ReviewCommunications engineering2025
Neuromorphic computing for robotic vision: algorithms to hardware advances.
Review in Communications engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Two-Dimensional Indium Selenide for Next-Generation Electronics.International journal of molecular sciences · 2026Review
- System-level FPGA validation of a trainable and robust multiplier-free spiking neural network.iScience · 2026Article
- Topology-aware adaptive scheduling algorithm for heterogeneous AI-PC collaborative computing environments.Scientific reports · 2026Article
- Development of digital hardware for a spiking image recognition network employing a novel burst-based reinforcement learning approach.Scientific reports · 2025Article
- Hybrid GNN-LSTM defense with differential privacy and secure multi-party computation for edge-optimized neuromorphic autonomous systems.Scientific reports · 2025Article
- Marine-Inspired Multimodal Sensor Fusion and Neuromorphic Processing for Autonomous Navigation in Unstructured Subaquatic Environments.Sensors (Basel, Switzerland) · 2025Review
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
Neuromorphic computing offers transformative potential for AI in resource-constrained environments by mimicking biological neural efficiency. This perspective article analyzes recent advances and future directions, advocating a system design approach that integrates specialized sensing (e.g., event-based cameras), brain-inspired algorithms (SNNs and SNN-ANN hybrids), and dedicated neuromorphic hardware. Using vision-based drone navigation (VDN) as an exemplar-drawing parallels with biological systems like Drosophila-we demonstrate how these components enable event-driven processing and overcome von Neumann architecture limitations through near-/in-memory computing. Key challenges include large-scale integration, benchmarking standardization, and algorithm-hardware co-design for emerging applications, which we discuss alongside current and future research directions.
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.