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DL-ReSuMe: A Delay Learning-Based Remote Supervised Method for Spiking Neurons
TLDR
A learning method for spiking neurons, called delay learning remote supervised method (DL-ReSuMe), is proposed to merge the delay shift approach and ReSuMe-based weight adjustment to enhance the learning performance.
Adaptive Hidden Markov Model With Anomaly States for Price Manipulation Detection
TLDR
The evaluation experiments show that the proposed adaptive hidden Markov model with anomaly states (AHMMAS) model can effectively detect price manipulation patterns and outperforms the selected benchmark models.
A Novel Approach for the Implementation of Large Scale Spiking Neural Networks on FPGA Hardware
TLDR
FPGA implementation results demonstrate a performance increase over a PC based simulation and an alternative approach where a trade off in terms of speed/area is made and time multiplexing of the neuron model implemented on the FPGA is used to generate large network topologies.
SpikeTemp: An Enhanced Rank-Order-Based Learning Approach for Spiking Neural Networks With Adaptive Structure
TLDR
The results show that SpikeTemp can achieve better classification performance and is much faster than the existing rank-order-based learning approach, and the number of output neurons is much smaller when the square cosine encoding scheme is employed.
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