HTAF is a sigmoid-tanh composite that approximates the Heaviside function to allow stable gradient training of binary activation networks, yielding ICBMs with stable discretization and competitive performance on image tasks.
Networks of spiking neurons: the third generation of neural network models
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ASTDP-GAD unifies spiking neural computation, STDP learning, and graph anomaly detection with claimed theoretical guarantees on encoding, convergence, and score calibration.
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A Composite Activation Function for Learning Stable Binary Representations
HTAF is a sigmoid-tanh composite that approximates the Heaviside function to allow stable gradient training of binary activation networks, yielding ICBMs with stable discretization and competitive performance on image tasks.
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Neuromorphic Graph Anomaly Detection via Adaptive STDP and Spiking Graph Neural Networks
ASTDP-GAD unifies spiking neural computation, STDP learning, and graph anomaly detection with claimed theoretical guarantees on encoding, convergence, and score calibration.