[HTML][HTML] Temporal dendritic heterogeneity incorporated with spiking neural networks for learning multi-timescale dynamics
It is widely believed the brain-inspired spiking neural networks have the capability of
processing temporal information owing to their dynamic attributes. However, how to …
processing temporal information owing to their dynamic attributes. However, how to …
Tc-lif: A two-compartment spiking neuron model for long-term sequential modelling
S Zhang, Q Yang, C Ma, J Wu, H Li… - Proceedings of the AAAI …, 2024 - ojs.aaai.org
The identification of sensory cues associated with potential opportunities and dangers is
frequently complicated by unrelated events that separate useful cues by long delays. As a …
frequently complicated by unrelated events that separate useful cues by long delays. As a …
Exploiting neuron and synapse filter dynamics in spatial temporal learning of deep spiking neural network
The recent discovered spatial-temporal information processing capability of bio-inspired
Spiking neural networks (SNN) has enabled some interesting models and applications …
Spiking neural networks (SNN) has enabled some interesting models and applications …
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 …
Deep CovDenseSNN: A hierarchical event-driven dynamic framework with spiking neurons in noisy environment
Neurons in the brain use an event signal, termed spike, encode temporal information for
neural computation. Spiking neural networks (SNNs) take this advantage to serve as …
neural computation. Spiking neural networks (SNNs) take this advantage to serve as …
[HTML][HTML] Stsc-snn: Spatio-temporal synaptic connection with temporal convolution and attention for spiking neural networks
Spiking neural networks (SNNs), as one of the algorithmic models in neuromorphic
computing, have gained a great deal of research attention owing to temporal information …
computing, have gained a great deal of research attention owing to temporal information …
[HTML][HTML] Impact of spiking neurons leakages and network recurrences on event-based spatio-temporal pattern recognition
Spiking neural networks coupled with neuromorphic hardware and event-based sensors are
getting increased interest for low-latency and low-power inference at the edge. However …
getting increased interest for low-latency and low-power inference at the edge. However …
Long short-term memory with two-compartment spiking neuron
The identification of sensory cues associated with potential opportunities and dangers is
frequently complicated by unrelated events that separate useful cues by long delays. As a …
frequently complicated by unrelated events that separate useful cues by long delays. As a …
Improving spiking dynamical networks: Accurate delays, higher-order synapses, and time cells
AR Voelker, C Eliasmith - Neural computation, 2018 - ieeexplore.ieee.org
Researchers building spiking neural networks face the challenge of improving the biological
plausibility of their model networks while maintaining the ability to quantitatively characterize …
plausibility of their model networks while maintaining the ability to quantitatively characterize …
Exploring loss functions for time-based training strategy in spiking neural networks
Abstract Spiking Neural Networks (SNNs) are considered promising brain-inspired energy-
efficient models due to their event-driven computing paradigm. The spatiotemporal spike …
efficient models due to their event-driven computing paradigm. The spatiotemporal spike …