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Institute of Neuroinformatics

Neural Computation and Cognition

Mante Group Picture

Research in the Mante lab lies at the intersection of experimental and theoretical neuroscience and aims at uncovering the neural computations underlying cognition and learning. We combine methods from machine learning, applied mathematics, and theoretical neuroscience to decipher high-dimensional data sets obtained with large-scale behavioral and neural recordings. Our main goal is to obtain a mechanistic understanding of how the cognitive abilities in humans and primates emerge from the collective activity of distributed networks of neurons, and how impairments in such emergent computations result in cognitive deficits observed in neurological and psychiatric disorders. In the process, we develop novel approaches for the analysis of large, high-dimensional datasets in neuroscience and beyond.

Code and Data

General purpose code for the analysis of high-dimensional data, as well as data sets we recorded, can be found here:
Code and Data

Relevant publications

Ehret et al, Population-level neural correlates of flexible avoidance learning in medial prefrontal cortex, bioRxiv, 2023 Link

Soldado-Magraner, Mante*, and Sahani*, Inferring context-dependent computations through linear approximations of prefrontal cortex dynamics, bioRxiv, 2023 Link

Galgali, Sahani, and Mante, Residual dynamics resolves recurrent contributions to neural computation, Nature Neuroscience, 2023 Link

Krause et al, Operative dimensions in unconstrained connectivity of recurrent neural networks, Advances in Neural Information Processing Systems (NeurIPS), 2022 Link

Calangiu, Kollmorgen, Reppas, and Mante, Primate pre-arcuate cortex actively maintains persistent representations of saccades from plans to outcomes, bioRxiv, 2022 Link

Marks et al, Deep-learning based identification, pose estimation and end-to-end behaviour classification for interacting primates and mice in complex environments, Nature Machine Intelligence, 2022 Link

Schoenfeld et al, Dendritic integration of sensory and reward information facilitates learning, bioRxiv, 2021 Link

Kollmorgen, Hahnloser, and Mante, Nearest neighbours reveal fast and slow components of motor learning, Nature, 2020 Link

Aoi, Mante, and Pillow, Prefrontal cortex exhibits multidimensional dynamic encoding during decision-making, Nature Neuroscience, 2020 Link

Kollmorgen, Newsome, and Mante, Spatial and temporal structure of choice representations in prefrontal cortex, bioRxiv, 2019 Link

Galgali and Mante, Set in one’s thoughts, Nature Neuroscience, 2018 Link

Mante, Sussillo, Shenoy, and Newsome, Context-dependent computation by recurrent dynamics in prefrontal cortex, Nature, 2013 Link

Work with primates

An important part of our research involves work with a group of four male rhesus macaques, named Charles, Max, Paul, and Alan. Information about the life of our monkeys and our work with them can be found here.