Abstract
A central topic in neuroscience is the neural coding problem which aims to decipher how the brain signals sensory information through neural activity. Despite significant advancements in this area, the characterisation of information encoding through the precise timing of spikes in the somatosensory cortex is limited. Here, we utilised a comprehensive dataset from previous studies to identify and characterise temporal response patterns of Layer 4 neurons of the rat barrel cortex to five distinct stimuli with varying complexities: Basic, Contact, Whisking, Rough, and Smooth. A Gaussian Mixture Model (GMM) clustering analysis was applied to identify distinct temporal response patterns. We found that three stimuli (Rough, Smooth, and Contact) produced multiple temporal response patterns while Whisking and Basic stimuli exhibited a single pattern for all conditions. These patterns of neuronal responses were differentiated by the speed and strength of the responses when more than two clusters were present. Investigation into stimulus complexity indicated that stimuli with lower complexity scores (Whisking and Basic) resulted in fewer distinct response patterns, reflecting the reduced variability in the input information signal to Layer 4. In contrast, stimuli with higher complexity scores (Rough, Smooth, and Contact) produced distinct temporal response patterns, likely driven by a broader range of deflection amplitude variations and whisker direction changes. Further analysis of neuronal responses to Contact, Rough, and Smooth stimuli revealed three broad groups of temporal response patterns: phasic on-off response, prolonged on-off response, and tonic response. We speculate that these groups of temporal response patterns encode information about the velocity, acceleration, position, direction, and continuous monitoring of whisker deflection stimuli. The observed patterns contribute to the understanding of how neurons in Layer 4 of the rat barrel cortex specialise in encoding specific features of sensory stimuli and highlight the role of stimulus complexity in shaping neuronal responses.