Biology subjects

Williams, A. H.

Publications and source records attributed to Williams, A. H..

18 recordsLinked to original sources

bioRxiv2026

A shared cortical manifold links sensory error to motor planning

The ability to detect and correct errors is central to learning and executing skilled behaviors such as speech and musical performance. Accordingly, sensory and motor regions of the brain must communicate and coordinate their activity in order to both detect errors and adapt in response to them. However, the neural mechanisms by which sensory feedback is transformed into updated motor plans across distributed cortical circuits remain unclear. Here, we identify how the mouse brain encodes error signals during a skilled acoustic task and how these signals guide motor adaptation. We developed a novel, sound-dependent behavior in which mice use real-time auditory feedback to adjust ongoing forelimb movements. Task performance critically depends on auditory cortex, where neurons encode error-related feedback that predicts both within-trial and across-trial behavioral adaptations. Acoustic error feedback alters secondary motor cortex (M2) dynamics, selectively pushing activity along dimensions that encode planning signals for upcoming movements. Notably, auditory cortex and M2 share a low-dimensional manifold that emerges only after learning the skilled behavior. This shared geometry is not engaged during simpler forelimb tasks, suggesting that it reflects a learned, context-dependent solution for coordinating distributed cortical activity. In trained mice, silencing auditory cortex disrupts M2 dynamics, consistent with a model of continuously coupled cortical interactions during skilled performance. Together, these findings identify a learned coordinate transformation that maps sensory error onto corrective motor plans. More broadly, they point to a candidate general principle for how learning can establish a shared cortical manifold that links distant brain regions, enabling sensory feedback to selectively reshape future actions and support flexible, goal-directed control.

neuroscience↗

bioRxiv2026

Tracking the Fidelity of Internal Neural Representations with Error-In-Variables Regression

Internal neural representations can systematically deviate from externally measured sensory and behavioral variables, yet neuroscientists lack a principled statistical framework to quantify these mismatches. Here we introduce a nonlinear error-in-variables regression framework that explicitly models neural activity as a function of latent internal variables that deviate from measured sensory and behavioral variables. This approach uses a flexible basis expansion and a sampling-based inference scheme to jointly infer neuron-specific tuning functions, latent trajectories, and a representational fidelity parameter{kappa} that controls the strength of coupling between latent and measured variables. On synthetic datasets, the model accurately recovers latent dynamics, tuning curves, and identifies the true fidelity regime via cross-validated marginal likelihood. Applied to population recordings from mouse anterodorsal thalamic nucleus and rat medial entorhinal cortex across distinct sensory and behavioral conditions, the framework reveals condition-dependent changes in representational fidelity, tuning gain and profile, and uncovers latent population manifolds that are obscured in conventional tuning analyses. These results establish error-in-variables regression as a powerful and computationally tractable tool for tracking the fidelity of internal neural representations in systems neuroscience experiments.

neuroscience↗

bioRxiv2025

From labels to latents: revealing state-dependent hippocampal computations with Jump Latent Variable Model

Neural activity is usually interpreted by imposing external labels (e.g., stimuli or position during locomotion) and decoding within that space (e.g. replay). While powerful, such supervision can mask structure in the data that do not correspond to the label. Unsupervised methods, in turn, often assume smooth latent dynamics and miss genuine discontinuities. We introduce a conceptually simple, computationally efficient latent variable model that infers both (i) the latent variables organizing population activity and (ii) whether their dynamics are continuous or fragmented in time. Fitting reduces to an expectation-maximization (EM) procedure that alternates two operations familiar to systems neuroscience--tuning-curve estimation and label decoding--without requiring external labels. Applied to rodent hippocampal spike recordings, the model reveals distinct population patterns at the same physical position that supervised spatial decoding fails to detect. While learned latents exhibit place-field-like tuning, their reactivation patterns are better distinguished by behavioral states. The model further identifies a continuity-fragmentation axis that characterizes population activities across sleep-wake brain states that is modulated by cholinergic inputs. By not relying on externally imposed spatial labels, our approach exposes structure that supervised approaches obscure and provides a powerful tool for datasets lacking behavioral tracking.

neuroscience↗

bioRxiv2025

Strong genotype-by-genotype and genotype-by-environment interactions induced by antibiotic resistance mutations

