Physiology seminars

Since Darwin, comparative research has shown that most animals share basic timing capacities, such as the ability to process temporal regularities and produce rhythmic behaviors. What seems to be more exclusive, however, are the capacities to generate temporal predictions and to display anticipatory behavior at salient time points. These abilities are associated with subcortical structures like basal ganglia (BG) and cerebellum (CE), which are more developed in humans as compared to nonhuman animals. In the first research line, we investigated the basic capacities to extract temporal regularities from the acoustic environment and produce temporal predictions. We did so by adopting a comparative and translational approach, thus making use of a unique EEG dataset including 2 macaque monkeys, 20 healthy young, 11 healthy old participants and 22 stroke patients, 11 with focal lesions in the BG and 11 in the CE. In the second research line, we holistically explore the functional relevance of body-brain physiological interactions in human behavior. Thus, a series of planned studies investigate the functional mechanisms by which body signals (e.g., respiratory and cardiac rhythms) interact with and modulate neurocognitive functions from rest and sleep states to action and perception. This project supports the effort towards individual profiling: are individuals’ timing capacities (e.g., rhythm perception and production), and general behavior (e.g., individual walking and speaking rates) influenced / shaped by body-brain interactions?

Sensory experience and perceptual learning changes receptive field properties of cortical pyramidal neurons possibly mediated by long-term potentiation (LTP) of synapses. We have previously shown in the mouse somatosensory cortex (S1) that sensory-driven LTP in layer (L) 2/3 pyramidal neurons is dependent on higher order thalamic feedback from the posteromedial nucleus (POm), which is thought to convey contextual information from various cortical regions integrated with sensory input. We have followed up on this work by dissecting the cortical microcircuitry that underlies this form of LTP. We found that repeated pairing of Pom thalamocortical and intracortical pathway activity in brain slices induces NMDAr-dependent LTP of the L2/3 synapses that are driven by the intracortical pathway. Repeated pairing also recruits activity of vasoactive intestinal peptide (VIP) interneurons, whereas it reduces the activity of somatostatin (SST) interneurons. VIP interneuron-mediated inhibition of SST interneurons has been established as a motif for the disinhibition of pyramidal neurons. By chemogenetic interrogation we found that activation of this disinhibitory microcircuit motif by higher-order thalamic feedback is indispensable for eliciting LTP. Preliminary results in vivo suggest that VIP neuron activity also increases during sensory-evoked LTP. Together, this suggests that the higherorder thalamocortical feedback may help modifying the strength of synaptic circuits that process first-order sensory information in S1. To start characterizing the relationship between higher-order feedback and cortical plasticity during learning in vivo, we adapted a perceptual learning paradigm in which head-fixed mice have to discriminate two types of textures in order to obtain a reward. POm axons or L2/3 pyramidal neurons labeled with the genetically encoded calcium indicator GCaMP6s were imaged during the acquisition of this task as well as the subsequent learning of a new discrimination rule. We found that a subpopulation of the POm axons and L2/3 neurons dynamically represent textures. Moreover, upon a change in reward contingencies, a fraction of the L2/3 neurons re-tune their selectivity to the texture that is newly associated with the reward. Altogether, our data indicates that higher-order thalamic feedback can facilitate synaptic plasticity and may be implicated in dynamic sensory stimulus representations in S1, which depends on higher-order features that are associated with the stimuli.

Birdsong emerges when a set of highly interconnected brain areas manage to generate a complex output. The similarities between birdsong production and human speech have positioned songbirds as unique animal models for studying learning and production of this complex motor skill. In this work, we developed a low dimensional model for a neural network in which the variables were the average activities of different neural populations within the nuclei of the song system. This neural network is active during production, perception and learning of birdsong. We performed electrophysiological experiments to record neural activity from one of these nuclei and found that the low dimensional model could reproduce the neural dynamics observed during the experiments. Also, this model could reproduce the respiratory motor patterns used to generate song. We showed that sparse activity in one of the neural nuclei could drive a more complex activity downstream in the neural network. This interdisciplinary work shows how low dimensional neural models can be a valuable tool for studying the emergence of complex motor tasks