Lead federated neuromorphic learning for wireless edge artificial intelligence
In order to realize the full potential of wireless edge artificial intelligence (AI), very large and
diverse datasets will often be required for energy-demanding model training on resource …
diverse datasets will often be required for energy-demanding model training on resource …
Comparing SNNs and RNNs on neuromorphic vision datasets: Similarities and differences
Neuromorphic data, recording frameless spike events, have attracted considerable attention
for the spatiotemporal information components and the event-driven processing fashion …
for the spatiotemporal information components and the event-driven processing fashion …
Brain-inspired learning on neuromorphic substrates
Neuromorphic hardware strives to emulate brain-like neural networks and thus holds the
promise for scalable, low-power information processing on temporal data streams. Yet, to …
promise for scalable, low-power information processing on temporal data streams. Yet, to …
Deep spiking neural networks for large vocabulary automatic speech recognition
Artificial neural networks (ANN) have become the mainstream acoustic modeling technique
for large vocabulary automatic speech recognition (ASR). A conventional ANN features a …
for large vocabulary automatic speech recognition (ASR). A conventional ANN features a …
Snn and sound: a comprehensive review of spiking neural networks in sound
S Baek, J Lee - Biomedical Engineering Letters, 2024 - Springer
The rapid advancement of AI and machine learning has significantly enhanced sound and
acoustic recognition technologies, moving beyond traditional models to more sophisticated …
acoustic recognition technologies, moving beyond traditional models to more sophisticated …
Delay learning based on temporal coding in Spiking Neural Networks
Abstract Spiking Neural Networks (SNNs) hold great potential for mimicking the brain's
efficient processing of information. Although biological evidence suggests that precise spike …
efficient processing of information. Although biological evidence suggests that precise spike …
Neural Mode Estimation
Mode decomposition methods are the current workhorse for the analysis of non-stationary
signals. However, current attempts at these methods mainly focus on improving accuracy …
signals. However, current attempts at these methods mainly focus on improving accuracy …
Multi-tone phase coding of interaural time difference for sound source localization with spiking neural networks
Mammals exhibit remarkable capability of detecting and localizing sound sources in
complex acoustic environments by using binaural cues in the spiking manner. Emulating the …
complex acoustic environments by using binaural cues in the spiking manner. Emulating the …
Axonal delay as a short-term memory for feed forward deep spiking neural networks
The information of spiking neural networks (SNNs) are propagated between the adjacent
biological neuron by spikes, which provides a computing paradigm with the promise of …
biological neuron by spikes, which provides a computing paradigm with the promise of …
Fast texture classification using tactile neural coding and spiking neural network
Touch is arguably the most important sensing modality in physical interactions. However,
tactile sensing has been largely under-explored in robotics applications owing to the …
tactile sensing has been largely under-explored in robotics applications owing to the …