Computational exposition of multistable rhythms in 4-cell neural circuits

•Methods of dynamical systems theory extended with unsupervised machine learning and GPU parallelization.•Multistable rhythmic capacities of biological neural networks identified.•Predicting the influence of network topology, intrinsic and extrinsic parameters on patterns of activity and multistability.•Inhibitory coupled 4-cell networks exhibit a plethora of monostable or multistable rhythmic states.•Rhythms include pacemakers, paired half-centers, full and mixed traveling-waves, synchronized states, and chimeras.•Network topologies identified to produce robust monostable rhythms, resilient to external perturbations.•Verifiable hypotheses generated for neurophysiological experiments on real animal CPGs with dynamic clamp technique.