Biology subjects

Loyola, S.

Publications and source records attributed to Loyola, S..

3 recordsLinked to original sources

bioRxiv2023

Sleep-state dependent cerebellar processing in adult mice

The cerebellum is important for motor performance and adaptation as well as cognition. Sleep is essential for optimizing of all these functions, but it remains to be elucidated how sleep affects cerebellar processing. It has been suggested that sleep periods with muscle twitches entrain the cerebellum with a copy of motor commands and subsequent sensory feedback signals, to develop predictive coding of movements. If this hypothesis is correct, one expects phasic correlations between the muscle twitches and specific features of the electro-encephalography (EEG) recordings in the cerebellum during various sleep stages as well as the climbing fiber activity in the cerebellar cortex, the modulation of which is relayed from the cerebral cortex via mesodiencephalic junction and inferior olive. Here we provide evidence for coherent correlations between cerebellar and cerebral cortical sleep spindles, twitches as well as patterns of climbing fiber activity. Our data are compatible with the novel concept that muscle twitches evoke complex spike synchronicity during NREM, which in turn affects cerebellar spindle activity and cerebellar-cortical information flow, thereby entraining an internal forward model.

neuroscience↗

bioRxiv2022

How inhibitory and excitatory inputs gate output of the inferior olive

The inferior olive provides the climbing fibers to Purkinje cells in the cerebellar cortex, where they elicit all-or-none complex spikes and control major forms of plasticity. Given their important role in both short-term and long-term coordination of cerebellum-dependent behaviors, it is paramount to understand the factors that determine the output of olivary neurons. Here, we investigate how the inhibitory and excitatory inputs to the olivary neurons interact with each other, generating spiking patterns of olivary neurons that ride on their intrinsic oscillations. Using dual color optogenetic stimulation and whole cell recordings we demonstrate how intervals between the inhibitory input from the cerebellar nuclei and excitatory input from the mesodiencephalic junction affect phase and gain of the olivary output at both the sub- and suprathreshold level. When the excitatory input is activated approximately 50 ms after the inhibitory input, the phase of the intrinsic oscillations becomes remarkably unstable and the excitatory input can hardly generate any olivary spike. Instead, when the excitatory input is activated approximately 150 ms after the inhibitory input, the excitatory input can optimally drive olivary spiking, riding on top of the first cycle of the subthreshold oscillations that have been powerfully reset by the preceding inhibitory input. Simulations of a large-scale network model of the inferior olive highlight to what extent the synaptic interactions penetrate in the neuropil, generating quasi-oscillatory spiking patterns in large parts of the olivary subnuclei, the size of which also depends on the relative timing of the inhibitory and excitatory inputs.

neuroscience↗

bioRxiv2020

OptiFlex: video-based animal pose estimation using deep learning enhanced by optical flow

Deep learning based animal pose estimation tools have greatly improved animal behaviour quantification. However, those tools all make predictions on individual video frames and do not account for variability of animal body shape in their model designs. Here, we introduce the first video-based animal pose estimation architecture, referred to as OptiFlex, which integrates a flexible base model to account for variability in animal body shape with an optical flow model to incorporate temporal context from nearby video frames. This approach can be combined with multi-view information, generating prediction enhancement using all four dimensions (3D space and time). To evaluate OptiFlex, we adopted datasets of four different lab animal species (mouse, fruit fly, zebrafish, and monkey) and proposed a more intuitive evaluation metric - percentage of correct key points (aPCK). Our evaluations show that OptiFlex provides the best prediction accuracy amongst current deep learning based tools, and that it can be readily applied to analyse a wide range of behaviours.

animal behavior and cognition↗