The often parallel evolution of antibiotic resistance in different species offers a promising basis for predicting evolutionary change. However, the extent to which parallel genetic evolution is reflected at the phenotypic level remains unclear. We investigated how genetic background and temperature influence the fitness effects of homologous rifampicin resistance mutations in absence of antibiotics across two temperatures and three species (Escherichia coli, Acinetobacter baylyi and Lactiplantibacillus plantarum). We measured the effect of resistance mutations on two growth traits (maximum growth rate and maximum optical density) across all conditions and fit linear models to this data using Bayesian inference. Our model fits provide evidence for pervasive genetic interactions in both growth traits, including mutation by species (GxG), mutation by temperature (GxE), and GxGxE interactions. In particular, the effect of resistance mutations was strongly dependent on genomic background, to the extent that mutations often reduced growth in one species but increased it in another (sign epistasis). Similarly, the effect of mutations on growth was strongly influenced by temperature, with some mutations increasing growth at one but decreasing growth at the other temperature. Most notably, three resistance mutations induced strong heat sensitivity in A. baylyi. Our results reinforce previous studies that report strong epistatic and GxE effects of resistance mutations, indicating that in the absence of additional data and more mechanistic models, predicting fitness effects of resistance mutations across species and environments can be very challenging.

evolutionary biology↗

bioRxiv2025

Comprehensive characterization of human color discrimination thresholds

Color discrimination thresholds--the smallest detectable color differences--provide a benchmark for models of color vision, enable quantitative evaluation of eye diseases, and inform the design of display technologies. Despite their importance, a comprehensive characterization of these thresholds has long been considered intractable due to the psychophysical curse of dimensionality. Here, we address this challenge using a novel semi-parametric Wishart Process Psychophysical Model (WPPM), which leverages the feature that the internal noise limiting color discrimination varies smoothly across stimulus space. The model was fit to data collected with a non-parametric adaptive trial-placement procedure, enabling efficient stimulus selection. Together, through the combination of adaptive trial placement and post hoc WPPM fitting, we achieved a comprehensive characterization of color discrimination in the isoluminant plane with only [~]6,000 trials per participant (N = 8). Once fit, the WPPM allows readouts of discrimination performance for any stimulus pair. We validated these readouts against 25 probe psychometric functions, measured with an additional 6,000 trials per participant held out from model fitting. In conclusion, our study provides a foundational dataset for color vision, and our approach generalizes beyond color to any domain in which the internal noise limiting performance varies smoothly across stimulus space, offering a powerful and efficient method for comprehensively characterizing various perceptual discrimination thresholds.

neuroscience↗

bioRxiv2025

Quantifying Differences in Neural Population Activity With Shape Metrics

Quantifying differences across species and individuals is fundamental to many fields of biology. However, it remains challenging to draw detailed functional comparisons between large populations of interacting neurons. Here, we introduce a general framework for comparing neural population activity in terms of shape distances. This approach defines similarity in terms of explicit geometric transformations, which can be flexibly specified to obtain different measures of population-level neural similarity. Moreover, differences between systems are defined by a distance that is symmetric and satisfies the triangle inequality, enabling downstream analyses such as clustering and nearest-neighbor regression. We demonstrate this approach on datasets spanning multiple behavioral tasks (navigation, passive viewing of images, and decision making) and species (mice and non-human primates), highlighting its potential to measure functional variability across subjects and brain regions, as well as its ability to relate neural geometry to animal behavior.

neuroscience↗

bioRxiv2024

Equivalence between representational similarity analysis, centered kernel alignment, and canonical correlations analysis

Centered kernel alignment (CKA) and representational similarity analysis (RSA) of dissimilarity matrices are two popular methods for comparing neural systems in terms of representational geometry. Although they follow a conceptually similar approach, typical implementations of CKA and RSA tend to result in numerically different outcomes. Here, I show that these two approaches are largely equivalent once one incorporates a mean-centering step into RSA. This equivalence holds for both linear and nonlinear variants of these methods. These connections are simple to derive, but appear to have been thus far overlooked in the context of comparing neural representations. By unifying these measures, this paper hopes to simplify a complex and fragmented literature on this subject.

