Mehrdad Jazayeri

OpenDrive provides an open-source microdrive system for high-density neurophysiology recording in large animals, enabling scalable and precise neuronal recordings.

The paper investigates how neural circuits in the primate frontal cortex support compositional generalization by separating computations into spatial and temporal components and identifying biases when these components are misaligned.

This study shows that rhesus monkeys learn maze-solving tasks significantly faster in a physical environment than in a virtual one, with limited performance transfer between the two.

This study shows that neurons in primate frontal cortex encode both object locations and their spatial relationships during working memory.

The paper develops a computational model of memory and learning in the brain that enables generalization and continual learning through factorized entorhinal-hippocampal and entorhinal-parietal circuits.

This study investigates how monkeys adaptively solve problems by analyzing neural activity in the frontal cortex during a decision task, revealing distinct dynamical regimes associated with different strategies.

This paper introduces NeuraGEM, a neural architecture that models online discovery of latent contexts through fast transient activity and slow synaptic plasticity, reproducing human contextual learning features and brain-like dynamics.

This study investigates how primates store multiple objects in working memory using neural data from monkeys, finding that a weighted-sum model better explains memory representation than slot or switching models.

This study investigates how the anterior cingulate cortex integrates experiential and observational evidence in a two-player game, revealing neural mechanisms for dynamic belief updating.

This study investigates how humans solve complex decision problems by combining hierarchical and counterfactual reasoning, finding that these strategies are computationally rational under specific constraints.

MEDiCINe improves motion correction in neural electrophysiology recordings, leading to more accurate spike sorting.

The paper tests the hypothesis that mental simulations underlie physical inferences by analyzing neural activity in monkeys during a ball interception task, finding a low-dimensional neural embedding that tracks the ball's position.

The paper shows that the LP-ACC pathway in mice is essential for using prior stimulus history to guide decision-making by estimating differences in evidence between trials.

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.

The paper demonstrates that monkeys use structured representations in the entorhinal cortex for mental navigation without sensory feedback, showing endogenous cognitive map recruitment.

This paper introduces AutoLFADS, an automated framework for estimating neural population dynamics from neural data without supervisory information, validated on multiple datasets.

This study identifies behavioral strategies in rodent foraging by analyzing switching dynamics and develops a method to infer mixed strategy use during dynamic tasks.

The paper investigates whether short-term synaptic plasticity can generate neural dynamics for sub-second temporal learning using cerebellar simulations.

The paper shows that primates internally maintain rhythms through widespread brain activity resembling a metronome, replaying sensory-motor engrams without external stimuli.

The paper identifies a precise neural mechanism in the frontal cortex of non-human primates that adapts to temporal statistics, supporting predictive processing of time.

This study compares human and monkey behavior in a ball interception task with RNN models to test if mental simulation aids physical inference.

This paper investigates how reinforcement affects timing variability in humans and monkeys, identifying two distinct sources of variability and their neural correlates in thalamus.

The paper analyzes neural activity in non-human primates during time delays to understand working memory and time encoding mechanisms, finding that time can be decoded with high precision and neural trajectories are low dimensional.

The paper develops a method to design recurrent neural networks with dynamics that solve specific tasks, demonstrating its application to a working memory task and exploring network flexibility and capacity.

This study introduces a method to test whether humans use model-based Bayesian computations by assessing sensitivity to equivalent prior-cost pairs that require different internal models.

This paper develops a neural circuit model to explain how humans coordinate movements with expected and unexpected temporal events, showing it matches human behavior in various timing tasks.

The paper identifies three distinct spatiotemporal mechanisms of eCRF modulation in macaque V1 microcircuits and shows how they differ across layers and processing streams.

The paper shows how prior beliefs influence behavior by shaping cortical latent dynamics through neural activity in monkeys during a time interval reproduction task.

This study investigates how the premotor cortex encodes and maintains rhythms and time perception in nonhuman primates during a visual metronome task.

The paper shows humans use a nonlinear updating algorithm for time interval estimation, leading to suboptimal performance under signal-dependent noise.