Introduction:Cognitive functions rely on collective neuronal oscillations, captured by EEG/LFP. Beta (13-30 Hz) and gamma (30-100 Hz) oscillations are linked to cognition [1]. These oscillations occur in short bursts with variable frequencies, challenging trial averages and simplified models. In Ref. [2] we showed spiking and neural mass models reproduce gamma bursts but not variability. In a recent study using the adaptive exponential integrate-and-fire (AdEx) model with different percentages of somatostatin (SOM) and parvalbumin (PV), we have shown that SOM/PV density affects oscillation frequencies [3]. Experimental study shows, PV/SOM variability exists across layers [4]. Using AdEx, we model layer-specific inhibitory variability to explain burst frequency/power variability.
Methods:We analyze experimental LFP data using time-frequency spectrogram analysis to identify bursts, defined as oscillatory events lasting at least two cycles with power exceeding six times the median at that frequency. We extract burst features, including peak frequency, peak power, mean power, duration, frequency span, time of peak power, and burst size. Machine learning methods are applied to assess how these features relate to cognitive processes. We use a computational spiking network model based on the adaptive exponential integrate-and-fire (AdEx) model, incorporating layer-specific variability in inhibitory populations. This allows us to simulate burst dynamics observed in experimental data and explore how different inhibitory neuron densities influence oscillatory behavior. Results:From the experimental signal, we first calculate the averaged beta and gamma power in the lateral prefrontal cortex (LPFC) across layers. As shown by Ref. [5], we also observed a crossover of powers across layers, where beta power dominates in deep layers, and gamma power dominates in superficial layers. The extracted burst power follows this trend, validating the burst extraction process. Using our model, we replicate this behavior, demonstrating the role of varying inhibitory neuron densities in different cortical layers. Discussion:Our model can exhibit burst dynamics across beta to gamma bands as observed in experimental data. Introducing distinct inhibitory populations (SOM, PV) predicts a cortical hierarchy where increased SOM/PV densities lower oscillation frequencies. Layer-wise modeling reveals burst-like features resembling experimental data. These findings highlight the importance of inhibitory diversity in shaping oscillatory dynamics and suggest that layer-specific variability plays a key role in modulating neural activity across frequency bands.
Acknowledgements This work is supported by the French Ministry of Higher Education (Ministére de l’Enseignement Supérieur) and the project LABEX CORTEX (ANR-11-LABX-0042) of Université Claude Bernard Lyon 1 operated by the ANR. References [1]https://doi.org/10.1016/j.neuron.2016.02.028 [2]https://doi.org/10.3389/fncom.2024.1422159 [3]https://doi.org/10.1101/2025.02.23.639719 [4]https://doi.org/10.1038/nn.3446 [5]https://doi.org/10.1038/s41593-023-01554-7