neuroscience↗

bioRxiv2023

Unsupervised discovery of family specific vocal usage in the Mongolian gerbil

In nature, animal vocalizations can provide crucial information about identity, including kinship and hierarchy. However, lab-based vocal behavior is typically studied during brief interactions between animals with no prior social relationship, and under environmental conditions with limited ethological relevance. Here, we address this gap by establishing long-term acoustic recordings from Mongolian gerbil families, a core social group that uses an array of sonic and ultrasonic vocalizations. Three separate gerbil families were transferred to an enlarged environment and continuous 20-day audio recordings were obtained. Using a variational autoencoder (VAE) to quantify 583,237 vocalizations, we show that gerbils exhibit a more elaborate vocal repertoire than has been previously reported and that vocal repertoire usage differs significantly by family. By performing gaussian mixture model clustering on the VAE latent space, we show that families preferentially use characteristic sets of vocal clusters and that these usage preferences remain stable over weeks. Furthermore, gerbils displayed family-specific transitions between vocal clusters. Since gerbils live naturally as extended families in complex underground burrows that are adjacent to other families, these results suggest the presence of a vocal dialect which could be exploited by animals to represent kinship. These findings position the Mongolian gerbil as a compelling animal model to study the neural basis of vocal communication and demonstrates the potential for using unsupervised machine learning with uninterrupted acoustic recordings to gain insights into naturalistic animal behavior.

animal behavior and cognition↗

bioRxiv2023

Remapping in a recurrent neural network model of navigation and context inference

Neurons in navigational brain regions provide information about position, orientation, and speed relative to environmental landmarks. These cells also change their firing patterns ("remap") in response to changing contextual factors such as environmental cues, task conditions, and behavioral state, which influence neural activity throughout the brain. How can navigational circuits preserve their local computations while responding to global context changes? To investigate this question, we trained recurrent neural network models to track position in simple environments while at the same time reporting transiently-cued context changes. We show that these combined task constraints (navigation and context inference) produce activity patterns that are qualitatively similar to population-wide remapping in the entorhinal cortex, a navigational brain region. Furthermore, the models identify a solution that generalizes to more complex navigation and inference tasks. We thus provide a simple, general, and experimentally-grounded model of remapping as one neural circuit performing both navigation and context inference.

neuroscience↗

bioRxiv2022

Distinguishing discrete and continuous behavioral variability using warped autoregressive HMMs

A core goal in systems neuroscience and neuroethology is to understand how neural circuits generate naturalistic behavior. One foundational idea is that complex naturalistic behavior may be composed of sequences of stereotyped behavioral syllables, which combine to generate rich sequences of actions. To investigate this, a common approach is to use autoregressive hidden Markov models (ARHMMs) to segment video into discrete behavioral syllables. While these approaches have been successful in extracting syllables that are interpretable, they fail to account for other forms of behavioral variability, such as differences in speed, which may be better described as continuous in nature. To overcome these limitations, we introduce a class of warped ARHMMs (WARHMM). As is the case in the ARHMM, behavior is modeled as a mixture of autoregressive dynamics. However, the dynamics under each discrete latent state (i.e. each behavioral syllable) are additionally modulated by a continuous latent "warping variable." We present two versions of warped ARHMM in which the warping variable affects the dynamics of each syllable either linearly or nonlinearly. Using depth-camera recordings of freely moving mice, we demonstrate that the failure of ARHMMs to account for continuous behavioral variability results in duplicate cluster assignments. WARHMM achieves similar performance to the standard ARHMM while using fewer behavioral syllables. Further analysis of behavioral measurements in mice demonstrates that WARHMM identifies structure relating to response vigor.

