ArticleFrontiers in neuroscience2016
Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines.
Article in Frontiers in neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 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
25 citing papers in PubMed.
- Synaptic Synchronization-Based Learning of Pattern Separation in Self-Organizing Probabilistic Spiking Neural Networks.bioRxiv : the preprint server for biology · 2025Article
- Synapses learn to utilize stochastic pre-synaptic release for the prediction of postsynaptic dynamics.PLoS computational biology · 2024Article
- Synchronization of Complex Dynamical Networks with Stochastic Links Dynamics.Entropy (Basel, Switzerland) · 2023Article
- Coherent noise enables probabilistic sequence replay in spiking neuronal networks.PLoS computational biology · 2023Article
- Sharing leaky-integrate-and-fire neurons for memory-efficient spiking neural networks.Frontiers in neuroscience · 2023Article
- Neural sampling machine with stochastic synapse allows brain-like learning and inference.Nature communications · 2022Article
- Phase-change memtransistive synapses for mixed-plasticity neural computations.Nature nanotechnology · 2022Article
- Probabilistic Spike Propagation for Efficient Hardware Implementation of Spiking Neural Networks.Frontiers in neuroscience · 2021Article
- Effects of synaptic integration on the dynamics and computational performance of spiking neural network.Cognitive neurodynamics · 2020Article
- Biologically Plausible Class Discrimination Based Recurrent Neural Network Training for Motor Pattern Generation.Frontiers in neuroscience · 2020Article
- Deterministic networks for probabilistic computing.Scientific reports · 2019Article
- ConvPath: A software tool for lung adenocarcinoma digital pathological image analysis aided by a convolutional neural network.EBioMedicine · 2019Article
- Versatile stochastic dot product circuits based on nonvolatile memories for high performance neurocomputing and neurooptimization.Nature communications · 2019Article
- Using Memristors for Robust Local Learning of Hardware Restricted Boltzmann Machines.Scientific reports · 2019Article
- Analysis of Liquid Ensembles for Enhancing the Performance and Accuracy of Liquid State Machines.Frontiers in neuroscience · 2019Article
- Review
- On Practical Issues for Stochastic STDP Hardware With 1-bit Synaptic Weights.Frontiers in neuroscience · 2018Article
- Ultralow power artificial synapses using nanotextured magnetic Josephson junctions.Science advances · 2018Article
- Neural and Synaptic Array Transceiver: A Brain-Inspired Computing Framework for Embedded Learning.Frontiers in neuroscience · 2018Article
- Large-Scale Neuromorphic Spiking Array Processors: A Quest to Mimic the Brain.Frontiers in neuroscience · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent studies have shown that synaptic unreliability is a robust and sufficient mechanism for inducing the stochasticity observed in cortex. Here, we introduce Synaptic Sampling Machines (S2Ms), a class of neural network models that uses synaptic stochasticity as a means to Monte Carlo sampling and unsupervised learning. Similar to the original formulation of Boltzmann machines, these models can be viewed as a stochastic counterpart of Hopfield networks, but where stochasticity is induced by a random mask over the connections. Synaptic stochasticity plays the dual role of an efficient mechanism for sampling, and a regularizer during learning akin to DropConnect. A local synaptic plasticity rule implementing an event-driven form of contrastive divergence enables the learning of generative models in an on-line fashion. S2Ms perform equally well using discrete-timed artificial units (as in Hopfield networks) or continuous-timed leaky integrate and fire neurons. The learned representations are remarkably sparse and robust to reductions in bit precision and synapse pruning: removal of more than 75% of the weakest connections followed by cursory re-learning causes a negligible performance loss on benchmark classification tasks. The spiking neuron-based S2Ms outperform existing spike-based unsupervised learners, while potentially offering substantial advantages in terms of power and complexity, and are thus promising models for on-line learning in brain-inspired hardware.
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.