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1.
Elife ; 122024 Jul 10.
Artigo em Inglês | MEDLINE | ID: mdl-38985568

RESUMO

Accurate tracking of the same neurons across multiple days is crucial for studying changes in neuronal activity during learning and adaptation. Advances in high-density extracellular electrophysiology recording probes, such as Neuropixels, provide a promising avenue to accomplish this goal. Identifying the same neurons in multiple recordings is, however, complicated by non-rigid movement of the tissue relative to the recording sites (drift) and loss of signal from some neurons. Here, we propose a neuron tracking method that can identify the same cells independent of firing statistics, that are used by most existing methods. Our method is based on between-day non-rigid alignment of spike-sorted clusters. We verified the same cell identity in mice using measured visual receptive fields. This method succeeds on datasets separated from 1 to 47 days, with an 84% average recovery rate.


Assuntos
Neurônios , Animais , Neurônios/fisiologia , Camundongos , Eletrofisiologia/métodos , Fenômenos Eletrofisiológicos , Potenciais de Ação/fisiologia , Rastreamento de Células/métodos
2.
bioRxiv ; 2024 Apr 28.
Artigo em Inglês | MEDLINE | ID: mdl-38260339

RESUMO

Accurate tracking of the same neurons across multiple days is crucial for studying changes in neuronal activity during learning and adaptation. Advances in high density extracellular electrophysiology recording probes, such as Neuropixels, provide a promising avenue to accomplish this goal. Identifying the same neurons in multiple recordings is, however, complicated by non-rigid movement of the tissue relative to the recording sites (drift) and loss of signal from some neurons. Here we propose a neuron tracking method that can identify the same cells independent of firing statistics, that are used by most existing methods. Our method is based on between-day non-rigid alignment of spike sorted clusters. We verified the same cell identity in mice using measured visual receptive fields. This method succeeds on datasets separated from one to 47 days, with an 84% average recovery rate.

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