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Temporal Spiking Neural Networks with Synaptic Delay for Graph Reasoning
This paper introduces Graph Reasoning Spiking Neural Networks (GRSNN) for efficient graph reasoning, leveraging the temporal domain and synaptic delay. -
Minimal Spiking Neural Networks
A minimal motif consists of only two interconnected neurons – one excitatory neuron with a delayed self-connection (autapse) and one inhibitory neuron, yielding a bistable motif... -
Spiking Associative Memory For Spatio-Temporal Patterns
The dataset is used to test the proposed learning mechanism for spiking neural networks. -
Spiking Neural Network Dataset
The dataset used in this paper is a spiking neural network (SNN) with 20 layers, where each layer has 2000 LIF neurons. The input spikes are Poisson trains at a target rate of... -
FPGA Implementation of Simplified Spiking Neural Network
The proposed model is validated on a Xilinx Virtex 6 FPGA and analyzes a fully connected network which consists of 800 neurons and 12,544 synapses in real-time. -
MNIST, CIFAR10, CIFAR100, DVS-Gesture
The dataset used in this paper is a spiking neural network dataset.
