Protocol for analyzing slow cortical dynamics in mouse neuronal recordings

Temporal information processing is critical for brain function, supporting neural computations such as novelty detection, adaptation, and temporal normalization. Its disruption is implicated in schizophrenia. We present a protocol for analyzing ongoing neuronal network activity using binwise decoding, trial-to-trial variability analysis, and estimation of network-intrinsic timescales (INTs). We apply these techniques to identify slow dynamics that encode the memory of recent stimuli in neuronal populations in the mouse auditory cortex and in artificial neural networks trained on a novelty-detection task. For complete details on the use and execution of this protocol, please refer to Shymkiv et al. 1