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Found 10 papers, 1 papers with code
Benchmarking Hebbian learning rules for associative memory
In this paper we characterize these different aspects of associative memory performance and benchmark six different learning rules on storage capacity and prototype extraction.
Spiking neural networks with Hebbian plasticity for unsupervised representation learning
We introduce a novel spiking neural network model for learning distributed internal representations from data in an unsupervised procedure.
Hebbian fast plasticity and working memory
Theories and models of working memory (WM) were at least since the mid-1990s dominated by the persistent activity hypothesis.
Brain-like combination of feedforward and recurrent network components achieves prototype extraction and robust pattern recognition
We approach this problem by combining a recurrent attractor network with a feedforward network that learns distributed representations using an unsupervised Hebbian-Bayesian learning rule.
Semi-supervised learning with Bayesian Confidence Propagation Neural Network
Learning internal representations from data using no or few labels is useful for machine learning research, as it allows using massive amounts of unlabeled data.
StreamBrain: An HPC Framework for Brain-like Neural Networks on CPUs, GPUs and FPGAs
The modern deep learning method based on backpropagation has surged in popularity and has been used in multiple domains and application areas.
Brain-like approaches to unsupervised learning of hidden representations - a comparative study
Unsupervised learning of hidden representations has been one of the most vibrant research directions in machine learning in recent years.
Brain-like approaches to unsupervised learning of hidden representations -- a comparative study
Unsupervised learning of hidden representations has been one of the most vibrant research directions in machine learning in recent years.
Learning representations in Bayesian Confidence Propagation neural networks
Unsupervised learning of hierarchical representations has been one of the most vibrant research directions in deep learning during recent years.
Characterization and Compensation of Network-Level Anomalies in Mixed-Signal Neuromorphic Modeling Platforms
Advancing the size and complexity of neural network models leads to an ever increasing demand for computational resources for their simulation.
