Detection Algorithms

Our website currently offers 4 major algorithms for the detection of sharp wave ripples:

Ripple Band Power

This classical method remains one of the most widely used algorithms for the detection of sharp wave ripples. While it is simple in its operations, the detected events may contain false positives, especially in noisy datasets.

It operates in the following steps:

  1. Filter the raw extracellular signal in the ripple band (150-250 Hz).
  2. Compute the RMS power in a sliding window.
  3. Standardize the power using z-score.
  4. Set a detection threshold.
  5. Detect peak, start, and end points for each potential sharp wave ripple event.

The detected sharp wave ripples are often manually curated by expert scientists to filter out the false positives.

High Synchrony Events

This approach, first described by Gridchyn et al. 2020, detects sharp wave ripples based on multi-unit activity. The algorithm relies on a large number of simultaneously recorded units and directly captures sharp wave ripple events associated with large multi-unit activity. Thus, sharp wave ripple events associated with low multi-unit activity may not be detected using this approach. However, it is worth noting that about 50% of the high synchrony events are followed by a ripple within 50 milliseconds.

Ripple Net

Ripple Net is a deep neural network-based detection algorithm described in Hagen et al. 2021.

Abstract: Hippocampal sharp wave ripples (SPW-R) have been identified as key biomarkers of important brain functions such as memory consolidation and decision making. Understanding their underlying mechanisms in healthy and pathological brain function and behavior relies on accurate SPW-R detection. In this multidisciplinary study, we propose a novel, self-improving artificial intelligence (AI) detection method in the form of deep Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM) layers that can learn features of SPW-R events from raw, labeled input data. The approach contrasts conventional routines that typically rely on hand-crafted, heuristic feature extraction and often laborious manual curation. The algorithm is trained using supervised learning on hand-curated datasets with SPW-R events obtained under controlled conditions. The input to the algorithm is the local field potential (LFP), the low-frequency part of extracellularly recorded electric potentials from the CA1 region of the hippocampus. Its output predictions can be interpreted as time-varying probabilities of SPW-R events for the duration of the inputs. A simple thresholding applied to the output probabilities is found to identify times of SPW-R events with high precision. The non-causal or bidirectional variant of the proposed algorithm demonstrates consistently better accuracy compared to the causal or unidirectional counterpart. Reference implementations of the algorithm, named 'RippleNet,' are open source, freely available, and implemented using a common open-source framework for neural networks (tensorflow.keras) and can be easily incorporated into existing data analysis workflows for processing experimental data.

CNN Ripple

CNN Ripple is a convolutional neural network-based detection algorithm described in Navas-Olive et al. 2022.

Abstract: 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 has permitted advances, the surge of ultra-dense recordings now calls for new automatic detection strategies. Here, we show how one-dimensional convolutional networks operating over high-density LFP hippocampal recordings allowed for the 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 with the emergence of SWR, prompting novel classification criteria. To gain interpretability, we developed a method to interrogate the operation of the artificial network. We found it relied on feature-based specialization, which permits 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.

Have a novel algorithm?

Have you developed a novel algorithm for the detection of sharp wave ripples? We encourage researchers to submit their detection algorithms to the community and benchmark their performance on a diverse set of neuroscience datasets. Please get in touch with us for the addition of new algorithms!