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Recent advances and new frontiers in spiking neural networks
In recent years, spiking neural networks (SNNs) have received extensive attention in brain-
inspired intelligence due to their rich spatially-temporal dynamics, various encoding …
inspired intelligence due to their rich spatially-temporal dynamics, various encoding …
Self-backpropagation of synaptic modifications elevates the efficiency of spiking and artificial neural networks
Many synaptic plasticity rules found in natural circuits have not been incorporated into
artificial neural networks (ANNs). We showed that incorporating a nonlocal feature of …
artificial neural networks (ANNs). We showed that incorporating a nonlocal feature of …
[HTML][HTML] Braincog: A spiking neural network based, brain-inspired cognitive intelligence engine for brain-inspired ai and brain simulation
Spiking neural networks (SNNs) serve as a promising computational framework for
integrating insights from the brain into artificial intelligence (AI). Existing software …
integrating insights from the brain into artificial intelligence (AI). Existing software …
GLSNN: A multi-layer spiking neural network based on global feedback alignment and local STDP plasticity
Spiking Neural Networks (SNNs) are considered as the third generation of artificial neural
networks, which are more closely with information processing in biological brains. However …
networks, which are more closely with information processing in biological brains. However …
Research advances and new paradigms for biology-inspired spiking neural networks
Spiking neural networks (SNNs) are gaining popularity in the computational simulation and
artificial intelligence fields owing to their biological plausibility and computational efficiency …
artificial intelligence fields owing to their biological plausibility and computational efficiency …
Tuning convolutional spiking neural network with biologically plausible reward propagation
Spiking neural networks (SNNs) contain more biologically realistic structures and
biologically inspired learning principles than those in standard artificial neural networks …
biologically inspired learning principles than those in standard artificial neural networks …
Increasing liquid state machine performance with edge-of-chaos dynamics organized by astrocyte-modulated plasticity
The liquid state machine (LSM) combines low training complexity and biological plausibility,
which has made it an attractive machine learning framework for edge and neuromorphic …
which has made it an attractive machine learning framework for edge and neuromorphic …
[HTML][HTML] Backeisnn: A deep spiking neural network with adaptive self-feedback and balanced excitatory–inhibitory neurons
Spiking neural networks (SNNs) transmit information through discrete spikes that perform
well in processing spatial–temporal information. Owing to their nondifferentiable …
well in processing spatial–temporal information. Owing to their nondifferentiable …
Meta neurons improve spiking neural networks for efficient spatio-temporal learning
Spiking neural networks (SNNs) have incorporated many biologically-plausible structures
and learning principles, and hence are playing critical roles in bridging the gap between …
and learning principles, and hence are playing critical roles in bridging the gap between …
Spike calibration: Fast and accurate conversion of spiking neural network for object detection and segmentation
Spiking neural network (SNN) has been attached to great importance due to the properties
of high biological plausibility and low energy consumption on neuromorphic hardware. As …
of high biological plausibility and low energy consumption on neuromorphic hardware. As …