ArticleInterface focus2018
Spiking neural networks for computer vision.
Article in Interface focus, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed, 43 citations in OpenAlex.
- Bidirectional dynamic threshold SNN for enhanced object detection with rich spike information.Frontiers in neuroscience · 2025Article
- A spiking neural network for active efficient coding.Frontiers in robotics and AI · 2024Article
- Article
- Large-Scale Algorithmic Search Identifies Stiff and Sloppy Dimensions in Synaptic Architectures Consistent With Murine Neocortical Wiring.Neural computation · 2022Article
- Solving the spike feature information vanishing problem in spiking deep Q network with potential based normalization.Frontiers in neuroscience · 2022Article
- Long-range temporal correlations in scale-free neuromorphic networks.Network neuroscience (Cambridge, Mass.) · 2020Article
- Avalanches and criticality in self-organized nanoscale networks.Science advances · 2019Article
- Sepia, Tarsier, and Chameleon: A Modular C++ Framework for Event-Based Computer Vision.Frontiers in neuroscience · 2019Article
- Spiking neural networks for computer vision.Interface focus · 2018Article
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 at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
State-of-the-art computer vision systems use frame-based cameras that sample the visual scene as a series of high-resolution images. These are then processed using convolutional neural networks using neurons with continuous outputs. Biological vision systems use a quite different approach, where the eyes (cameras) sample the visual scene continuously, often with a non-uniform resolution, and generate neural spike events in response to changes in the scene. The resulting spatio-temporal patterns of events are then processed through networks of spiking neurons. Such event-based processing offers advantages in terms of focusing constrained resources on the most salient features of the perceived scene, and those advantages should also accrue to engineered vision systems based upon similar principles. Event-based vision sensors, and event-based processing exemplified by the SpiNNaker (Spiking Neural Network Architecture) machine, can be used to model the biological vision pathway at various levels of detail. Here we use this approach to explore structural synaptic plasticity as a possible mechanism whereby biological vision systems may learn the statistics of their inputs without supervision, pointing the way to engineered vision systems with similar online learning capabilities.
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.