"...we address the sensitivity of plasticity to trial-to-trial variability and delineate how spatiotemporal synaptic input patterns produce plasticity with in vivo-like conditions using a data-driven computational model with a calcium-based plasticity rule. Using in vivo spike train recordings as inputs, we show that plasticity is strongly robust to trial-to-trial variability of spike timing, and derive general synaptic plasticity rules describing how spatiotemporal patterns of synaptic inputs control the magnitude and direction of plasticity..."
Model Type: Synapse; Channel/Receptor; Dendrite; Neuron or other electrically excitable cell
Cell Type(s): Neostriatum medium spiny direct pathway GABA cell
Currents: I ANO2; I L high threshold; I Na,t; Kir; I N; I R; IK Bkca; I Krp; I T low threshold; I A; I A, slow
Model Concept(s): Detailed Neuronal Models; Calcium dynamics; Synaptic Plasticity; Learning
Simulation Environment: MOOSE/PyMOOSE; Python
Implementer(s): Dorman, Daniel B
References:
Dorman DB, Blackwell KT. (in press). Synaptic plasticity is predicted by spatiotemporal firing rate patterns and robust to in vivo-like variability .