„Vor wenigen Wochen stellte ein chinesisches Forscherteam das Modell „SpikingBrain 1.0“ vor – eine #KI auf Basis eines #SpikingNeuralNetworks. Diese Technik soll nicht nur weniger Energie verbrauchen, sondern auch ohne Nvidia-Chips und ohne große Datenmengen auskommen.“🤔
Lina Knees via #Handelsblatt
Lina Knees via #Handelsblatt
October 10, 2025 at 4:34 AM
„Vor wenigen Wochen stellte ein chinesisches Forscherteam das Modell „SpikingBrain 1.0“ vor – eine #KI auf Basis eines #SpikingNeuralNetworks. Diese Technik soll nicht nur weniger Energie verbrauchen, sondern auch ohne Nvidia-Chips und ohne große Datenmengen auskommen.“🤔
Lina Knees via #Handelsblatt
Lina Knees via #Handelsblatt
A self‑distillation framework speeds SNN training and cuts memory use, achieving competitive accuracy on CIFAR‑10, CIFAR‑100 and ImageNet; code on GitHub. Read more: https://getnews.me/self-distillation-boosts-efficient-spiking-neural-network-training/ #spikingneuralnetworks #selfdistillation
October 9, 2025 at 10:32 PM
A self‑distillation framework speeds SNN training and cuts memory use, achieving competitive accuracy on CIFAR‑10, CIFAR‑100 and ImageNet; code on GitHub. Read more: https://getnews.me/self-distillation-boosts-efficient-spiking-neural-network-training/ #spikingneuralnetworks #selfdistillation
Study introduces S-SWIM, a random-feature training for spiking neural networks that needs only one data pass and matches state-of-the-art accuracy; submitted 1 Oct 2025. https://getnews.me/random-feature-spiking-neural-networks-offer-new-training-path/ #sswim #spikingneuralnetworks
October 3, 2025 at 2:55 AM
Study introduces S-SWIM, a random-feature training for spiking neural networks that needs only one data pass and matches state-of-the-art accuracy; submitted 1 Oct 2025. https://getnews.me/random-feature-spiking-neural-networks-offer-new-training-path/ #sswim #spikingneuralnetworks
SpikeSR with a Spiking Attention Block beat prior methods on AID, DOTA and DIOR benchmarks; the paper was accepted at a 2025 ML conference. Read more: https://getnews.me/spiking-neural-networks-with-attention-boost-remote-sensing-super-resolution/ #spikingneuralnetworks #attention #remotesensing
October 1, 2025 at 11:59 AM
SpikeSR with a Spiking Attention Block beat prior methods on AID, DOTA and DIOR benchmarks; the paper was accepted at a 2025 ML conference. Read more: https://getnews.me/spiking-neural-networks-with-attention-boost-remote-sensing-super-resolution/ #spikingneuralnetworks #attention #remotesensing
DelRec is a method that learns weights and axonal delays in spiking neural networks, reaching high accuracy on Speech Commands and Permuted Sequential MNIST. Code on GitHub. https://getnews.me/delrec-enables-learning-of-delays-in-recurrent-spiking-neural-networks/ #spikingneuralnetworks #delrec
October 1, 2025 at 12:30 AM
DelRec is a method that learns weights and axonal delays in spiking neural networks, reaching high accuracy on Speech Commands and Permuted Sequential MNIST. Code on GitHub. https://getnews.me/delrec-enables-learning-of-delays-in-recurrent-spiking-neural-networks/ #spikingneuralnetworks #delrec
Scientists introduced a hybrid ANN‑SNN model with a surrogate gradient for bit‑plane spike encoding, allowing end‑to‑end training. The pre‑print appeared in September 2025. Read more: https://getnews.me/hybrid-ann-snn-framework-uses-surrogate-spike-encoding/ #hybridannsn #spikingneuralnetworks
September 30, 2025 at 8:59 PM
Scientists introduced a hybrid ANN‑SNN model with a surrogate gradient for bit‑plane spike encoding, allowing end‑to‑end training. The pre‑print appeared in September 2025. Read more: https://getnews.me/hybrid-ann-snn-framework-uses-surrogate-spike-encoding/ #hybridannsn #spikingneuralnetworks
Researchers find spiking neural network designs produce sparse gradients, giving robustness without regularization, reducing generalization on clean data. Read more: https://getnews.me/spiking-neural-networks-naturally-sparse-gradients-enhance-robustness/ #spikingneuralnetworks #sparsity
