Chambers, A. R. | Search bioRxiv

bioRxiv2021

Cell-type-specific silence in thalamocortical circuits precedes hippocampal sharp-wave ripples.

Memory consolidation requires the encoding of neocortical memory traces, which is thought to occur during hippocampal oscillations called sharp-wave ripples (SWR). Evidence suggests that the hippocampus communicates memory-related neural patterns across distributed cortical circuits via its major output pathways. Here, we sought to understand how this information is processed in the retrosplenial cortex (RSC), a primary target circuit. Using patch-clamp recordings from mice during quiet wakefulness, we found that SWR-aligned synaptic modulation is widespread but weak, and that spiking responses are sparse. However, using cell type and projection-specific two-photon calcium imaging and optogenetics, we show that, starting 1-2 seconds before SWR, superficial inhibition in RSC is reduced, along with thalamocortical input. We propose that pyramidal dendrites experience a period of decreased local inhibition and subcortical interference in a seconds-long time window preceding hippocampal SWR. This may aid communication of weak and sparse SWR-aligned excitation between the hippocampus and neocortex, and promote the selective strengthening of memory-related connections.

neuroscience↗

bioRxiv2020

RippleNet: A Recurrent Neural Network for Sharp Wave Ripple (SPW-R) Detection

Hippocampal sharp wave ripples (SPW-R) have been identified as key bio-markers of important brain functions such as memory consolidation and decision making. SPW-R detection typically relies on hand-crafted feature extraction, and laborious manual curation is often required. In this multidisciplinary study, we propose a novel, self-improving artificial intelligence (AI) 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 algorithm is trained using supervised learning on hand-curated data sets with SPW-R events. 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. The output prediction can be interpreted as the time-varying probability of SPW-R events for the duration of the input. A simple thresholding applied to the output probabilities is found to identify times of events with high precision. The reference implementation of the algorithm, named RippleNet, is 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.

neuroscience↗