Unlocking the Dance of Chimera Patterns in Hebb Synapses

Published on June 23, 2022

Imagine a group of dancers trying to synchronize their moves on a crowded dance floor. They follow a specific rhythm and rely on visual cues to stay in sync. Now, picture this scenario playing out inside our brains, where groups of neurons perform a complex dance of communication. In a recent study, scientists explored the intricate dynamics between two well-known models – the Kuramoto-Sakaguchi model and Hebb dynamics – to understand how synchronized states emerge. What they discovered is fascinating: under certain conditions, a mesmerizing phenomenon called the ‘chimera pattern’ can occur within the Hebbian synaptic strengths. This pattern arises when two different regions of oscillators rotate at different speeds – one moving slower, while the other spins faster. By adjusting the parameters that govern frustration and introducing a pacemaker oscillator, researchers created non-stationary behavior reminiscent of hysteresis and observed a switch-like phenomenon similar to how we learn and remember things. The implications of this research could help us unravel more secrets about the brain’s incredible capacity for information processing and storage! To delve into the mesmerizing world of chimera patterns and Hebb synapses, check out the full article.

The union of the Kuramoto–Sakaguchi model and the Hebb dynamics reproduces the Lisman switch through a bistability in synchronized states. Here, we show that, within certain ranges of the frustration parameter, the chimera pattern can emerge, causing a different, time-evolving, distribution in the Hebbian synaptic strengths. We study the stability range of the chimera as a function of the frustration (phase-lag) parameter. Depending on the range of the frustration, two different types of chimeras can appear spontaneously, i.e., from randomized initial conditions. In the first type, the oscillators in the coherent region rotate, on average, slower than those in the incoherent region; while in the second type, the average rotational frequencies of the two regions are reversed, i.e., the coherent region runs, on average, faster than the incoherent region. We also show that non-stationary behavior at finite N can be controlled by adjusting the natural frequency of a single pacemaker oscillator. By slowly cycling the frequency of the pacemaker, we observe hysteresis in the system. Finally, we discuss how we can have a model for learning and memory.

Read Full Article (External Site)

Leave a Reply

Your email address will not be published. Required fields are marked *

You may use these HTML tags and attributes:

<a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <s> <strike> <strong>