Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

State Space Representation01:27

State Space Representation

The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
Neural Circuits01:25

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

The Central Nucleus of the Amygdala Encodes the Motivation to Pursue Ethanol.

bioRxiv : the preprint server for biology·2026
Same author

Sequence termination cues drive automated habit-like strategy via dopamine-mediated processes.

Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology·2026
Same author

Artificial intelligence for adaptive neuromodulation in drug-resistant epilepsy.

Epilepsia·2026
Same author

Revealing abrupt transitions from goal-directed to habitual behavior.

Nature communications·2026
Same author

Fast and accessible morphology-free functional fluorescence imaging analysis.

PLoS computational biology·2026
Same author

Memory erasure by dopamine-gated retrospective learning.

bioRxiv : the preprint server for biology·2026

Related Experiment Videos

Uncovering internal states with a robust shared-state multi-neuron GLM-HMM framework.

Aamna Lawrence, Eva Yezerets, Patricia H Janak

    Biorxiv : the Preprint Server for Biology
    |July 10, 2026
    PubMed
    Summary
    This summary is machine-generated.

    We developed a robust framework to analyze neural population activity and uncover brain states. Our method accurately models complex neural data, improving understanding of brain-state-dependent behavior.

    Related Experiment Videos

    Area of Science:

    • Neuroscience
    • Computational Neuroscience
    • Systems Neuroscience

    Background:

    • Neural systems exhibit dynamic firing states influencing behavior and environmental responses.
    • Traditional models like Hidden Markov Models (HMMs) struggle with complex neural data, especially multi-neuron activity, due to sparsity and low trial counts.
    • Generalized Linear Models (GLMs) combined with HMMs (GLM-HMMs) have been used for behavioral data but are challenging to apply to neural recordings.

    Purpose of the Study:

    • To develop a robust multi-neuron GLM-HMM framework for uncovering latent brain states from population neural activity.
    • To incorporate time-stamped task variables and spike histories into the model for a comprehensive analysis.
    • To address challenges in fitting GLM-HMMs to sparse, collinear, and low-count neuronal datasets.

    Main Methods:

    • Developed a modified expectation-maximization procedure for stable parameter estimation.
    • Implemented neuron-adaptive penalization to handle covariate collinearity and sparse spiking.
    • Utilized a trust-region algorithm for robust M-step convergence with ill-conditioned Hessians.
    • Employed leave-one-out cross-validation for model evaluation on low-trial datasets.

    Main Results:

    • Successfully fitted multi-neuron GLM-HMMs to electrophysiological data from primates and rodents.
    • Demonstrated stable model convergence and reliable estimation of Poisson GLM coefficients.
    • Identified behavioral relevance of inferred latent states in decision-making tasks.
    • Showcased the utility of the framework for analyzing complex neural population dynamics.

    Conclusions:

    • The proposed GLM-HMM framework provides a robust method for analyzing neural population activity and inferring brain states.
    • The framework overcomes key challenges in fitting complex models to neural data, enabling better understanding of brain-behavior relationships.
    • This approach facilitates the study of how dynamic neural states influence behavior in various cognitive tasks.