Manuel Beirán

The paper investigates how motivational context affects cortico-medullary communication during different types of licking behavior in mice.

This paper develops a theory of multi-tasking in neural circuits, distinguishing between sequential and simultaneous task performance and introduces a model of task barcodes to parameterize task selectivity.

This paper introduces CURBD, a method using data-constrained recurrent neural networks to infer brain-wide interactions from neural recordings across species.

The paper develops a framework to predict neural network activity using connectome-constrained recurrent networks and shows that targeted neuron recordings can resolve ambiguities in network dynamics.

This study identifies neural correlates of perceptual bias in the prefrontal cortex of monkeys using vibrotactile frequency discrimination task data.

This paper develops and tests neural networks that perform transitive inference, showing they generalize and exhibit behaviors similar to humans, while also revealing inconsistencies between human behavior and certain network solutions.

The paper shows that mice learning to use novel optogenetic activity patterns in V1 experience amplified neural responses, suggesting cortical plasticity improves behavioral performance.

The paper shows that low-dimensional neural manifolds enable parametric control of flexible timing through tonic inputs in trained recurrent networks and confirms this in monkey behavior.

This study trains spiking recurrent neural networks to discriminate time intervals and finds that they exhibit contraction biases similar to animal behavior, encoded through geometric features of neural trajectories.

This study shows that population structure and dynamical dimensionality complement each other in shaping neural computations, with non-random structures enabling flexible input-output mappings.

This study shows dopamine firing encodes reward prediction errors and motivation during a working memory task in monkeys, with distinct roles for phasic and tonic activity.

The paper examines how internal bias influences dopamine neuron activity during decision-making tasks in monkeys, showing bias affects phasic activity and reward prediction errors but not delay period activity.