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

• Published in: eLife Vol. 11
• Main Authors: Navas-Olive, Andrea, Amaducci, Rodrigo, Jurado-Parras, Maria-Teresa, Sebastian, Enrique R, de la Prida, Liset M
• Format: Journal Article
• Language: English
• Published: Cambridge eLife Sciences Publications Ltd 05.09.2022; eLife Sciences Publications, Ltd
• Subjects: convolutional neural networks; Deep learning; dorsoventral; Electrophysiological recording; Hippocampus; Neural networks; Neuropixels; Neuroscience; Oscillations; ripples; Tools and Resources
• ISSN: 2050-084X; 2050-084X
• DOI: 10.7554/eLife.77772

Local field potential (LFP) deflections and oscillations define hippocampal sharp-wave ripples (SWRs), one of the most synchronous events of the brain. SWRs 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. Artificial intelligence is finding greater use in society through its ability to process data in new ways. One particularly useful approach known as convolutional neural networks is typically used for image analysis, such as face recognition. This type of artificial intelligence could help neuroscientists analyze data produced by new technologies that record brain activity with higher resolution. Advanced processing could potentially identify events in the brain in real-time. For example, signals called sharp-wave ripples are produced by the hippocampus, a brain region involved in forming memories. Detecting and interacting with these events as they are happening would permit a better understanding of how memory works. However, these signals can vary in form, so it is necessary to detect several distinguishing features to recognize them. To achieve this, Navas-Olive, Amaducci et al. trained convolutional neural networks using signals from electrodes placed in a region of the mouse hippocampus that had already been analyzed, and ‘telling’ the neural networks whether they got their identifications right or wrong. Once the networks learned to identify sharp-wave ripples from this data, they could then apply this knowledge to analyze other recordings. These included datasets from another part of the mouse hippocampus, the rat brain, and ultra-dense probes that simultaneously assess different brain regions. The convolutional networks were able to recognize sharp-wave ripple events across these diverse circumstances by identifying unique characteristics in the shapes of the waves. These results will benefit neuroscientists by providing new tools to explore brain signals. For instance, this could allow them to analyze the activity of the hippocampus in real-time and potentially discover new aspects of the processes behind forming memories.