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Found 8 papers, 1 papers with code
Stochastic Spiking Neural Networks with First-to-Spike Coding
Spiking Neural Networks (SNNs), recognized as the third generation of neural networks, are known for their bio-plausibility and energy efficiency, especially when implemented on neuromorphic hardware.
Benchmarking Spiking Neural Network Learning Methods with Varying Locality
However, training SNNs is challenging due to the non-differentiable nature of the spiking mechanism.
Deep Unsupervised Learning Using Spike-Timing-Dependent Plasticity
Spike-Timing-Dependent Plasticity (STDP) is an unsupervised learning mechanism for Spiking Neural Networks (SNNs) that has received significant attention from the neuromorphic hardware community.
On the Self-Repair Role of Astrocytes in STDP Enabled Unsupervised SNNs
Neuromorphic computing is emerging to be a disruptive computational paradigm that attempts to emulate various facets of the underlying structure and functionalities of the brain in the algorithm and hardware design of next-generation machine learning platforms.
Power System Disturbance Classification with Online Event-Driven Neuromorphic Computing
Accurate online classification of disturbance events in a transmission network is an important part of wide-area monitoring.
Exploring the Connection Between Binary and Spiking Neural Networks
On-chip edge intelligence has necessitated the exploration of algorithmic techniques to reduce the compute requirements of current machine learning frameworks.
Exploiting Oxide Based Resistive RAM Variability for Bayesian Neural Network Hardware Design
Uncertainty plays a key role in real-time machine learning.
All-Spin Bayesian Neural Networks
Probabilistic machine learning enabled by the Bayesian formulation has recently gained significant attention in the domain of automated reasoning and decision-making.
Emerging Technologies
