.png){kind=icon}
Return
Rethinking post-synaptic potential dynamics: Adaptive Synaptic Filter for versatile temporal integration
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2026.113765.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
• ASF models PSP dynamics using synaptic time constants. • ASF is architecture-agnostic in spiking and non-spiking models. • Frequency analysis shows ASF preserves temporal features. • SNNs equipped with ASF achieve strong results across diverse tasks.
Keywords:
Adaptive Synaptic Filter
Spiking Neural Networks
Temporal Integration
Post-synaptic Potential
Frequency Analysis
Journal
IF:
7.6
Papers:
1.3W
Citations:
4.5W
Organization
Cited Papers
ShareSaveUnsupervised visual feature learning with spike-timing-dependent plasticity: How far are we from traditional feature learning approaches?.png)
PATTERN RECOGNITION
OAAI
PATTERN RECOGNITIONIF7.6
OAAIShareSaveAccurate and efficient time-domain classification with adaptive spiking recurrent neural networks
OAAI
OAAIShareSaveShareSaveTemporal dendritic heterogeneity incorporated with spiking neural networks for learning multi-timescale dynamicsNATURE COMMUNICATIONSOAAI
NATURE COMMUNICATIONSIF15.7
OAAIShareSaveDynamic memristor-based reservoir computing for high-efficiency temporal signal processingNATURE COMMUNICATIONSOAAI
NATURE COMMUNICATIONSIF15.7
OAAIShareSave
View more