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Found 5 papers, 2 papers with code
Expanding memory in recurrent spiking networks
Recurrent spiking neural networks (RSNNs) are notoriously difficult to train because of the vanishing gradient problem that is enhanced by the binary nature of the spikes.
Voltage-Dependent Synaptic Plasticity (VDSP): Unsupervised probabilistic Hebbian plasticity rule based on neurons membrane potential
To validate the system-level performance of VDSP, we train a single-layer spiking neural network (SNN) for the recognition of handwritten digits.
Signals to Spikes for Neuromorphic Regulated Reservoir Computing and EMG Hand Gesture Recognition
Using a simple machine learning algorithm after spike encoding, we report performance higher than the state-of-the-art spiking neural networks on two open-source datasets for hand gesture recognition.
Improving Zero-Shot Neural Architecture Search with Parameters Scoring
The performance of a network in a task can be predicted by a score, even before the network is trained: this is referred to as zero-shot NAS.
P-CRITICAL: A Reservoir Autoregulation Plasticity Rule for Neuromorphic Hardware
Backpropagation algorithms on recurrent artificial neural networks require an unfolding of accumulated states over time.
