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Unsupervised Learning with Self-Organizing Spiking Neural Networks
TLDR
A hybridization of self-organized map properties with spiking neural networks that retain many of the features of SOMs is presented, and using the optimal choice of parameters, this approach produces improvements over state-of-art spiking Neural networks. Expand
BindsNET: A Machine Learning-Oriented Spiking Neural Networks Library in Python
TLDR
It is argued that this package facilitates the use of spiking networks for large-scale machine learning problems and some simple examples by using BindsNET in practice are shown. Expand
Locally Connected Spiking Neural Networks for Unsupervised Feature Learning
TLDR
A method for learning image features with locally connected layers in SNNs using a spike-timing-dependent plasticity (STDP) rule, which has the advantage of fast convergence to a dataset representation, and they require fewer learnable parameters than other SNN approaches with unsupervised learning. Expand
Lattice map spiking neural networks (LM-SNNs) for clustering and classifying image data
TLDR
Spiking neural networks with a lattice architecture are introduced in this work, combining several desirable properties of SNNs and self-organized maps, including a population-level confidence rating, and an n-gram inspired method. Expand
STDP Learning of Image Patches with Convolutional Spiking Neural Networks
TLDR
A class of convolutional spiking neural networks is introduced, trained to detect image features with an unsupervised, competitive learning mechanism, and the time and memory requirements of learning with and operating such networks are analyzed. Expand
Minibatch Processing in Spiking Neural Networks
TLDR
To the knowledge, this is the first general-purpose implementation of mini-batch processing in a spiking neural networks simulator, which works with arbitrary neuron and synapse models and shows the effectiveness of large batch sizes in two SNN application domains. Expand
Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to Atari Breakout game
TLDR
This paper provides a proof of principle of the conversion of standard NN to SNN, and shows that the SNN has improved robustness to occlusion in the input image, paving the way for future research to robust Deep RL applications. Expand
Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to ATARI games
TLDR
This paper provides a proof of principle of the conversion of standard NN to SNN, and shows that the SNN has improved robustness to occlusion in the input image, paving the way for future research to robust Deep RL applications. Expand
Minibatch Processing for Speed-up and Scalability of Spiking Neural Network Simulation
TLDR
This work provides an implementation of mini-batch processing applied to clock-based SNN simulation, leading to drastically increased data throughput and different parameter reduction techniques are shown to produce different learning outcomes in a simulation of networks trained with spike-timing-dependent plasticity. Expand