Drawing inspiration from the central pattern generators (CPGs) in the human brain, which produce rhythmic patterned outputs without requiring rhythmic inputs, we propose a novel PE technique …

Apr 24, 2025 · To address these challenges, this study conducts a systematic analysis of the fundamental mechanisms underlying reset operations and refractory periods in binary …

Sep 18, 2024 · Recurrent neural networks (RNNs) are powerful models for processing sequential data while spiking neural networks (SNNs) show huge potential for processing sequential event …

The Binary S4D model (Section “Binary S4D”), proposed in this paper, is trained and evaluated on all tasks within the LRA, to explore how binarisation impacts baseline performance.

Apr 24, 2025 · We re-examine whether such biological mechanisms are strictly necessary for generating sparse spiking patterns, provide new theoretical explanations and insights, and …

Jan 1, 2023 · Emergence of deep neural networks (DNNs) has raised enormous attention towards artificial neural networks (ANNs) once again. They have become the stat…

Aug 24, 2023 · To address this issue, this research study compares the performance, behavior, and spike generation of multiple SNN models using consistent inputs and neurons.

In this paper, we investigate the performance of LIF and Cuba-LIF models with recurrent topologies on three spiking audio datasets from a Dynamic Au-dio Sensor (DASDIGITS [2]) or …

Here, we present a simple framework to construct biologically realistic spiking recurrent neural networks (RNNs) capable of learning a wide range of tasks.

Aug 26, 2024 · Overall, the CRNN-SNN conversion technique presented in this paper is a valuable contribution to the field of spiking neural networks and their application to sequence learning …