ReviewiScience2018
Data and Power Efficient Intelligence with Neuromorphic Learning Machines.
Review in iScience, 2018. 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, 52 citations in OpenAlex.
- Perception, Synaptic Plasticity, and Spiking Neuron Function Enabled by a 2D Ferroelectric NbOBrAdvanced materials (Deerfield Beach, Fla.) · 2026Article
- Neuromorphic-inspired multi-view global-local fusion for IR-UWB radar dynamic gesture recognition.Frontiers in neuroscience · 2026Article
- Energy-adaptive resistive switching with controllable thresholds in insulator-metal transition.RSC advances · 2022Article
- The Remarkable Robustness of Surrogate Gradient Learning for Instilling Complex Function in Spiking Neural Networks.Neural computation · 2021Article
- Learning automata based energy-efficient AI hardware design for IoT applications.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2020Article
- Neural network design for energy-autonomous artificial intelligence applications using temporal encoding.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2020Article
- Technique Advances in Enteroatmospheric Fistula Isolation After Open Abdomen: A Review and Outlook.Frontiers in surgery · 2020Review
- Multi-context blind source separation by error-gated Hebbian rule.Scientific reports · 2019Article
- Direct Feedback Alignment With Sparse Connections for Local Learning.Frontiers in neuroscience · 2019Article
- Making BREAD: Biomimetic Strategies for Artificial Intelligence Now and in the Future.Frontiers in neuroscience · 2019Review
- Theoretical Models of Neural Development.iScience · 2018Review
- Neural and Synaptic Array Transceiver: A Brain-Inspired Computing Framework for Embedded Learning.Frontiers in neuroscience · 2018Article
- Organizing Sequential Memory in a Neuromorphic Device Using Dynamic Neural Fields.Frontiers in neuroscience · 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
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The success of deep networks and recent industry involvement in brain-inspired computing is igniting a widespread interest in neuromorphic hardware that emulates the biological processes of the brain on an electronic substrate. This review explores interdisciplinary approaches anchored in machine learning theory that enable the applicability of neuromorphic technologies to real-world, human-centric tasks. We find that (1) recent work in binary deep networks and approximate gradient descent learning are strikingly compatible with a neuromorphic substrate; (2) where real-time adaptability and autonomy are necessary, neuromorphic technologies can achieve significant advantages over main-stream ones; and (3) challenges in memory technologies, compounded by a tradition of bottom-up approaches in the field, block the road to major breakthroughs. We suggest that a neuromorphic learning framework, tuned specifically for the spatial and temporal constraints of the neuromorphic substrate, will help guiding hardware algorithm co-design and deploying neuromorphic hardware for proactive learning of real-world data.
Indexed as
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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.