ArticleeLife2022
The geometry of robustness in spiking neural networks.
Article in eLife, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed.
- Spiking neurons as predictive controllers of linear systems.PLoS computational biology · 2026Article
- Task success in trained spiking neural network models coincides with emergence of cross-stimulus-modulated inhibition.Biological cybernetics · 2026Article
- Design principles for cost functions in optimal sensory coding.Frontiers in computational neuroscience · 2026Review
- Three types of remapping with linear decoders: A population-geometric perspective.PLoS computational biology · 2025Article
- Three types of remapping with linear decoders: a population-geometric perspective.bioRxiv : the preprint server for biology · 2025Article
- Representational drift without synaptic plasticity.bioRxiv : the preprint server for biology · 2025Article
- Article
- Efficient coding in biophysically realistic excitatory-inhibitory spiking networks.bioRxiv : the preprint server for biology · 2025Article
- The impact of spike timing precision and spike emission reliability on decoding accuracy.Scientific reports · 2024Article
- From Brain Models to Robotic Embodied Cognition: How Does Biological Plausibility Inform Neuromorphic Systems?Brain sciences · 2023Review
- Increments in visual motion coherence are more readily detected than decrements.Journal of vision · 2023Article
- Computational methods to study information processing in neural circuits.Computational and structural biotechnology journal · 2023Review
- Natural gradient enables fast sampling in spiking neural networks.Advances in neural information processing systems · 2022Article
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
Neural systems are remarkably robust against various perturbations, a phenomenon that still requires a clear explanation. Here, we graphically illustrate how neural networks can become robust. We study spiking networks that generate low-dimensional representations, and we show that the neurons' subthreshold voltages are confined to a convex region in a lower-dimensional voltage subspace, which we call a 'bounding box'. Any changes in network parameters (such as number of neurons, dimensionality of inputs, firing thresholds, synaptic weights, or transmission delays) can all be understood as deformations of this bounding box. Using these insights, we show that functionality is preserved as long as perturbations do not destroy the integrity of the bounding box. We suggest that the principles underlying robustness in these networks - low-dimensional representations, heterogeneity of tuning, and precise negative feedback - may be key to understanding the robustness of neural systems at the circuit level.
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.