Search results for "Spiking neural network"
showing 3 items of 13 documents
Fast spiking neural network architecture for low-cost FPGA devices
2012
Spiking Neural Networks (SNN) consist of fully interconnected computation units (neurons) based on spike processing. This type of networks resembles those found in biological systems studied by neuroscientists. This paper shows a hardware implementation for SNN. First, SNN require the inputs to be spikes, being necessary a conversion system (encoding) from digital values into spikes. For travelling spikes, each neuron interconnection is characterized by weights and delays, requiring an internal neuron processing by a Postsynaptic Potential (PSP) function and membrane potential threshold evaluation for a postsynaptic output spike generation. In order to model a real biological system by arti…
Frequency spike encoding using Gabor-like receptive fields
2014
Abstract Spiking Neural Networks (SNN) are a popular field of study. For a proper development of SNN algorithms and applications, special encoding methods are required. Signal encoding is the first step since signals need to be converted into spike trains as the primary input to an SNN. We present an efficient frequency encoding system using receptive fields. The proposed encoding is versatile and it can provide simple image transforms like edge detection, spot detection or removal, or Gabor-like filtering. The proposed encoding can be used in many application areas as image processing and signal processing for detection and classification.
Event-Based Trajectory Prediction Using Spiking Neural Networks
2021
International audience; In recent years, event-based sensors have been combined with spiking neural networks (SNNs) to create a new generation of bio-inspired artificial vision systems. These systems can process spatio-temporal data in real time, and are highly energy efficient. In this study, we used a new hybrid event-based camera in conjunction with a multi-layer spiking neural network trained with a spike-timing-dependent plasticity learning rule. We showed that neurons learn from repeated and correlated spatio-temporal patterns in an unsupervised way and become selective to motion features, such as direction and speed. This motion selectivity can then be used to predict ball trajectory…