Inspired by recent spiking neural networks (SNNs), which emulate a biological inference process and provide an energy-efficient neural architecture, we incorporate the SNNs with CGNNs in a unified framework, named Continuous Spiking Graph Neural Networks (COS-GNN). We employ SNNs for graph node ...
We obtained, with artificial evolution, very small (one or two interneurons, one output neuron) spiking neural networks (SNNs) recognizing a simple temporal pattern in a continuous input stream. The patterns the network evolved to recognize consisted of three different signals. In other words, the...
Biological: Simulate large populations of spatially and/or velocity modulated cell types. Neurons can be rate based or spiking. The random motion model is fitted to match real rodent motion. Flexible: Simulate environments in 1D or 2D with arbitrarily wall, boundary and hole arrangements. Combine ...
In this work, we propose a new Graph Neural Network (GNN)-based approach to model dynamic signed networks, named SEMBA: Signed link's Evolution using Memory modules and Balanced Aggregation. Here, the idea is to incorporate the signs of temporal interactions using separate modules guided by ...
Differentiating signal from artefacts in cosmic ray detection: Applying Siamese spiking neural networks to CREDO experimental data. Measurement 2023, 220, 113273. [Google Scholar] [CrossRef] Hachaj, T.; Bibrzycki, L.; Piekarczyk, M. Recognition of Cosmic Ray Images Obtained from CMOS Sensors ...