outside task-coding dimensions drive decision trajectories through transient amplification.bioRxiv : the preprint server for biology · 2025Article
- A doubly stochastic renewal framework for partitioning spiking variability.Nature communications · 2025Article
- Motor cortex flexibly deploys a high-dimensional repertoire of subskills.bioRxiv : the preprint server for biology · 2025Article
- A neural manifold view of the brain.Nature neuroscience · 2025Review
- Cooperative coding of continuous variables in networks with sparsity constraint.PLoS computational biology · 2025Article
- Control Principles of Neural Dynamics Revealed by the Neurobiology of Timing.Annual review of neuroscience · 2025Review
- Cortical sculpting of a rhythmic motor program.bioRxiv : the preprint server for biology · 2025Article
- High-dimensional neuronal activity from low-dimensional latent dynamics: a solvable model.bioRxiv : the preprint server for biology · 2025Article
- Flexible control of motor units: is the multidimensionality of motor unit manifolds a sufficient condition?The Journal of physiology · 2025Article
- Article
- Representation of a perceptual bias in the prefrontal cortex.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- The Problem-Ladenness of Theory.Computational brain & behavior · 2024Article
- Review
- Automated customization of large-scale spiking network models to neuronal population activity.Nature computational science · 2024Article
- Motor Cortex Latent Dynamics Encode Spatial and Temporal Arm Movement Parameters Independently.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Orthogonality of sensory and contextual categorical dynamics embedded in a continuum of responses from the second somatosensory cortex.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Motor cortex latent dynamics encode spatial and temporal arm movement parameters independently.bioRxiv : the preprint server for biology · 2024Article
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
Abstract
Neural activity is often described in terms of population-level factors extracted from the responses of many neurons. Factors provide a lower-dimensional description with the aim of shedding light on network computations. Yet, mechanistically, computations are performed not by continuously valued factors but by interactions among neurons that spike discretely and variably. Models provide a means of bridging these levels of description. We developed a general method for training model networks of spiking neurons by leveraging factors extracted from either data or firing-rate-based networks. In addition to providing a useful model-building framework, this formalism illustrates how reliable and continuously valued factors can arise from seemingly stochastic spiking. Our framework establishes procedures for embedding this property in network models with different levels of realism. The relationship between spikes and factors in such networks provides a foundation for interpreting (and subtly redefining) commonly used quantities such as firing rates.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.