Deep learning based feature extraction for prediction and interpretation of sharp-wave ripples in the rodent hippocampus

Abstract
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Abstract

Local field potential (LFP) deflections and oscillations define hippocampal sharp-wave ripples (SWR), one of the most synchronous events of the brain. SWR reflect firing and synaptic current sequences emerging from cognitively relevant neuronal ensembles. While spectral analysis have permitted advances, the surge of ultra-dense recordings now call for new automatic detection strategies. Here, we show how one-dimensional convolutional networks operating over high-density LFP hippocampal recordings allowed for automatic identification of SWR from the rodent hippocampus. When applied without retraining to new datasets and ultra-dense hippocampus-wide recordings, we discovered physiologically relevant processes associated to the emergence of SWR, prompting for novel classification criteria. To gain interpretability, we developed a method to interrogate the operation of the artificial network. We found it relied in feature-based specialization, which permit identification of spatially segregated oscillations and deflections, as well as synchronous population firing typical of replay. Thus, using deep learning based approaches may change the current heuristic for a better mechanistic interpretation of these relevant neurophysiological events.

Data availability

Data is deposited in the Figshare repository https://figshare.com/projects/cnn-ripple-data/117897. The trained model is accessible at the Github repository for both Python: https://github.com/PridaLab/cnn-ripple, and Matlab: https://github.com/PridaLab/cnn-matlab Code visualization and detection is shown in an interactive notebook https://colab.research.google.com/github/PridaLab/cnn-ripple/blob/main/src/notebooks/cnn-example.ipynb . The online detection Open Ephys plugin is accessible at the Github repository: https://github.com/PridaLab/CNNRippleDetectorOEPlugin

Article and author information

Author details

Andrea Navas-Olive

Functional and Systems Neuroscience, Instituto Cajal, Madrid, Spain

Competing interests
No competing interests declared.
Rodrigo Amaducci

Grupo de Neurocomputación Biológica, Universidad Autónoma de Madrid, Madrid, Spain

Competing interests
No competing interests declared.
Maria-Teresa Jurado-Parras

Functional and Systems Neuroscience, Instituto Cajal, Madrid, Spain

Competing interests
No competing interests declared.
Enrique R Sebastian

Functional and Systems Neuroscience, Instituto Cajal, Madrid, Spain

Competing interests
No competing interests declared.
Liset M de la Prida

Functional and Systems Neuroscience, Instituto Cajal, Madrid, Spain

For correspondence
lmprida@cajal.csic.es

Competing interests
Liset M de la Prida, Reviewing editor, eLife.

"This ORCID iD identifies the author of this article:" 0000-0002-0160-6472

Funding

Fundacion La Caixa (LCF/PR/HR21/52410030)

Liset M de la Prida

Ministerio de Educacion (FPU17/03268)

Andrea Navas-Olive

Universidad Autónoma de Madrid (FPI-UAM-2017)

Rodrigo Amaducci

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Animal experimentation: All protocols and procedures were performed according to the Spanish legislation (R.D. 1201/2005 and L.32/2007) and the European Communities Council Directive 2003 (2003/65/CE). Experiments and procedures were approved by the Ethics Committee of the Instituto Cajal and the Spanish Research Council (PROEX131-16 and PROEX161-19). All surgical procedures were performed under isoflurane anesthesia and every effort was made to minimize suffering.

Copyright

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.