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Although inhibitory maturation and shifts in excitation-inhibition balance have been implicated in this process, how individual neurons implement the transition remains unclear because rapid brain growth has prevented long-term, same-neuron mapping. Here, we introduce growth-adaptive spring electronics that provide depth-wise compliance during tissue expansion, maintaining a stable electrode-tissue interface over weeks of neonatal development. We developed a vision-language model-assisted spike processing pipeline for the developing brain that probabilistically matches units across days using high-density waveform spatial footprints, despite developmental changes in the neonatal brain. Together, these innovations enable spike-resolved mapping of the same neurons in rat visual cortex and medial prefrontal cortex from postnatal day 10 to 45. Using population coupling to quantify each neuron\u2019s coordination with local population activity, we show that developmental decorrelation is driven primarily by a distinct subset of neurons that progressively shifts from strong to weak coupling during postnatal weeks 3 to 5, whereas other neurons remain stably weakly or strongly coupled throughout development. These results resolve population-level desynchronization into identifiable neuron-specific trajectories. This framework enables direct tests in neurodevelopmental disorder models, including schizophrenia and autism, of whether altered maturation reflects global circuit imbalance or selective disruption and mistiming of specific developmental programs.<\/jats:p>","DOI":"10.64898\/2026.02.16.705931","type":"posted-content","created":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T22:00:21Z","timestamp":1771452021000},"source":"Crossref","is-referenced-by-count":1,"title":["Growth-adaptive spring electronics for long-term, same-neuron mapping in the developing rat brain"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8549-6310","authenticated-orcid":false,"given":"Ariel J.","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA"},{"name":"John A. 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