Stress-Induced Impairment of a Working Memory Task: Role of Spiking Rate and Spiking History Predicted Discharge
Figure 6
Characterization of generalized linear models of task-related activity.
Plot of 80 seconds of spike train data, spanning three trials and fit with a GLM using (A) a homogeneous Poisson model, (B) an inhomogeneous Poisson model, and (C) an a conditional intensity model (Model 1b, during baseline conditions only). Spike counts of the original spike train are plotted with black dots against lambda (λ; green line with red confidence intervals). X-axis = experimental time. D) Kolmogorov–Smirnov (K-S) goodness-of-fit plot demonstrates that incorporation of spike history improves performance of the CI-GLM (blue vs. green line). The K-S plot of the final model (blue line; model from panel C) falls within equivalency confidence intervals of the K-S test (diagonal solid and dotted lines) for all quantiles, indicating that inclusion of spike history with behavioral intervals in the CI-GLM is critical to appropriately model plPFC spiking activity. Inhomogeneous Poisson models using solely the behavioral states of the task overestimate neuron interspike intervals (green line; model from panel B). Models of neuronal activity (1–3; main text) also passed K-S goodness-of-fit tests (Fig. S3).