Lubenov, E. V. | Search bioRxiv

bioRxiv2021

Inhibition is the hallmark of CA3 intracellular dynamics around awake ripples

Hippocampal ripples are transient population bursts that structure cortico-hippocampal communication and play a central role in memory processing. However, the mechanisms controlling ripple initiation in behaving animals remain poorly understood. Here we combine multisite extracellular and whole cell recordings in awake mice to contrast the brain state and ripple modulation of subthreshold dynamics across hippocampal subfields. We find that entorhinal input to DG exhibits UP and DOWN dynamics with ripples occurring exclusively in UP states. While elevated cortical input in UP states generates depolarization in DG and CA1, it produces persistent hyperpolarization in CA3 neurons. Furthermore, growing inhibition is evident in CA3 throughout the course of the ripple buildup, while DG and CA1 neurons exhibit depolarization transients 100 ms before and during ripples. These observations highlight the importance of CA3 inhibition for ripple generation, while pre-ripple responses indicate a long and orchestrated ripple initiation process in the awake state.

neuroscience↗

bioRxiv2017

Model-based spike sorting with a mixture of drifting t-distributions

Chronic extracellular recordings are a powerful tool for systems neuroscience, but spike sorting remains a challenge. A common approach is to fit a generative model, such as a mixture of Gaussians, to the observed spike data. Even if non-parametric methods are used for spike sorting, such generative models provide a quantitative measure of unit isolation quality, which is crucial for subsequent interpretation of the sorted spike trains. We present a spike sorting strategy that models the data as a mixture of drifting t-distributions. This model captures two important features of chronic extracellular recordings--cluster drift over time and heavy tails in the distribution of spikes--and offers improved robustness to outliers. We evaluate this model on several thousand hours of chronic tetrode recordings and show that it fits the empirical data substantially better than a mixture of Gaussians. We also provide a software implementation that can re-fit long datasets (several hours, millions of spikes) in a few seconds, enabling interactive clustering of chronic recordings. Using experimental data, we identify three common failure modes of spike sorting methods that assume stationarity. We also characterize the limitations of several popular unit isolation metrics in the presence of empirically-observed variations in cluster size and scale. We find that the mixture of drifting t-distributions model enables efficient spike sorting of long datasets and provides an accurate measure of unit isolation quality over a wide range of conditions.

neuroscience↗