animal behavior and cognition↗

bioRxiv2021

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

Medial entorhinal cortex (MEC) supports a wide range of navigational and memory related behaviors. Well-known experimental results have revealed specialized cell types in MEC -- e.g. grid, border, and head-direction cells -- whose highly stereotypical response profiles are suggestive of the role they might play in supporting MEC functionality. However, the majority of MEC neurons do not exhibit stereotypical firing patterns. How should the response profiles of these more "heterogeneous" cells be described, and how do they contribute to behavior? In this work, we took a computational approach to addressing these questions. We first performed a statistical analysis that shows that heterogeneous MEC cells are just as reliable in their response patterns as the more stereotypical cell types, suggesting that they have a coherent functional role. Next, we evaluated a spectrum of candidate models in terms of their ability to describe the response profiles of both stereotypical and heterogeneous MEC cells. We found that recently developed task-optimized neural network models are substantially better than traditional grid cell-centric models at matching most MEC neuronal response profiles -- including those of grid cells themselves -- despite not being explicitly trained for this purpose. Specific choices of network architecture (such as gated nonlinearities and an explicit intermediate place cell representation) have an important effect on the ability of the model to generalize to novel scenarios, with the best of these models closely approaching the noise ceiling of the data itself. We then performed in silico experiments on this model to address questions involving the relative functional relevance of various cell types, finding that heterogeneous cells are likely to be just as involved in downstream functional outcomes (such as path integration) as grid and border cells. Finally, inspired by recent data showing that, going beyond their spatial response selectivity, MEC cells are also responsive to non-spatial rewards, we introduce a new MEC model that performs reward-modulated path integration. We find that this unified model matches neural recordings across all variable-reward conditions. Taken together, our results point toward a conceptually principled goal-driven modeling approach for moving future experimental and computational efforts beyond overly-simplistic single-cell stereotypes.

neuroscience↗

bioRxiv2020

Dynamic and reversible remapping of network representations in an unchanging environment

In response to environmental changes, the medial entorhinal cortex alters its single-cell firing properties. This flexibility in neural coding is hypothesized to support navigation and memory by dividing sensory experience into unique contextual episodes. However, it is unknown how the entorhinal circuit transitions between different representations, particularly when sensory information is not delineated into discrete contexts. Here, we describe spontaneous and abrupt transitions between multiple spatial maps of an unchanging task and environment. These remapping events were synchronized across hundreds of medial entorhinal neurons and correlated with changes in running speed. While remapping altered spatial coding in individual neurons, we show that features of the environment were statistically preserved at the population-level, enabling simple decoding strategies. These findings provoke a reconsideration of how medial entorhinal cortex dynamically represents space and broadly suggest a remarkable capacity for higher-order cortical circuits to rapidly and substantially reorganize their neural representations.

neuroscience↗

bioRxiv2020

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

The brain has a remarkable, yet poorly understood, capacity to perform rapid dynamic switching between different cognitive states. Absence epilepsy, characterized by sudden transitions to and from highly synchronous thalamocortical oscillations, provides a unique window to investigate rapid state switching. Here we explored the transition into seizures in detail using simultaneous extracellular unit recordings from the thalamocortical circuit in the Scn8a mouse, a validated murine model of absence epilepsy. We find that trial-averaged neural firing in the thalamus, but not cortex, was transiently elevated several seconds prior to seizure onset. However, we observed large single-trial variability in pre-ictal dynamics both within and across subjects, suggesting possible heterogeneous transition dynamics into absence seizures. To quantify the single-trial amplitude and temporal variability, we developed a statistical model, which revealed that individual seizures are preceded by low dimensional neural dynamics that vary in amplitude and time across seizures. Interestingly, the single-trial pre-seizure amplitude modulation uncovered by the model showed strong periodicity over trials, suggesting that pre-ictal dynamics may co-modulate with arousal state. To our knowledge, our results are the first characterization of single-unit pre-ictal firing dynamics across the thalamocortical circuit in absence epilepsy. Our results argue that seizure-monitoring devices may be able to capitalize on seizure-by-seizure changes in pre-ictal activity to better predict seizure onset, and that the thalamus may be a source of clinically useful pre-ictal signatures.

neuroscience↗

bioRxiv2020

Combining tensor decomposition and time warping models for multi-neuronal spike train analysis

Recordings from large neural populations are becoming an increasingly popular and accessible method in experimental neuroscience. While the activity of individual neurons is often too stochastic to interrogate circuit function on a moment-by-moment basis, multi-neuronal recordings enable us to do so by pooling statistical power across many cells. For example, groups of neurons often exhibit correlated gain or amplitude modulation across trials, which can be statistically formalized in a tensor decomposition framework (Williams et al. 2018). Additionally, the time course of neural population dynamics can be shifted or stretched/compressed, which can be modeled by time warping methods (Williams et al. 2020). Here, I describe how these two modeling frameworks can be combined, and show some evidence that doing so can be highly advantageous for practical neural data analysis--for example, the presence of random time shifts hampers the performance and interpretability of tensor decomposition, while a time-shifted variant of this model corrects for these disruptions and uncovers ground truth structure in simulated data.

