Ila Fiete

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 paper introduces DSA, a metric to compare systems by their dynamics, showing it captures dynamical structure missed by geometric and topological methods.

This study uses control-theoretic methods to show that propofol anesthesia reduces interareal control in the brain, leading to a directional breakdown of cortical control.

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.

The paper shows that different anesthetics destabilize neural dynamics similarly, affecting recovery and low-frequency power, and linking this to unconsciousness.

The paper examines dopamine dynamics during cocaine and fentanyl self-administration in mice, identifying distinct patterns and developing a computational model to explain compulsive behavior.

The paper identifies how prior expectations modulate brain activity during decision-making and shows that a few-parameter model captures these dynamics across the brain.

Mice infer stimulus-side priors from recent choices; prior signals are decodable pre-stimulus in 24.0% to 30.2% of 242 brain regions, including early visual and motor areas.

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.

The paper shows that slow hippocampus-to-entorhinal plasticity underlies gradual grid cell map formation and fragmentation in novel environments through longitudinal imaging and modeling.

The paper presents a dynamical map of brain-wide computation in mice, revealing broadcast sequences and non-random selectivity at the single neuron level.

The paper shows that mice with ASD-related mutations exhibit similar computational and neural anomalies in prior updating during decision-making.

This study shows that mouse retrosplenial cortex uses recurrent neural dynamics to form and refine internal hypotheses for spatial reasoning based on ambiguous landmarks.

The paper presents a multi-region brain model to study how hippocampal circuits contribute to spatial decision-making, finding that a specific architecture optimizes learning and matches experimental data.

This paper introduces a framework to construct biologically plausible continuous attractor neural networks with specified topologies and integration capabilities.

This paper introduces a neural network model of the hippocampus that integrates spatial and episodic memory by using structured scaffold states for high-capacity, error-correcting memory storage.

The paper develops a method to infer functional dynamics from a connectome of the Drosophila head direction circuit by estimating cell-type-level parameters using self-supervised and unsupervised learning.

This study uses DeLASE to show that propofol anesthesia reduces dynamic stability in macaque cortex, mirroring destabilized linear systems.

This paper demonstrates that a simple synaptic growth rule generates modular, hierarchical, and topographically organized visual pathways in a developmental model.

The paper identifies balanced excitatory and inhibitory circuitry in primate sensory cortex that amplifies weak signals while maintaining fast dynamics.

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

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 shows that deep learning models of grid cells in the entorhinal-hippocampal circuit do not reliably capture fundamental neural mechanisms and are influenced by non-fundamental implementation choices.

This study shows that place cell activity in the hippocampus is structured by a grid scaffold, challenging the notion of randomness in their responses.

The paper develops a theory for spatial map fragmentation using surprisal and predicts the existence of 'doorway' cells and improved navigation through complex environments.

This paper introduces peak selection, a process that explains how local interactions and smooth gradients can lead to robust global modular organization in biological systems.

The paper demonstrates a neural circuit mechanism that enables fast, parallel computation of the max operation in noisy environments, achieving efficiency comparable to serial computation.

This paper studies how RNNs solve a hierarchical inference task with two latent variables and disparate timescales, finding they learn behavior similar to Bayesian baselines and develop interpretable models through knowledge distillation.

This study identifies noise and speed estimation bias as primary sources of path integration errors in young and aging humans, showing age-related declines are linked to increased unbiased noise.