Biology subjects

Fredes, F.

Publications and source records attributed to Fredes, F..

4 recordsLinked to original sources

bioRxiv2024

Frequency-specific theta states in the hippocampus modulate population activity with respect to behavioural context

Neural activity reflects both external stimuli and the brains internal state, which shapes how information is processed and perceived. An example of modulation of neural responses by network states is phase-precession in the hippocampus, where the phase of theta oscillations affects the firing of single neurons (place cells) and its relation to the external world. Here, we examine a different form of relationship between oscillations and neural activity, where frequency and power of the oscillation, instead of phase, are associated with differential neural firing patterns at the population level. We refer to this as ensemble pattern reconfiguration. To study this effect, we use electrophysiological recordings of rats performing an odour-memory (non-spatial) task. Using a data-driven model, we identified two distinct theta states: low-power-lower-theta (LPLT) and high-power-higher-theta (HPHT). Through regression and decoding analyses, we found that these states are associated to modulations in hippocampal neural ensemble activity representing trial outcome - though the effect did not consistently reach statistical significance in all our subjects. Our findings suggest that power and frequency variations within theta oscillations may be linked to the reconfiguration of neural firing at the network level, motivating further investigation in larger datasets.

neuroscience↗

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↗

bioRxiv2019

Novelty gates memory formation through ventro-dorsal hippocampal interaction

Novelty facilitates formation of memories. The detection of novelty and storage of contextual memories are both mediated by the hippocampus, yet the mechanisms that link these two functions remain to be defined. Dentate granule cells (GCs) of the dorsal hippocampus fire upon novelty exposure forming engrams of contextual memory. However, their key excitatory inputs from the entorhinal cortex are not responsive to novelty and are insufficient to make dorsal GCs fire reliably. Here we uncover a powerful glutamatergic pathway to dorsal GCs from ventral hippocampal mossy cells (MCs) that relays novelty, and is necessary and sufficient for driving dorsal GCs activation. Furthermore, manipulation of ventral MCs activity bidirectionally regulates novelty-induced contextual memory acquisition. Our results show that ventral MCs activity controls memory formation through an intra-hippocampal interaction mechanism gated by novelty.

neuroscience↗