Masaracchia, L. | Search bioRxiv

bioRxiv2023

Differential activity patterns in the upper and lower blade of the dentate gyrus

The dentate gyrus is considered the first stage in the trisynaptic circuit of the hippocampus. Granule cells of the dentate gyrus fire very sparsely with a low probability of overlapping patterns of active neurons. This characteristic supports the most prevalent view of the dentate gyrus function as that of a pattern separator: to generate dissimilar neuronal representations from overlapping input states that represent similar but not identical environments. However, there are two distinct granule cell blades that have been shown to have different activity patterns: the upper blade and the lower blade. These may support different purposes, but their differential function is not well understood. Here we have recorded calcium imaging data from both the upper and the lower blade of the dentate gyrus from two mice (the upper blade from one, the lower blade from the other), while they perform a simple decision-making task. We found that only the lower blade encodes subjective emotional response while the upper blade preferentially encodes the actual response. Importantly, we found that correlations between cells carried more information about these two behavioural variables than individual firings, suggesting that neuron correlations encode here important information above and beyond individual unit activity.

neuroscience↗

bioRxiv2023

Dissecting unsupervised learning through hidden Markov modelling in electrophysiological data

Unsupervised, data-driven methods are commonly used in neuroscience to automatically decompose data into interpretable patterns. These patterns differ from one another depending on the assumptions of the models. How these assumptions affect specific data decompositions in practice, however, is often unclear, which hinders model applicability and interpretability. For instance, the hidden Markov model (HMM) automatically detects characteristic, recurring activity patterns (so-called states) from time series data. States are defined by a certain probability distribution, whose state-specific parameters are estimated from the data. But what specific features, from all of those that the data contain, do the states capture? That depends on the choice of probability distribution and on other model hyperparameters. Using both synthetic and real data, we aim at better characterizing the behavior of two HMM types that can be applied to electrophysiological data. Specifically, we study which differences in data features (such as frequency, amplitude or signal-to-noise ratio) are more salient to the models and therefore more likely to drive the state decomposition. Overall, we aim at providing guidance for an appropriate use of this type of analysis on one or two-channel neural electrophysiological data, and an informed interpretation of its results given the characteristics of the data and the purpose of the analysis. NEW & NOTEWORTHYCompared to classical supervised methods, unsupervised methods of analysis have the advantage to be freer of subjective biases. However, it is not always clear what aspects of the data these methods are most sensitive to, which complicates interpretation. Focusing on the Hidden Markov Model, commonly used to describe electrophysiological data, we explore in detail the nature of its estimates through simulations and real data examples, providing important insights about what to expect from these models.

neuroscience↗