2025/03/01 by Jade Poirier, John Beninger, Richard Naud · 1 voice
Computer Science · Engineering · Neuroscience · #Advanced Memory and Neural Computing #Neural Networks and Applications #Neural dynamics and brain function
paper · doi:10.1016/j.xpro.2025.103652
openalex publication_date 2025/03/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/03
Transient changes in synaptic strength, known as short-term plasticity (STP), play a fundamental role in neuronal communication. Here, we present a protocol for using SRPlasticity, a software package that implements a computational model of STP. SRPlasticity supports automatic characterization of electrophysiological data and simulation of synaptic responses. We describe steps for installing and utilizing SRPlasticity, preprocessing data, fitting models, and simulating responses. We then detail procedures for analyzing spike response plasticity (SRP) model parameters to infer functional groupings of STP. For complete details on the use and execution of this protocol, please refer to Rossbroich et al. 1 and Beninger et al. 2 • Steps for flexibly capturing synaptic dynamics using SRPlasticity software • Instructions for automated fitting to infer model parameters from experimental data • Guidance on predicting synaptic responses to novel presynaptic spike trains in silico • Procedures for visualization, clustering, and predicting classes Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Transient changes in synaptic strength, known as short-term plasticity (STP), play a fundamental role in neuronal communication. Here, we present a protocol for using SRPlasticity, a software package that implements a computational model of STP. SRPlasticity supports automatic characterization of electrophysiological data and simulation of synaptic responses. We describe steps for installing and utilizing SRPlasticity, preprocessing data, fitting models, and simulating responses. We then detail procedures for analyzing spike response plasticity (SRP) model parameters to infer functional groupings of STP.