Adaptive robustness through incoherent signaling mechanisms in a regenerative brain

Animal behavior emerges from collective dynamics of interconnected neural populations, making it vulnerable to damages to the connectome architecture. However, many organisms can maintain significant behavioral output in the face of large-scale neural damages, though molecular underpinnings of this extreme robustness remain mostly unknown. Here, we develop a high-content imaging platform and quantitative behavioral analysis framework that enable us to measure a previously uncharacterized long-lasting latent memory state in planarian flatworms during their whole-brain regeneration with unprecedented precision. By combining over 10,000 animal trials with computational modeling of neural population dynamics, we show that long-range volumetrically transmitted peptidergic signals allow the nervous system to maintain robust control over behavior when large portions of neurons are ablated. The different time and length scales of neuropeptide and small molecule transmission lead to incoherent signals competitively regulating the latent memory. During regeneration, long-range peptide signals dominate, allowing the planarian to rapidly reestablish the latent behavioral state and restore coarse behavioral output while gradually refining it to a precise response. Controlling behavior through two overlapping but opposing communication mechanisms creates a more robust system than either alone and may serve as a generic approach to construct robust neural networks.