Alex H. Williams

This paper identifies a shared cortical manifold in mice that links sensory error signals to motor planning during a skilled acoustic task.

The paper introduces an error-in-variables regression framework to quantify mismatches between neural representations and measured variables, showing it accurately recovers dynamics and identifies fidelity regimes in neural data.

This study characterizes human color discrimination thresholds using a novel psychophysical model and adaptive trial placement, achieving comprehensive results with fewer trials.

This paper introduces a latent variable model to analyze hippocampal activity without external labels, revealing continuity-fragmentation dynamics and non-spatial replay in rodent behavior.

The paper introduces a framework for comparing neural population activity using shape distances, demonstrating its application across species and behavioral tasks.

This study investigates how hippocampal neurons in mice encode different variables during a task, finding that spiking activity aligns with internal action plans rather than external cues.

This paper shows that representational similarity analysis and centered kernel alignment are equivalent when mean-centering is applied to RSA, unifying two methods for comparing neural representations.

The paper shows that hippocampal neurons exhibit step-like changes in place fields, suggesting pre-existing assemblies are reused rather than new fields being formed.

The paper introduces the VCL Benchmark, a dataset for evaluating sound source localization algorithms in rodents using real and simulated audio data.

This study uses recurrent neural networks to model how navigational circuits can maintain local computations while responding to global context changes, showing activity patterns similar to entorhinal cortex remapping.

The paper identifies privileged representational axes in both biological and artificial neural networks, showing alignment between DCNNs and perceptual cortices and benefits of axis-sensitive coding.

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 paper shows that the medial entorhinal cortex can spontaneously switch between spatial maps in an unchanging environment, with population-level features preserved despite individual neuron changes.

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

This paper combines tensor decomposition and time warping models to improve analysis of multi-neuronal spike trains by addressing time shifts and uncovering hidden structure in simulated data.

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.

The paper introduces seqNMF, a software toolbox for extracting and analyzing temporal sequences in high-dimensional neural data, showing its effectiveness on simulated and real neural datasets.

This paper introduces tensor components analysis to uncover low-dimensional neural dynamics across multiple timescales in diverse datasets.