neuroscience↗

bioRxiv2019

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

Though the temporal precision of neural computation has been studied intensively, a data-driven determination of this precision remains a fundamental challenge. Reproducible spike time patterns may be obscured on single trials by uncontrolled temporal variability in behavior and cognition, or may not even be time locked to measurable signatures in either behavior or local field potentials (LFP). To overcome these challenges, we describe a general-purpose time warping framework that reveals precise spike-time patterns in an unsupervised manner, even when spiking is decoupled from behavior or is temporally stretched across single trials. We demonstrate this method across diverse systems: cued reaching in nonhuman primates, motor sequence production in rats, and olfaction in mice. This approach flexibly uncovers diverse dynamical firing patterns, including pulsatile responses to behavioral events, LFP-aligned oscillatory spiking, and even unanticipated patterns, like 7 Hz oscillations in rat motor cortex that are not time-locked to measured behaviors or LFP.

neuroscience↗

bioRxiv2019

Crippling the bacterial cell wall molecular machinery

Lytic transglycosylases (LT) are redundant enzymes that play a critical role in peptidoglycan (PG) recycling and metabolism. LT(s) role in cell wall-modifying complexes and usefulness as antimicrobial drug targets remain elusive. We determined at high-resolution a structure of the membrane-bound homolog of the soluble LT from Neisseria species with a disordered active site helix (alpha helix 30). Alpha helix 30 is crucial for binding PG during catalysis1. Here we show using an alpha helix 30 deletion strain that LT (LtgA) determines the integrity of the cell wall, participates in cell division and separation, and can be manipulated to impair the fitness of the human pathogen Neisseria meningitidis during infection. Characterization of ltgA helix deleted strain interactome identified glycan chain remodeling enzymes whose function appear to be modulated by LTs. Targeting LTs can disrupt the PG machinery, which is fatal for the bacterium, a new approach for antibiotic development.

biochemistry↗

bioRxiv2018

Unsupervised discovery of temporal sequences in high-dimensional datasets, with applications to neuroscience

Identifying low-dimensional features that describe large-scale neural recordings is a major challenge in neuroscience. Repeated temporal patterns (sequences) are thought to be a salient feature of neural dynamics, but are not succinctly captured by traditional dimensionality reduction techniques. Here we describe a software toolbox--called seqNMF--with new methods for extracting informative, non-redundant, sequences from high-dimensional neural data, testing the significance of these extracted patterns, and assessing the prevalence of sequential structure in data. We test these methods on simulated data under multiple noise conditions, and on several real neural and behavioral data sets. In hippocampal data, seqNMF identifies neural sequences that match those calculated manually by reference to behavioral events. In songbird data, seqNMF discovers neural sequences in untutored birds that lack stereotyped songs. Thus, by identifying temporal structure directly from neural data, seqNMF enables dissection of complex neural circuits without relying on temporal references from stimuli or behavioral outputs.

neuroscience↗

bioRxiv2017

Unsupervised discovery of demixed, low-dimensional neural dynamics across multiple timescales through tensor components analysis

Perceptions, thoughts and actions unfold over millisecond timescales, while learned behaviors can require many days to mature. While recent experimental advances enable large-scale and long-term neural recordings with high temporal fidelity, it remains a formidable challenge to extract unbiased and interpretable descriptions of how rapid single-trial circuit dynamics change slowly over many trials to mediate learning. We demonstrate a simple tensor components analysis (TCA) can meet this challenge by extracting three interconnected low dimensional descriptions of neural data: neuron factors, reflecting cell assemblies; temporal factors, reflecting rapid circuit dynamics mediating perceptions, thoughts, and actions within each trial; and trial factors, describing both long-term learning and trial-to-trial changes in cognitive state. We demonstrate the broad applicability of TCA by revealing insights into diverse datasets derived from artificial neural networks, large-scale calcium imaging of rodent prefrontal cortex during maze navigation, and multielectrode recordings of macaque motor cortex during brain machine interface learning.

neuroscience↗