Valerio Mante

This study shows that low initial weight variance in recurrent neural networks leads to overlapping weight subspaces for subtasks, while high variance results in more segregated subspaces.

This study shows that hippocampal CA1 coding changes during categorization training in mice, with factorized representations of choice and outcome variables improving behavioral performance.

This study examines the long-term effects of prenatal exposure to WWII air raids on adult brain structure, cognition, and health using a large dataset and genetic analysis.

This study uses data-driven methods to reduce the dimensionality of RNNs and identify core dynamics, leading to new insights into neuronal decision-making and memory.

This study examines neural activity in primate dlPFC during saccadic movements, finding that retrospective representations of recent saccades are reliable, while prospective planning-like activity is limited to reward-eligible saccades.

The paper infers context-dependent neural computation mechanisms in the prefrontal cortex using linear dynamical models fitted to monkey behavioral data, revealing two distinct input selection and integration mechanisms.

The paper analyzes residual dynamics in macaque prefrontal cortex during a saccade-based decision task to uncover recurrent neural contributions and their stability.

This study uses calcium imaging in mice to identify population-level neural activity in the medial prefrontal cortex during avoidance learning, showing that avoidance-related activity predicts behavior and distinguishes between different learned actions.

This paper develops a theoretical framework explaining individual variability in decision-making by analyzing neural and behavioral data from rats performing a context-dependent task.

This paper identifies low-dimensional 'operative dimensions' in RNNs that significantly influence dynamics, showing that much of the high-dimensional connectivity is functionally irrelevant.

The paper shows that dendritic responses in L5 pyramidal neurons during learning correspond to unsigned temporal difference error components, with salience signals aiding task learning.

This paper introduces a deep learning pipeline for tracking and classifying behaviors of interacting primates and mice in complex environments using video data.

The paper investigates multi-dimensional dynamic encoding in prefrontal cortex during decision-making in monkeys, revealing time-evolving representations of decision variables and context.

The paper introduces a nearest-neighbor method to analyze song development in zebra finches, revealing complex temporal dynamics in vocal learning and development.

The paper examines how choice representations are structured in primate prefrontal cortex, finding that diverse neural responses reflect persistent population patterns rather than transient dynamics.