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N-MNIST and SHD datasets
The dataset used for training and testing the accelerated ALIF SNN model. -
Synaptic plasticity
We implemented synaptic plasticity and show that while individual weights show small deviations due to stochastic rounding, the statistics of a learning rule are preserved. -
Loihi’s computational unit and its implementation
We dissect an individual computational unit from Loihi. The basic building block is a spiking unit inspired by a current based leaky integrate and fire (LIF) neuron model. -
Brian2Loihi: An emulator for the neuromorphic chip Loihi
Developing intelligent neuromorphic solutions remains a challenging endeavour. It requires a solid conceptual understanding of the hardware’s fundamental building blocks. Beyond... -
Robust trajectory generation for robotic control on the neuromorphic research...
The anisotropic network is a spiking neural network model that generates spatially asymmetric non-plastic connections. The model is based on a biologically-inspired rule for... -
Analog-to-Spike Encoder for Time-Coded Spiking FT
The proposed approach can encode voltages with high accuracy. The dataset is a spike train generated by the proposed analog-to-spike encoder (ASE) using phase encoding. -
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.
