Low Dimensionality, High Robustness in Neural Population Dynamics

Román Rossi-Pool; Ranulfo Romo

doi:10.1016/j.neuron.2019.06.021

Low Dimensionality, High Robustness in Neural Population Dynamics

Neuron. 2019 Jul 17;103(2):177-179. doi: 10.1016/j.neuron.2019.06.021.

Authors

Román Rossi-Pool¹, Ranulfo Romo²

Affiliations

¹ Instituto de Fisiología Celular-Neurociencias, Universidad Nacional Autónoma de México, 04510 Mexico City, Mexico. Electronic address: romanr@ifc.unam.mx.
² Instituto de Fisiología Celular-Neurociencias, Universidad Nacional Autónoma de México, 04510 Mexico City, Mexico; El Colegio Nacional, 06020 Mexico City, Mexico. Electronic address: rromo@ifc.unam.mx.

PMID: 31319044
DOI: 10.1016/j.neuron.2019.06.021

Abstract

Neuronal populations respond within a small number of relevant dimensions. New research by Trautmann et al. (2019) shows that spike sorting is not necessary to extract the important features of this low-dimensional population signal. Combined responses of multiple neurons (multiunit activity) only generate small changes in the extracted signals.

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MeSH terms

Action Potentials
Algorithms*
Neurons
Population Dynamics
Signal Processing, Computer-Assisted*