Scott W. Linderman

Inferring the evolving objectives that organize animal behavior

This paper develops inverse reinforcement learning to infer evolving objectives from animal behavior and shows it can identify time-varying objectives in mice.

Dynamic belief representation and updating through learned attractor-like dynamics in the frontal cortex

The paper shows that OFC population activity in mice exhibits task-specific attractor-like dynamics during belief-state inference, mirroring Value-RNN dynamics.

Architecture of the Serotonergic Projectome in the Mouse Brain

Whole-brain axon tracing in 110 mouse brains divides raphe serotonin neurons into five projectomic groups, tied to transcriptome plus position and to dissociable behavioral effects of depletion.

Excitation-inhibition balance dynamics across brain networks during naturalistic behavior

The paper investigates excitation-inhibition balance dynamics in brain networks during naturalistic behavior in rats, finding coordinated E-I dynamics and transient imbalances linked to behavioral changes.

Stiefel Manifold Dynamical Systems for Tracking Representational Drift

This paper introduces SMDS, a model that accounts for representational drift in neural data, outperforming LDS in log-likelihood and requiring fewer latent dimensions.

Cross-brain transfer of high-performance intracortical speech and handwriting BCIs

This paper demonstrates cross-brain transfer learning for intracortical BCIs decoding speech and handwriting, showing improved performance with limited training data and specific decoder design.

Life-long behavioral screen reveals an architecture of vertebrate aging

The paper develops a platform for continuous behavioral tracking of vertebrates across their lifespan and identifies distinct aging trajectories linked to transcriptomic changes and predictive models of lifespan.

Competitive integration of time and reward explains value-sensitive foraging decisions and frontal cortex ramping dynamics

The paper develops a model integrating time and reward to explain foraging decisions and identifies frontal cortex ramping dynamics as a mechanism for solving patch foraging problems.

Concerted actions of distinct serotonin neurons orchestrate female pup care behavior

The paper identifies two distinct serotonin neuron populations that regulate pup care behavior in female mice, showing how they switch function across reproductive stages.

A Bayesian Hierarchical Model of Trial-to-Trial Fluctuations in Decision Criterion

This paper introduces hMFC, a Bayesian model to estimate slow decision criterion fluctuations from limited data, showing it improves accuracy over stable criterion assumptions.

Computation-through-DynamicsToolkit: Simulated datasets and quality metrics for dynamical models of neural activity

The paper introduces CtDToolkit, a framework providing synthetic datasets and metrics to evaluate and improve neural dynamics models of neural computation.

Extracting task-relevant preserved dynamics from contrastive aligned neural recordings

This paper introduces CANDY, a framework that aligns neural and behavioral data across sessions and subjects, enabling cross-session behavior decoding and identifying shared neural dynamics.

Transcriptomically-measured gene expression predicts physiological variation across single neurons in humans and mice

The paper shows that single-cell transcriptomic data can robustly predict single-neuron physiology in humans and mice, outperforming cell type averages.

Concerted modulation of spontaneous behavior and time-integrated whole-brain neuronal activity by serotonin receptors

The paper examines how serotonin receptor agonists and antagonists affect mouse behavior and whole-brain neuronal activity, revealing relationships between behavioral changes and Fos expression patterns.

Intrinsic dynamics and neural implementation of a hypothalamic line attractor encoding an internal behavioral state

The paper identifies line attractor dynamics in hypothalamic neurons during aggressive behavior and shows their intrinsic properties and functional connectivity.

Structured flexibility in recurrent neural networks via neuromodulation

This paper introduces a neuromodulated RNN model that uses synaptic gain scaling to improve training and generalization on canonical tasks, and shows how it implements gating mechanisms found in artificial RNNs.

Emergence of belief-like representations through reinforcement learning

This paper shows that recurrent neural networks can estimate value directly from observations without explicitly forming beliefs, generating reward prediction errors similar to experimental observations.

Periodic hypothalamic attractor-like dynamics during the estrus cycle

The paper shows that hypothalamic neural activity follows attractor-like dynamics during the estrus cycle, which are hormone-regulated and linked to sexual receptivity.

Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics

Keypoint-MoSeq fits a generative model that tells tracking jitter apart from real movement, cutting pose time series into unsupervised sub-second behavioural syllables in mice.

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.

Efficient and stochastic mouse action switching during probabilistic decision making

The paper models mouse decision-making in a probabilistic task, finding that behavior can be described by equivalent models with parameters capturing stickiness and stochasticity.

Dynamic and reversible remapping of network representations in an unchanging environment

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.

Recurrent Switching Dynamical Systems Models for Multiple Interacting Neural Populations

Each recorded population gets its own latent dynamics, a discrete state sets how the populations couple, and the switch rules say which population triggers the change.

Hierarchical recurrent state space models reveal discrete and continuous dynamics of neural activity in C. elegans

A hierarchical state space model splits optical neural recordings into linear segments and pools partial data across worms, recovering discrete states whose transitions match hand-labelled crawling, reversals and turns.

Using computational theory to constrain statistical models of neural data

This paper reviews how computational theories can inform statistical models of neural data by providing constrained priors, enabling data-driven evaluation of theories.