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Found 9 papers, 2 papers with code
Embedded Encoder-Decoder in Convolutional Networks Towards Explainable AI
This paper proposes a new explainable convolutional neural network (XCNN) which represents important and driving visual features of stimuli in an end-to-end model architecture.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
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Deep Learning in Spiking Neural Networks
In this approach, a deep (multilayer) artificial neural network (ANN) is trained in a supervised manner using backpropagation.
BP-STDP: Approximating Backpropagation using Spike Timing Dependent Plasticity
This approach enjoys benefits of both accurate gradient descent and temporally local, efficient STDP.
Representation Learning using Event-based STDP
Although representation learning methods developed within the framework of traditional neural networks are relatively mature, developing a spiking representation model remains a challenging problem.
Bio-Inspired Multi-Layer Spiking Neural Network Extracts Discriminative Features from Speech Signals
This paper develops a bio-inspired SNN that uses unsupervised learning to extract discriminative features from speech signals, which can subsequently be used in a classifier.
Bio-Inspired Spiking Convolutional Neural Network using Layer-wise Sparse Coding and STDP Learning
Kernels for the convolutional layer are trained using local learning.
Acquisition of Visual Features Through Probabilistic Spike-Timing-Dependent Plasticity
The original model showed that a spike-timing-dependent plasticity (STDP) learning algorithm embedded in an appropriately selected SCN could perform unsupervised feature discovery.
A Spiking Network that Learns to Extract Spike Signatures from Speech Signals
Spiking neural networks (SNNs) with adaptive synapses reflect core properties of biological neural networks.
Training a Hidden Markov Model with a Bayesian Spiking Neural Network
The emission (observation) probabilities of the HMM are represented in the SNN and trained with the STDP rule.