September 30, 2025 at 1:49 PM
Researchers find spiking neural network designs produce sparse gradients, giving robustness without regularization, reducing generalization on clean data. Read more: https://getnews.me/spiking-neural-networks-naturally-sparse-gradients-enhance-robustness/ #spikingneuralnetworks #sparsity
Researchers proved that spiking neural networks can universally approximate any continuous function on compact domains, and the pre‑print was submitted on 26 Sep 2025. https://getnews.me/spiking-neural-networks-gain-universal-approximation-theory/ #spikingneuralnetworks #neuromorphic
September 29, 2025 at 5:52 PM
Researchers proved that spiking neural networks can universally approximate any continuous function on compact domains, and the pre‑print was submitted on 26 Sep 2025. https://getnews.me/spiking-neural-networks-gain-universal-approximation-theory/ #spikingneuralnetworks #neuromorphic
SpikeMatch, a technique for spiking neural networks, creates pseudo‑labels from weakly‑augmented data and improves results, keeping energy use low. Read more: https://getnews.me/spikematch-semi-supervised-learning-for-spiking-neural-networks/ #spikematch #spikingneuralnetworks
September 29, 2025 at 4:31 PM
SpikeMatch, a technique for spiking neural networks, creates pseudo‑labels from weakly‑augmented data and improves results, keeping energy use low. Read more: https://getnews.me/spikematch-semi-supervised-learning-for-spiking-neural-networks/ #spikematch #spikingneuralnetworks
Spiking neural networks classify mental workload with accuracy to traditional models while using less power, thanks to event‑driven processing and multimodal sensor data. https://getnews.me/spiking-neural-networks-enable-low-power-mental-workload-detection/ #spikingneuralnetworks #mentalworkload
September 29, 2025 at 4:17 AM
Spiking neural networks classify mental workload with accuracy to traditional models while using less power, thanks to event‑driven processing and multimodal sensor data. https://getnews.me/spiking-neural-networks-enable-low-power-mental-workload-detection/ #spikingneuralnetworks #mentalworkload
RPLIF raises a neuron's firing threshold after each spike, adding a refractory window. It records 82.40% accuracy on CIFAR‑10‑DVS and 97.22% on DVS128 Gesture. Read more: https://getnews.me/new-refractory-period-spiking-neural-network-boosts-ai-performance/ #spikingneuralnetworks #rplif
September 25, 2025 at 2:11 AM
RPLIF raises a neuron's firing threshold after each spike, adding a refractory window. It records 82.40% accuracy on CIFAR‑10‑DVS and 97.22% on DVS128 Gesture. Read more: https://getnews.me/new-refractory-period-spiking-neural-network-boosts-ai-performance/ #spikingneuralnetworks #rplif
SPACE is a source‑free test‑time adaptation for spiking neural networks that aligns feature maps across augmented views of a test sample, keeping low‑power operation. https://getnews.me/space-method-boosts-test-time-adaptation-for-spiking-neural-networks/ #spikingneuralnetworks #testtimeadaptation
September 22, 2025 at 10:20 PM
SPACE is a source‑free test‑time adaptation for spiking neural networks that aligns feature maps across augmented views of a test sample, keeping low‑power operation. https://getnews.me/space-method-boosts-test-time-adaptation-for-spiking-neural-networks/ #spikingneuralnetworks #testtimeadaptation
A new predictive spike‑timing algorithm enables spiking neural networks to compute shortest paths using only local neuron connections; the study was submitted on 12 September 2025. https://getnews.me/predictive-spike-timing-enables-shortest-path-computation-in-networks/ #spikingneuralnetworks
September 17, 2025 at 3:15 AM
A new predictive spike‑timing algorithm enables spiking neural networks to compute shortest paths using only local neuron connections; the study was submitted on 12 September 2025. https://getnews.me/predictive-spike-timing-enables-shortest-path-computation-in-networks/ #spikingneuralnetworks
Edge Intelligence with Spiking Neural Networks
Albert Y. Zomaya, Changze Lv et al.
