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Conversion of a single-layer ANN to photonic SNN for pattern recognition
This work presents a complete conversion scheme for photonic spiking neural networks (SNNs). We verified that the output of an artificial neural...
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Error-Aware Conversion from ANN to SNN via Post-training Parameter Calibration
Spiking Neural Network (SNN), originating from the neural behavior in biology, has been recognized as one of the next-generation neural networks....
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CIRM-SNN: Certainty Interval Reset Mechanism Spiking Neuron for Enabling High Accuracy Spiking Neural Network
Spiking neural network (SNN) based on sparse trigger and event-driven information processing has the advantages of ultra-low power consumption and...
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Diagnostic biomarker discovery from brain EEG data using LSTM, reservoir-SNN, and NeuCube methods in a pilot study comparing epilepsy and migraine
The study introduces a new online spike encoding algorithm for spiking neural networks (SNN) and suggests new methods for learning and identifying...
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Digital design of a spatial-pow-STDP learning block with high accuracy utilizing pow CORDIC for large-scale image classifier spatiotemporal SNN
The paramount concern of highly accurate energy-efficient computing in machines with significant cognitive capabilities aims to enhance the accuracy...
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Leaky Integrate-and-Fire Neuron Model-Based SNN Latency Estimation Using FNS
The use of neural modeling tools is becoming increasingly common in the exploration of human brain behavior, enabling effective simulations through...
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Grid Search Optimization of Novel SNN-ESN Classifier on a Supercomputer Platform
This work is demonstrating the use of a supercomputer platform to optimise hyper-parameters of a proposed by the team novel SNN-ESN computational...
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Real-Time Adaptive Physical Sensor Processing with SNN Hardware
Spiking Neural Networks (SNNs) offer bioinspired computation based on local adaptation and plasticity as well as close biological compatibility. In...
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An SNN-CPG Hybrid Locomotion Control for Biomimetic Robotic Fish
Biomimetic robotic fish that absorbs inspiration from fish has the advantage of high mobility, high efficiency, and low noise. However, it is still...
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TM-SNN: Threshold Modulated Spiking Neural Network for Multi-task Learning
This paper introduces a spiking neural network able to learn multiple tasks using their unique characteristic, namely, that their behavior can be...
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Accelerated Optimization for Simulation of Brain Spiking Neural Network on GPGPUs
As the application scenarios for large-scale spiking neural networks (SNN) increase, efficient SNN simulation becomes more essential. However,...
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DNM-SNN: Spiking Neural Network Based on Dual Network Model
In recent years, deep neural network (DNN) has shown excellent performance in many applications. However, the huge energy consumption leads to many...
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Feed-Forward SNN for Touch Modality Prediction
Recently, Spiking Neural Networks (SNNs) have been considered as alternatives to the common deep neural networks (DNNs) when the energy efficiency...
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On the Generation of Desired Outputs for Spike Neural Networks (SNN)
In supervised learning algorithms, it is necessary to define an error function for the parameter adjustment process to take place. This function...
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N-Neuron Simulation Using Multiprocessor Cluster
A growing number of research effort has been made to make the Simulation of the astonishing biophysical activities of the massively interconnected...
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Sailboat navigation control system based on spiking neural networks
In this paper, we presented the development of a navigation control system for a sailboat based on spiking neural networks (SNN). Our inspiration for...
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Research Progress of spiking neural network in image classification: a review
Spiking neural network (SNN) is a new generation of artificial neural networks (ANNs), which is more analogous with the brain. It has been widely...
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Spiking SiamFC++: deep spiking neural network for object tracking
Spiking neural network (SNN) is a biologically-plausible model and exhibits advantages of high computational capability and low power consumption....
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GPU4SNN: GPU-Based Acceleration for Spiking Neural Network Simulations
Spiking Neural Networks (SNNs) are the most common and widely used artificial neural network models in bio-inspired computing. However, SNN...
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Dynamic layer-span connecting spiking neural networks with backpropagation training
Spiking Neural Network (SNN) is one of the mainstream frameworks for brain-like computing and neuromorphic computing, which has the potential to...
