Professor Miguel Maravall
Neuronal activity underlying sensory-guided behaviour
Brains have evolved to underpin behaviour, and the best way to understand neuronal function is in contexts where an animal is behaving in pursuit of an ethologically relevant goal. Taking this seriously can change our perspective of how the brain works. For example, in textbook accounts, the function of sensory cortex is simply to filter information about the environment and pass it along to higher brain areas in a hierarchy, where this information will be used to drive decisions and actions. However, our lab and multiple others have shown that, in situations where an animal is using sensory information to pursue a reward, neurons in sensory cortex do not just respond to sensory variables. Instead, they reflect the animal’s decision to act in the task, and even the outcomes of such actions, e.g., whether they go rewarded or unrewarded (Bale et al, Current Biology, 2021). What is the purpose of this non-sensory information, and how does it alter our textbook picture of hierarchical processing? This question is central to how we understand the function of cortex, and more broadly, to our understanding of the logic of neuronal systems.
Our lab’s goal is to understand neuronal codes underpinning sensory-guided behaviour. We investigate how task-dependent responses are distributed across neuronal types in somatosensory cortex, and how they vary across behavioural situations. To this end we have developed some exciting behavioural assays in which we track mice as they explore novel environments. We combine these new behaviours with various techniques for recording neuronal activity (two-photon imaging, electrophysiology) and computational methods for unravelling neuronal codes.
Projects in the lab (including rotations) can involve combinations of designing new approaches to mouse behaviour, and tracking and measuring the animals’ responses; recording neuronal activity; or analysing activity. This work can suit students with a range of backgrounds, including biology, biological or experimental psychology, or a quantitative subject (mathematics, physics, engineering). Students will need a strong quantitative background or a keen interest in learning quantitative approaches for sensory, systems and behavioural neuroscience. The project can involve a flexible blend of experimental and computational techniques, depending on the student’s interests.
Key references
- Maravall M, de Hoz L (2025) Setting the stage for statistical learning? Sensitivity to environmental statistics in early sensory processing. Curr Opin Neurobiol 95:103139. doi: 10.1016/j.conb.2025.103139.
- Colins Rodriguez A, Loft MSE, Schiessl I, Maravall M, Petersen RS (2024) Sensory adaptation in the barrel cortex during active sensation in the behaving mouse. Scientific Reports 14: 21588. doi: 10.1038/s41598-024-70524-1.
- Bale MR, Bitzidou M, Giusto E, Kinghorn P, Maravall M (2021) Sequence learning induces selectivity to multiple task parameters in mouse somatosensory cortex. Current Biology 31: 473-485. doi: 10.1016/j.cub.2020.10.059.
- Bale MR, Maravall M (2018) Organization of sensory feature selectivity in the whisker system. Neuroscience 368: 70-80. doi: 10.1016/j.neuroscience.2017.09.014
- Bale MR, Bitzidou M, Pitas A, Brebner L, Khazim L, Anagnou S, Stevenson C, Maravall M (2017) Learning and recognition of tactile temporal sequences by mice and humans. eLife doi: 10.7554/eLife.27333
- Safaai H, von Heimendahl M, Sorando JM, Diamond ME, Maravall M (2013) Coordinated population activity underlying texture discrimination in rat barrel cortex. J Neurosci 33: 5843-5855.