Paper
Details
#EdgeAI #SpikingNeuralNetworks #ZomayaResearch
Albert Y. Zomaya, Changze Lv et al.
Paper
Details
#EdgeAI #SpikingNeuralNetworks #ZomayaResearch
July 22, 2025 at 4:03 PM
Edge Intelligence with Spiking Neural Networks
Albert Y. Zomaya, Changze Lv et al.
Paper
Details
#EdgeAI #SpikingNeuralNetworks #ZomayaResearch
Albert Y. Zomaya, Changze Lv et al.
Paper
Details
#EdgeAI #SpikingNeuralNetworks #ZomayaResearch
#Neuromonster #WolfgangMaass #NeuroAI #SpikingNeuralNetworks #AIandBrains #KeynoteSpeaker #NeuroscienceMeetsAI
April 23, 2025 at 10:03 AM
Researchers from Queensland University of Technology present an energy-efficient #placerecognition system leveraging Spiking Neural Networks with modularity and sequence matching to rival traditional deep networks
ieeexplore.ieee.org/document/107...
#SpikingNeuralNetworks #RobotPerception
ieeexplore.ieee.org/document/107...
#SpikingNeuralNetworks #RobotPerception
![Robot deployment feasibility study: Left: Real-time deployment of our Modular SNN in a small indoor environment on a CPU. First, the reference dataset is collected and the Modular SNN is trained offline. During inference, the robot moves through the environment, collecting images, and predicting their place labels in real time. The reference and query images are collected at different times of the day. The images were collected at 1 Hz with the robot moving at approximately 1 m/s. The blue line and points represent the reference path and images, while the green line represents the query path. Correct predictions are marked with green crosses, and incorrect predictions are marked with red crosses. The four samples of the query and predicted places show the original images and their preprocessed forms, which are used as input to our Modular SNN. Right: Proof-of-concept robot deployment testing platform. We used an AgileX Scout Mini [60] robot equipped with an Intel RealSense D435 camera.](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:ttw5tcvxt52gbygtxrgvschk/bafkreic27yefodz766yooca2uftyswr2hlwokkwjqom74e3wszoq5bi7fa@jpeg)
February 21, 2025 at 9:14 PM
Researchers from Queensland University of Technology present an energy-efficient #placerecognition system leveraging Spiking Neural Networks with modularity and sequence matching to rival traditional deep networks
ieeexplore.ieee.org/document/107...
#SpikingNeuralNetworks #RobotPerception
ieeexplore.ieee.org/document/107...
#SpikingNeuralNetworks #RobotPerception
Hi! 👋 I am working to make #SpikingNeuralNetworks the next big thing for #MachineLearning. Currently I'm at SynSense, focussed on applications for #SNNs, as well as toolchains to enable ML and SW Engineers to use our #Neuromorphic technology for low-power sensing and processing.
November 18, 2024 at 2:39 AM
Hi! 👋 I am working to make #SpikingNeuralNetworks the next big thing for #MachineLearning. Currently I'm at SynSense, focussed on applications for #SNNs, as well as toolchains to enable ML and SW Engineers to use our #Neuromorphic technology for low-power sensing and processing.
Spiking Neural Networks (SNNs) are creating a buzz in the world of artificial intelligence and neuromorphic engineering. #eventbasedprocessing #eventdrivenAI #Perception #realtimeAI #realtimevision #SNNs #spikingneuralnetworks #visionsystems
aicompetence.org/snns-in-visi...
aicompetence.org/snns-in-visi...
SNNs In Vision: Event-Based Processing For Real-Time AI
SNNs power real-time perception in vision systems by enabling event-based processing, offering faster, more efficient AI for dynamic environments.
aicompetence.org
October 27, 2024 at 10:04 PM
Spiking Neural Networks (SNNs) are creating a buzz in the world of artificial intelligence and neuromorphic engineering. #eventbasedprocessing #eventdrivenAI #Perception #realtimeAI #realtimevision #SNNs #spikingneuralnetworks #visionsystems
aicompetence.org/snns-in-visi...
aicompetence.org/snns-in-visi...