CoMCo

The Cognitive Motor Control (CoMCo) team brings together several principal investigators who share a common goal: understanding how internal variables (such as decisions, actions, plans, and expectations) and external factors (such as rewards, task contingencies, and social context) are computed in the dynamics of distributed neural networks across multiple temporal and spatial scales. Our research spans topics from visuomotor attention to the roles of motivation and social factors in decision-making and action planning, and combines experimental and computational approaches across multiple levels of analysis. Our experimental work studies primate and rodent brains to investigate the underlying brain mechanisms generating behavior, in accordance with rigorous ethical and welfare standards. Together, these approaches aim to bridge brain, behavior, and computation, and to train the next generation of scientists in an interdisciplinary, collaborative environment. Below, you can explore the main research themes led by each PI.

From Beta Rhythms to Laminar Network Dynamics Underlying Primate Visuomotor Behavior (PI Bjørg Kilavik)

My research investigates how laminar cortical dynamics and large-scale network interactions support visuomotor behavior, motor planning and prediction in primate brains. By combining laminar electrophysiology and population-level analyses, the work adopts a dynamical, network-based perspective on cortical function. A central focus is how beta-band activity might reflect both network stabilization, coordination, and inter-areal communication rather than one fixed function. We further study how visuomotor information is represented and transformed across cortical layers and areas within parieto-motor networks.

Mapping intelligent behavior onto neural dynamics: from spikes to computations (PI: Nicolas Meirhaeghe)

My research seeks to understand how dynamic patterns of neural activity across the brain support high-level cognitive processes, including but not limited to motor planning, anticipation, introspection, deception, mental simulation, problem-solving, and learning. My group works at the interface of cognitive science, neurobiology, and AI/ML, and combines behavioral experiments in human and non-human primates with hypothesis-driven analyses and modeling of the underlying neural dynamics. In practice, we use techniques from linear algebra and machine learning to relate high-dimensional neural “spiking” activity recorded at a very fine spatiotemporal resolution to the underlying cognitive processes solicited by carefully-designed experimental conditions. When relevant, we also turn to artificial models to gain further insight into how “intelligent” systems, be it biological or synthetic, function at the algorithmic and implementation level.

For more information, check out the lab website: https://mindlaboratory.org/

Neural underpinnings of social learning (PI : Simon Nougaret) : 

Social learning is a fundamental ability that enables individuals to infer the consequences of actions without direct experience, through observation of others. My research focuses on identifying the neural circuits underlying social learning. To address this question, I combine electrophysiological and chemogenetic approaches in behaving non-human primates. Using behavioral paradigms in which two monkeys interact during social learning, neuronal activity is simultaneously recorded from prefrontal and striatal regions. These structures are respectively implicated in the representation of others and in individual learning processes. In parallel, the dopaminergic system, known to support individual learning, is manipulated to assess its contribution to social learning. My central hypothesis is that neural structures encoding the value of observed actions interact with canonical individual learning circuits, relying on shared neuromodulatory mechanisms, thereby enabling learning through social observation.

Neural interactions in cortical motor networks for reach to grasp movements (PI: Thomas Brochier)

My research explores the principles of neural interactions within cortical motor networks during the planning and execution of reaching and grasping movements. I also study how these cortical motor activities modulate sensory information processing in a predictive manner, how they evolve during learning and how they adapt to postural changes. My approach combines the analysis of behavioral and electrophysiological data in non-human primates (macaques and marmosets) and in humans. It is based on acute or chronic recording of brain activity using high-density multi-electrode arrays to analyze the neural population dynamics.

Neurobiology of motivations (PI: Frederic Ambroggi)

My research examines the neurobiological processes underlying the motivation to engage in action. Using an integrative neuroscience approach that combines multi-channel electrophysiological recordings, targeted optogenetic manipulations, and diverse behavioral tasks in rats, I investigate how the limbic basal ganglia loop integrates interoceptive information (such as hunger, thirst, stress) and exteroceptive information (such as predictive stimuli and context) through its cortical and subcortical connections to convey a motivational signal to the motor system. My current project explores the mechanisms involved in goal selection. Specifically, I aim to understand how these circuits, by filtering relevant information, enable the prioritization of one need over another based on its intensity and the cost required to fulfill it.

Variability, Coordination, and Computation in Motor Cortex (PI: Alexa Riehle) 

My work combines in vivo electrophysiological recordings from non-human primates with computational and theoretical approaches to understand how distributed motor cortical networks coordinate movement preparation and execution. My research interests span network variability, spike timing and synchrony, and context-dependent motor cortical computation, emphasizing how cortical processing supports flexible and reliable motor behavior.