Xaq Pitkow

The paper shows that both primates and artificial agents use eye movements to support memory when internal computation is limited, revealing a shared principle of resource allocation between internal and embodied memory.

This study uses a virtual reality task and Inverse Rational Control to identify sensory and motor differences in ASD compared to neurotypical individuals.

This study shows mice use active sensing strategies to resolve visual uncertainty in a virtual reality task, selecting informative views based on visual information availability.

This study shows that reward timing and sensory cue reliability influence how monkeys allocate foraging effort in a naturalistic task.

This study investigates how the brain generalizes emotions across individuals, finding that the insula encodes emotions and persons separately, enabling cross-person generalization.

This paper develops a low-rank tensor regression method to decompose neural responses to language features in MEG data, showing improved encoding performance and interpretable components.

NEURD automates proofreading and feature extraction from connectomics data to improve accuracy and accessibility of neural morphology analyses.

The paper presents a functional connectomics dataset of mouse visual cortex, combining calcium imaging with EM reconstruction to study neural circuits and their connectivity.

Functionally similar neurons in mouse visual cortex connect preferentially across layers and areas, including feedback, and feature tuning, not receptive-field position, predicts synapses beyond axon-dendrite proximity.

The paper shows macaques strategically abort rewards in a VR task to maximize long-term gains, with dlPFC activity reflecting this behavior.

This study shows that specialized neural modules improve learning and generalization in navigation tasks, resembling primate behavior and using a Kalman filter-like state representation.

The paper shows that the insula uses overlapping neural patterns for recognizing self and others' emotions, while other regions use different patterns.

This study examines how freely-moving macaques encode reward prediction dynamics in their prefrontal cortex during foraging decisions.

The paper analyzes the hierarchical modular structure of the Drosophila connectome using community detection methods to identify organization across scales.

This study identifies how posterior parietal cortex encodes and dynamically tracks hidden states during navigation, supported by a neural network model.

This study shows that robust deep learning models for object recognition rely on low spatial frequency information, as evidenced by their performance on adversarial attacks and common corruptions.

This paper trains a foundation model on neural activity data to predict responses to new stimuli and anatomical features, showing strong generalization across mice and tasks.

The paper identifies a novel bipartite invariance in mouse V1 neurons, where receptive fields encode shift-invariant high-frequency textures and fixed low-frequency patterns, contributing to segmentation.

The paper uses CNNs to analyze contextual modulation in visual cortex by synthesizing surround patterns, finding that pattern completion enhances responses while disruption suppresses them, consistent across mice and macaques.

This study shows that humans and macaques rely on optic flow for decision-making in a naturalistic navigation task, demonstrating sensory evidence accumulation during action-perception loops.

This study investigates how sensory modality and control dynamics affect human path integration in virtual reality, finding that vestibular signals alone are insufficient without sustained acceleration.

The paper develops a theoretical framework to quantify nonlinear neural decoding and applies it to analyze neural activity related to perceptual choices in monkeys.

Cloud-run convolutional nets aligned 19,939 serial EM sections of mouse visual cortex despite cracks and folds, segmented neurons and detected 523 million synapses; code and the reconstruction are public.

The paper uses intracranial recordings to create a spatiotemporal atlas of language-dominant cortex and identifies discrete neural states during speech production.

This paper introduces a method to extract rational thoughts from agent behavior by learning internal models and reward functions using Inverse Rational Control.

This study identifies dynamic neural network states during speech production using electrocorticographic signals from human patients.

The paper develops inception loops to identify Most Exciting Images for V1 neurons in mice, finding that these images outperform linear model stimuli and reveal complex spatial features.

A deep recurrent encoding model predicts spiking of thousands of mouse V1 neurons during natural video and carries over to noise stimuli when only its last layer is retrained.

The paper infers decoding strategies for two cortical areas in macaque monkeys using a mathematical framework that combines neural activity and inactivation experiments to resolve conflicting conclusions about brain function.

The paper shows that moving bars are processed faster than flashed bars in macaque V1, supporting the differential latency model of the flash lag illusion.