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Found 6 papers, 1 papers with code
A Neuromorphic Architecture for Reinforcement Learning from Real-Valued Observations
Reinforcement Learning (RL) provides a powerful framework for decision-making in complex environments.
Efficient Implementation of a Multi-Layer Gradient-Free Online-Trainable Spiking Neural Network on FPGA
This paper presents an efficient hardware implementation of the recently proposed Optimized Deep Event-driven Spiking Neural Network Architecture (ODESA).
Event-driven Spectrotemporal Feature Extraction and Classification using a Silicon Cochlea Model
This paper presents a reconfigurable digital implementation of an event-based binaural cochlear system on a Field Programmable Gate Array (FPGA).
An Optimized Deep Spiking Neural Network Architecture Without Gradients
Through the use of simple local adaptive selection thresholds at each node, the network rapidly learns to appropriately allocate its neuronal resources at each layer for any given problem without using an error measure.
An optimised deep spiking neural network architecture without gradients
Through the use of simple local adaptive selection thresholds at each node, the network rapidly learns to appropriately allocate its neuronal resources at each layer for any given problem without using a real-valued error measure.
A Compressive Sensing Video dataset using Pixel-wise coded exposure
The first one is sparsity which requires the signal to be sparse in some domain.
