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Combining modeling, deep learning for MEA neuron localization, classification (Buccino et al 2018)
Tom Morse - MoldelDB admin
"Neural circuits typically consist of many different types of neurons, and one faces a challenge in disentangling their individual contributions in measured neural activity. Classification of cells into inhibitory and excitatory neurons and localization of neurons on the basis of extracellular recordings are frequently employed procedures. Current approaches, however, need a lot of human intervention, which makes them slow, biased, and unreliable. In light of recent advances in deep learning techniques and exploiting the availability of neuron models with quasi-realistic three-dimensional morphology and physiological properties, we present a framework for automatized and objective classification and localization of cells based on the spatiotemporal profiles of the extracellular action potentials recorded by multielectrode arrays. We train convolutional neural networks on simulated signals from a large set of cell models and show that our framework can predict the position of neurons with high accuracy, more precisely than current state-of-the-art methods. Our method is also able to classify whether a neuron is excitatory or inhibitory with very high accuracy, substantially improving on commonly used clustering techniques. ..."
  • Neocortex L5/6 pyramidal GLU cell Show Other
  • Neocortex U1 L5B pyramidal pyramidal tract GLU cell Show Other
  • Neocortex layer 5 interneuron Show Other
  • Neocortex bitufted interneuron Show Other
  • Neocortex deep neurogliaform interneuron Show Other
  • Buccino AP, Kordovan M, Ness TV, Merkt B, Häfliger PD, Fyhn M, Cauwenberghs G, Rotter S, Einevoll GT (2018) Show Other
tom.morse@yale.edu
Buccineo et al 2018
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Other categories referring to Combining modeling, deep learning for MEA neuron localization, classification (Buccino et al 2018)
Revisions: 4
Last Time: 6/4/2020 12:14:34 PM
Reviewer: Tom Morse - MoldelDB admin
Owner: Tom Morse - MoldelDB admin