The systems showing chimera states are often also multi-stable, meaning that the system exhibits a synchronized or a chimera state depending on the initial conditions 3, 4. Abrams and Strogatz 1 aptly utilized the term ‘chimera’ to label a hybrid solution consisting of localized synchronized and unsynchronized dynamics to an otherwise homogeneous network of oscillators 2. ![]() In Greek mythology, a chimera is a hybrid creature composed of the components of more than one animal. Our results imply that the emergence of chimeras is quite generic at the meso- and macroscale suggesting their general relevance in neuroscience. The recurrent neural networks can also be trained to switch chimera solutions: an input pulse can trigger the neural network to switch the synchronized and the unsynchronized groups of the embedded chimera, reminiscent of uni-hemispheric sleep in a variety of animals. We also demonstrate that learning is robust to different biological constraints, such as the excitatory/inhibitory classification of neurons (Dale’s law), and the sparsity of connections in neural circuits. ![]() These solutions, which we refer to as embedded chimeras, are generically produced by recurrent neural networks with connectivity matrices only slightly perturbed from random networks. Here, we demonstrate how chimeras can emerge in recurrent neural networks by training the networks to display chimeras with machine learning. Chimera states, where synchrony and asynchrony coexist, have been documented only for precisely specified connectivity and network topologies. However, the mechanism by which synchrony and asynchrony co-exist in a population of neurons remains elusive. ![]() Fully and partially synchronized brain activity plays a key role in normal cognition and in some neurological disorders, such as epilepsy.
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