ArticleFrontiers in neuroscience2018
Neural and Synaptic Array Transceiver: A Brain-Inspired Computing Framework for Embedded Learning.
Article in Frontiers in neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Critically synchronized brain waves form an effective, robust and flexible basis for human memory and learning.Scientific reports · 2023Article
- A 22-pJ/spike 73-Mspikes/s 130k-compartment neural array transceiver with conductance-based synaptic and membrane dynamics.Frontiers in neuroscience · 2023Article
- SAM: A Unified Self-Adaptive Multicompartmental Spiking Neuron Model for Learning With Working Memory.Frontiers in neuroscience · 2022Article
- A Cost-Efficient High-Speed VLSI Architecture for Spiking Convolutional Neural Network Inference Using Time-Step Binary Spike Maps.Sensors (Basel, Switzerland) · 2021Article
- Learning Without Feedback: Fixed Random Learning Signals Allow for Feedforward Training of Deep Neural Networks.Frontiers in neuroscience · 2021Article
- Memory-Efficient Synaptic Connectivity for Spike-Timing- Dependent Plasticity.Frontiers in neuroscience · 2019Article
- Review
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Embedded, continual learning for autonomous and adaptive behavior is a key application of neuromorphic hardware. However, neuromorphic implementations of embedded learning at large scales that are both flexible and efficient have been hindered by a lack of a suitable algorithmic framework. As a result, most neuromorphic hardware are trained off-line on large clusters of dedicated processors or GPUs and transferred
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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.