Biology subjects

Duncker, L.

Publications and source records attributed to Duncker, L..

5 recordsLinked to original sources

bioRxiv2025

Sensory Compression as a Unifying Principle for Action Chunking and Time Coding in the Brain

The dorsolateral striatum (DLS) supports diverse time-sensitive behaviors--action chunking, duration estimation, and motor timing--yet no single framework is able to explain all of these phenomena. Here, we propose that the massive convergence of cortical projections onto the striatum provides such a framework. We develop a corticostriatal neural network model in which a recurrent cortical module communicates with a recurrent striatal module through a low-dimensional, noisy bottleneck, with the whole system trained via reinforcement learning. Across three DLS-associated tasks, compression produces a consistent computational motif whereby cortex provides low-dimensional control signals while the striatum generates stable, time-encoding dynamics. This separation gives rise to chunking behavior with action slipping, intensity-biased duration judgements with stimulus-modulated time coding, and stereotyped motor timing programs. Perturbation of the compressed cortical signal causally shifts behavior while preserving sequential structure in striatum activity. In sum, our results unify information-theoretic and dynamical systems perspectives of basal ganglia function to link anatomical compression to a variety of temporally-sensitive sensorimotor behaviors implicated in the DLS.

neuroscience↗

bioRxiv2022

Direct neural perturbations reveal a dynamical mechanism for robust computation

The rich repertoire of skilled mammalian behavior is the product of neural circuits that generate robust and flexible patterns of activity distributed across populations of neurons. Decades of associative studies have linked many behaviors to specific patterns of population activity, but association alone cannot reveal the dynamical mechanisms that shape those patterns. Are local neural circuits high-dimensional dynamical reservoirs able to generate arbitrary superpositions of patterns with appropriate excitation? Or might circuit dynamics be shaped in response to behavioral context so as to generate only the low-dimensional patterns needed for the task at hand? Here, we address these questions within primate motor cortex by delivering optogenetic and electrical microstimulation perturbations during reaching behavior. We develop a novel analytic approach that relates measured activity to theoretically tractable, dynamical models of excitatory and inhibitory neurons. This computational model captures the dynamical effects of these perturbations and demonstrates that motor cortical activity during reaching is shaped by a self-contained, low-dimensional dynamical system. The subspace containing task-relevant dynamics proves to be oriented so as to be robust to strong non-normal amplification within cortical circuits. This task dynamics space exhibits a privileged causal relationship with behavior, in that stimulation in motor cortex perturb reach kinematics only to the extent that it alters neural states within this subspace. Our results resolve long-standing questions about the dynamical structure of cortical activity associated with movement, and illuminate the dynamical perturbation experiments needed to understand how neural circuits throughout the brain generate complex behavior.

neuroscience↗

bioRxiv2022

Scalable variational inference for low-rank spatio-temporal receptive fields

An important problem in systems neuroscience is to characterize how a neuron integrates sensory inputs across space and time. The linear receptive field provides a mathematical characterization of this weighting function, and is commonly used to quantify neural response properties and classify cell types. However, estimating receptive fields is difficult in settings with limited data and correlated or high-dimensional stimuli. To overcome these difficulties, we propose a hierarchical model designed to flexibly parameterize low-rank receptive fields. The model includes Gaussian process priors over spatial and temporal components of the receptive field, encouraging smoothness in space and time. We also propose a new temporal prior called temporal relevance determination (TRD), which imposes a variable degree of smoothness as a function of time lag. We derive a scalable algorithm for variational Bayesian inference for both spatial and temporal receptive field components and hyperparameters. The resulting estimator scales to high-dimensional settings in which full-rank maximum likelihood or a posteriori estimates are intractable. We evaluate our approach on neural data from rat retina and primate cortex, and show that it substantially out-performs a variety of existing estimators. Our modeling approach will have useful extensions to a variety of other high-dimensional inference problems with smooth or low-rank structure.

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↗

bioRxiv2018

Temporal alignment and latent Gaussian process factor inference in population spike trains

We introduce a novel scalable approach to identifying common latent structure in neural population spike-trains, which allows for variability both in the trajectory and in the rate of progression of the underlying computation. Our approach is based on shared latent Gaussian processes (GPs) which are combined linearly, as in the Gaussian Process Factor Analysis (GPFA) algorithm. We extend GPFA to handle unbinned spike-train data by incorporating a continuous time point-process likelihood model, achieving scalability with a sparse variational approximation. Shared variability is separated into terms that express condition dependence, as well as trial-to-trial variation in trajectories. Finally, we introduce a nested GP formulation to capture variability in the rate of evolution along the trajectory. We show that the new method learns to recover latent trajectories in synthetic data, and can accurately identify the trial-to-trial timing of movement-related parameters from motor cortical data without any supervision.

neuroscience↗