Connectivity for Rapid Synchronization in a Neural Pacemaker Network
Williams, E.; Shifman, A. R.; Lewis, J. E.
Show abstract
Synchronization is a fundamental property of biological neural networks, playing a mechanistic role in both healthy and disease brain states. The medullary pacemaker nucleus of the weakly electric fish is a synchronized network of high-frequency neurons, weakly coupled via gap junctions. Synchrony in the pacemaker is behaviourally modulated on millisecond timescales, but how gap junctional connectivity enables such rapid resynchronization speeds is poorly understood. Here, we use a computational model of the pacemaker, along with graph theory and predictive analyses, to investigate how network properties, such as randomness and the directionality of coupling (bidirectional/non-rectifying versus directional/rectifying gap junctions) characterize the fast synchronization of the pacemaker network. Our results provide predictions about connectivity in the pacemaker and insight into the relationship between structural network properties and synchronization dynamics in neural systems more generally.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Distributed Phase Oscillatory Excitation Efficiently Produces Attractors Using Spike Timing Dependent Plasticity 96%
- Mean-field approximations with adaptive coupling for networks with spike-timing-dependent plasticity 95%
- Reduced Dimension, Biophysical Neuron Models Constructed From Observed Data 95%
Similar papers in this journal
Similar papers in this journal
- Neuromodulatory effects on synchrony and network reorganization in networks of coupled Kuramoto oscillators. 96%
- Robust Cortical Criticality and Diverse Neural Network Dynamics Resulting from Functional Specification 95%
- Foci, waves, excitability : self-organization of phase waves in a model of asymmetrically coupled embryonic oscillators 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.