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High-order synchronization in identical neurons with asymmetric pulse coupling
Abhay1, Gaurav Dar1
1BITS Pilani K K Birla Goa Campus, Department of Physics, Zuarinagar, Goa 403726, India.
Physical Review. E
|April 18, 2025
Summary
We demonstrate high-order (p/q) synchronization in coupled neurons with identical frequencies but asymmetric coupling. This study reveals complex structures and multistability within these synchronization regions.
Area of Science:
- Computational Neuroscience
- Nonlinear Dynamics
- Systems Biology
Background:
- High-order (p/q) synchronization, where p cycles of one oscillator match q cycles of another, is known in forced and coupled oscillators.
- This phenomenon is less understood in coupled neuronal systems, particularly concerning asymmetric coupling effects.
Purpose of the Study:
- To demonstrate and thoroughly investigate high-order (p/q) frequency-locking in a pair of coupled neurons with identical intrinsic frequencies but asymmetric coupling.
- To analyze the complex structure and dynamics within these synchronization regions in parameter space.
Main Methods:
- Simulations were used to explore the parameter space (g,α) and observe phenomena like quasiperiodicity, devil staircase, and Farey arrangements of spike sequences.
- An analytical method based on event-driven maps was developed to determine the existence and stability of p/q frequency-locked states, handling nonsmooth bifurcations.
Main Results:
- Asymmetric coupling in an excitatory-inhibitory (E-I) neuron pair naturally induces diverse p/q frequency-locking structures.
- Simulations revealed reducible and irreducible p/q regions with internal bifurcation structures, unlike traditional Arnold tongues.
- Multistability was observed both within and between different p/q synchronization states, with boundaries defined by saddle node and grazing bifurcations.
Conclusions:
- Asymmetric coupling is a key mechanism for generating complex synchronization patterns in neuronal networks.
- The developed analytical method provides a robust framework for understanding spike sequence stability in coupled neuron models.
- The findings highlight the intricate dynamics and multistability inherent in neuronal synchronization, crucial for understanding neural computation.
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