Surya Ganguli

A predictive theory of experimental design for inferring neural population geometry in large-scale recordings

The paper develops a predictive theory for experimental design in neural population geometry inference, showing that more neurons allow accurate inference with fewer trials.

Age-related changes in behavioural and neural variability in a decision-making task

This study examines age-related changes in neural and behavioural variability during a decision-making task in mice, finding increased neural variability and reduced variability quenching in specific brain regions.

Intrinsic space-time couplings governing multi-scale cortical dynamics

This study introduces a new microscopy technique to track neocortical activity with high spatial and temporal resolution, revealing fundamental dynamical elements and their properties across different neural activity regimes.

Gaussian Process Inference Reveals Non-separability of Positionand Velocity Tuning in Grid Cells

The paper uses Gaussian Process inference to analyze 4D tuning curves of grid cells in rats, revealing non-separability of position and velocity tuning.

Large-scale cellular-resolution read/write of activity enables discovery of cell types defined by complex circuit properties

The paper introduces a method to monitor and control neural activity at cellular resolution in mice, discovering new cell types with unique functional properties.

Interpretable Machine Learning Identifies an Emergent Absence Seizure Mechanism

The paper uses interpretable machine learning to derive dynamical equations from seizure data, revealing a chaotic attractor mechanism in thalamocortical circuits that drives absence seizures.

A Brain-Wide Map of Neural Activity during Complex Behaviour

The paper presents a brain-wide neural activity map from 621,733 neurons across 139 mice performing a decision-making task, showing how neural activity encodes task variables across brain regions.

One-shot entorhinal maps enable flexible navigation in novel environments

The paper shows that grid cells in the entorhinal cortex form stable maps of novel environments quickly and with minimal distortion, enabling flexible navigation through downstream plasticity.

Adaptation of retinal discriminability to natural scenes

The paper analyzes how retinal ganglion cells adapt discriminability to natural scenes using a model of neural sensitivity and variability, finding that correlated noise reduces information transmission.

Lightning Pose: improved animal pose estimation via semi-supervised learning, Bayesian ensembling, and cloud-native open-source tools

On prefrontal working memory and hippocampal episodic memory: Unifying memories stored in weights and activity slots

This paper develops a mathematical theory unifying hippocampal episodic memory and prefrontal working memory by linking memory storage in synapses to controllable neural activity slots.

The dynamic neural code of the retina for natural scenes

The paper develops a three-layer network model to predict retinal responses to natural scenes, revealing fast dynamics in ganglion cell signaling and the role of interneurons in generating the retinal code.

Data architecture for a large-scale neuroscience collaboration

The paper describes a modular data architecture for a neuroscience collaboration, enabling data contribution, access, and analysis across multiple labs.

Information Geometry of the Retinal Representation Manifold

The paper uses information geometry to analyze retinal representations of natural stimuli, finding that noise correlations limit information rather than enhance it, and that population coding improves information transmission.

An approximate line attractor in the hypothalamus that encodes an aggressive internal state

The paper identifies an approximate line attractor in the VMHvl that encodes aggression intensity through slow-ramping dynamics in Esr1 neurons.

When and why grid cells appear or not in trained path integrators

The paper reassesses claims about grid cell emergence in path-integrator circuits, finding consistency with prior theory.

A unified theory for the computational and mechanistic origins of grid cells

This paper shows that hexagonal grid cell patterns emerge in neural networks trained for path integration and develops methods to analyze these networks for mechanistic insights.

Recurrent Connections in the Primate Ventral Visual Stream Mediate a Tradeoff Between Task Performance and Network Size During Core Object Recognition

This study develops ConvRNN models to simulate primate ventral visual stream processing, finding that recurrent networks achieve better task performance and efficiency than deeper feedforward networks.

Distinct in vivo dynamics of excitatory synapses onto cortical pyramidal neurons and inhibitory interneurons

The paper uses in vivo imaging to compare synaptic dynamics between excitatory synapses on pyramidal neurons and inhibitory interneurons, finding that PV+ synapses are more stable and less heterogeneous.

Explaining heterogeneity in medial entorhinal cortex with task-driven neural networks

This study uses task-driven neural networks to show that heterogeneous MEC cells are functionally significant and that reward-modulated models better capture MEC response profiles than traditional models.

The Geometry of Concept Learning

The paper proposes a geometric framework for few-shot learning using neural manifolds and a single plastic neuron, demonstrating its effectiveness on visual concepts and linking neurophysiology to behavior.

GluD2- and Cbln1-mediated Competitive Synaptogenesis Shapes the Dendritic Arbors of Cerebellar Purkinje Cells

This study shows that GluD2 and Cbln1 mediate competitive synaptogenesis, shaping Purkinje cell dendritic arbors in the cerebellum through competitive synapse formation.

An Evolving-Dynamic Network Activity Approach toEpileptic Seizure Prediction using Machine Learning

This paper uses machine learning models to predict epileptic seizures in mice, achieving 98% accuracy with logistic regression and convolutional neural networks.

Distinct algorithms for combining landmarks and path integration in medial entorhinal, visual andretrosplenial cortex

The paper shows that medial entorhinal cortex uses Kalman filtering to integrate path integration and landmarks, while visual cortex codes position retrospectively and retrosplenial cortex is intermediate.

Causal coupling between neural activity, metabolism, and behavior across the Drosophila brain

The paper demonstrates causal coupling between neural activity, metabolism, and behavior in the Drosophila brain using simultaneous measurements of activity and metabolic flux across behavioral states.

Thalamic activity patterns unfolding over multiple time scales predict seizure onset in absence epilepsy

The paper identifies pre-seizure thalamic activity patterns in absence epilepsy that vary across seizures and may improve seizure prediction.

Discovering precise temporal patterns in large-scale neural recordings through robust and interpretable time warping

This paper introduces a time warping framework to uncover precise spike-time patterns in neural recordings across different systems, revealing both expected and unexpected firing dynamics.

Accurate estimation of neural population dynamics without spike sorting

The paper shows that neural dynamics can be accurately estimated without spike sorting by using multi-unit threshold crossings, enabling new analyses and electrode array design.

A Unified Theory of Early Visual Representations from Retina to Cortex through Anatomically Constrained Deep CNNs

Imposing an optic-nerve-like bottleneck in a deep convolutional neural network trained on image recognition causes antagonistic center-surround retinal receptive fields and orientation-tuned primary visual cortex representations to emerge simultaneously.

Emergent Elasticity in the Neural Code for Space

The paper develops a neural attractor model that explains how animals construct spatial maps using self-motion and landmark cues, showing emergent elasticity in grid cell firing patterns.