P116 Phase-locking patterns in oscillatory neural networks with diverse inhibitory populations
Aïda Cunill1, Marina Vegué1,Gemma Huguet*1,2,3
1Department of Mathematics, Universitat Politècnica de Catalunya, Barcelona, Spain 2Institute of Mathematics Barcelona-Tech (IMTech), Universitat Politècnica de Catalunya, Barcelona, Spain 3Centre de Recerca Matemàtica, Barcelona, Spain
*Email: gemma.huguet@upc.edu
Introduction.Brain oscillations play a crucial role in cognitive processes, yet their precise function is not completely understood. Communication through coherence theory [1] suggests that rhythms regulate information flow between neural populations: to communicate effectively, neural populations must synchronize their rhythmic activity. Studies on gamma-frequency oscillations have shown that when input frequency exceeds the target oscillator's natural frequency, oscillators phase-lock in an optimal phase relationship for effective communication [2,3]. Inhibitory neurons play a crucial role in modulating cortical oscillations, and exhibit diverse biophysical properties. We explore theoretically how diverse inhibitory populations influence oscillatory dynamics.
Methods.We use exact mean-field models [4,5] to explore how different inhibitory populations shape cortical oscillations and influence neural communication. We consider a neural network that includes one excitatory population and two distinct inhibitory populations with a network connectivity inspired in cortical circuits. The network receives an external periodic excitatory input in the gamma frequency range, simulating the input from other oscillating neural populations. We use phase-reduction techniques to identify the phase-locked states between the input and the target population as a function of the amplitude, frequency and coherence of the inputs. We propose several factors to measure communication between neural oscillators. Results.We have developed a theoretical framework to study the conditions for effective communication, exploring the role of different types of inhibitory neurons. We compare phase-locking and synchronization properties in networks with either a single or two distinct inhibitory populations. In a network with a single inhibitory population,communication is only effective for inputs that are faster than the natural frequency of the target oscillator. The inclusion of a second inhibitory population with slower synapses expands 1:1 phase-locking range to both higher and lower frequency inputs and improves the encoding of inputs with frequencies near the natural gamma rhythm of the target oscillator. Discussion.Our results contribute to understand how different types of inhibitory populations regulate the timing and coordination of neural activity through mean-field models and mathematical analysis. We identify the role of different types of inhibition in generating and maintaining distinct phase-locking patterns, which are essential for communication between brain regions.
Acknowledgements Work produced with the support of the grant PID-2021-122954NB-I00 funded by MCIN/AEI/ 10.13039/501100011033 and “ERDF: A way of making Europe”, the Maria de Maeztu Award for Centers and Units of Excellence in R&D (CEX2020-001084-M) and the AGAUR project 2021SGR1039. References 1.https://doi.org/10.1016/j.neuron.2015.09.034 2.https://doi.org/10.1111/ejn.12453 3.https://doi.org/10.1371/journal.pcbi.1009342 4.https://doi.org/10.1103/PhysRevX.5.021028 5.https://doi.org/10.1371/journal.pcbi.1007019
