Andreas Savas Tolias

This paper develops a functional digital twin of the hippocampus in bats to uncover latent neural organization during natural flight behavior.

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 paper incorporates inhibition into deep neural networks for neural prediction, showing it improves surround suppression learning but not cross-orientation inhibition.

The paper maps retinal ganglion cell functional diversity in the mouse superior colliculus and identifies specialized response types for collision detection.

This paper presents a high-bandwidth, wireless brain-computer interface device capable of chronic recordings in animals, demonstrating reliable performance over weeks.

This paper improves visual prosthetics by incorporating orientation selectivity with retinotopy to better match natural neural responses in the visual cortex.

The paper develops a hybrid modeling approach to link ion channel gene expression to the electrophysiological properties of cortical neuron types using statistical and mechanistic models.

This study shows that temporal coding in visual cortical neurons provides more stable representations of visual stimuli over time compared to firing rate coding.

The paper identifies a dual-feature encoding strategy in visual cortical neurons where neurons respond to both enhancing and suppressing visual features, showing this strategy is conserved across species.

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 uses graph-based machine learning to analyze dendritic morphology of excitatory neurons in mouse visual cortex, finding a continuous morphological landscape rather than discrete types.

This study shows that mouse V1 has an asymmetric distribution of color-opponent neurons, particularly in areas encoding the sky, which may enhance detection of dark UV-objects in daylight.

The paper investigates how macaque V4 neurons respond to natural images using features from CNNs trained on various tasks, finding V4 is best predicted by semantic features and shows task selectivity.

The paper uses deep learning to analyze single-cell neural responses in primate visual area V4, finding functional clustering that mirrors artificial vision systems.

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.

Electron microscopy of 1352 neurons in a mouse visual cortex column reveals widespread cell-type-specific inhibition, a basket-cell-targeting disinhibitory class, and interneuron motif groups sharing excitatory targets.

This paper introduces Energy Guided Diffusion (EGG) to generate neurally exciting images, outperforming gradient ascent in generalization and efficiency for macaque V4 models.

The paper synthesizes neural metamers for the mouse visual system using deep encoding models and shows they reproduce neural activity across different animals.

The paper maps synaptic connectivity between cortical pyramidal neurons and shows synapse size is modeled by a combination of binary and analog variables.

The paper identifies distinct feedback circuit motifs in cortico-cortical pathways, showing different functional dynamics in motor-sensory and visual feedback circuits.

The paper finds increased reliability of visually-evoked activity in V1 neurons of MECP2-duplication mice, suggesting altered sensory processing mechanisms in autism.

The paper uses digital twins to characterize the functional organization of mouse V1, revealing a combinatorial code of nonlinear response properties across 30 modes.

The paper introduces a CRISPR toolkit for generating intersectional genetic mice to study cellular circuits, demonstrating its use in neuronal and ovarian populations.

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

The paper generates a multimodal cell census and atlas of the mammalian primary motor cortex, revealing a unified molecular genetic landscape and cross-species taxonomy of cell types.