Rhythmic neural activity underlies essential biological functions such as locomotion, breathing, and feeding. Computational models are widely used to study how such rhythms emerge from interactions between neuron-level and circuit-level dynamics. Intrinsically bursting neurons are key components of many central pattern generators (CPGs), yet existing models span a tradeoff between biological realism and practical usability. Biophysical models involve many parameters that are difficult to tune, whereas abstract models often integrate poorly into neural circuit simulations. We propose a simplified model of intrinsically bursting neurons derived from a reduced non-spiking biophysical formulation. The model integrates readily into neural circuits while enabling direct and independent control of bursting characteristics, including duration, amplitude, and shape. We show that the model reproduces single-unit biophysical responses to diverse stimuli as well as circuit-level activity patterns from crustacean and mammalian CPGs. This model provides a practical tool for studying rhythm generation in neural circuits.
Paper: Simplified Model of Intrinsically Bursting Neurons
@misc{Bhattasali2026SimplifiedBurstingNeurons,
title = {Simplified Model of Intrinsically Bursting Neurons},
author = {Nikhil X. Bhattasali and Lerrel Pinto and Grace W. Lindsay},
archivePrefix={bioRxiv},
year={2026},
}- Installation: Set up virtual environment and path variables.
- Usage: Reproduce analyses from the paper.