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

The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

3.3K
A postsynaptic neuron usually receives numerous impulses from several other presynaptic neurons. The axon hillock of the postsynaptic neuron integrates all these signals and determines the likelihood of firing an action potential.
Sometimes a single EPSP is strong enough to induce an action potential in the postsynaptic neuron. However, multiple presynaptic inputs must often create EPSPs around the same time for the postsynaptic neuron to be sufficiently depolarized to fire an action potential....
3.3K
Action Potential01:14

Action Potential

8.3K
Neurons communicate by firing action potentials—the electrochemical signal that is propagated along the axon. The signal results in the release of neurotransmitters at axon terminals, thereby transmitting information to the nervous system. An action potential is a specific "all-or-none" change in membrane potential that results in a rapid spike in voltage.
Membrane potential in neurons
Neurons typically have a resting membrane potential of about -70 millivolts (mV). When they receive...
8.3K
Reason and Intuition01:37

Reason and Intuition

6.9K
The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
6.9K
Decision Making01:20

Decision Making

230
Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
230

You might also read

Related Articles

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

Sort by
Same author

Combining radiomics and machine learning for enhanced localization of premature ventricular contractions.

Digital health·2026
Same author

Research on predicting the progression of multiple myeloma treated with bortezomib based on multimodal ensemble learning.

Digital health·2026
Same author

Integrated ultrasound radiomics and clinical data to predict PD-1 blockade efficacy in unresectable hepatocellular carcinoma.

BMC gastroenterology·2025
Same author

Predicting one-year post-surgical recurrence in colorectal liver metastasis using CT radiomics and machine learning.

PloS one·2025
Same author

Construction of a predictive model for the efficacy of anti-VEGF therapy in macular edema patients based on OCT imaging: a retrospective study.

Frontiers in medicine·2025
Same author

Tensor-powered insights into neural dynamics.

Communications biology·2025
Same journal

Combinatorial multiomic analysis from a pedigree of Sox10Dom Hirschsprung mice identifies multiple high confidence candidate modifiers of Enteric Nervous System development.

PLoS computational biology·2026
Same journal

Extracting host-specific developmental signatures from longitudinal microbiome data.

PLoS computational biology·2026
Same journal

Population sparseness determines strength of Hebbian plasticity for maximal memory lifetime in associative networks.

PLoS computational biology·2026
Same journal

Predictive coding explains asymmetric connectivity in the brain: A neural network study.

PLoS computational biology·2026
Same journal

Zooplankton feeding behavioral signatures in the morphology of macroscale prey spatial distribution.

PLoS computational biology·2026
Same journal

A brief overview of 20 years of neuroscience in PLoS Computational Biology.

PLoS computational biology·2026
See all related articles

Related Experiment Video

Updated: Sep 10, 2025

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
05:19

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

7.1K

Decoding decision-making behavior from sparse neural spiking activity.

Yuhang Zhang1,2, Tao Sun1,2,3, Boyang Zang1,2

  • 1Department of Automation, Tsinghua University, Beijing, China.

Plos Computational Biology
|August 21, 2025
PubMed
Summary
This summary is machine-generated.

Researchers developed a novel channel attention bi-directional long short-term memory network (CA-BiLSTM) model to decode mouse decision-making behavior from neural spike data. This advanced model accurately predicts behavior, offering new insights into neural mechanisms.

More Related Videos

Multi-layer Cortical Ca2+ Imaging in Freely Moving Mice with Prism Probes and Miniaturized Fluorescence Microscopy
10:35

Multi-layer Cortical Ca2+ Imaging in Freely Moving Mice with Prism Probes and Miniaturized Fluorescence Microscopy

Published on: June 13, 2017

31.4K
A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

10.0K

Related Experiment Videos

Last Updated: Sep 10, 2025

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
05:19

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

7.1K
Multi-layer Cortical Ca2+ Imaging in Freely Moving Mice with Prism Probes and Miniaturized Fluorescence Microscopy
10:35

Multi-layer Cortical Ca2+ Imaging in Freely Moving Mice with Prism Probes and Miniaturized Fluorescence Microscopy

Published on: June 13, 2017

31.4K
A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

10.0K

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Machine Learning in Biology

Background:

  • Decoding animal decision-making from neural activity is complex.
  • Sparse neural spike data across brain regions presents a significant challenge.
  • Understanding neural correlates of decision-making is crucial.

Purpose of the Study:

  • To develop an advanced model for decoding decision-making behavior from neural spike data.
  • To effectively parse sparse neural data across multiple brain regions.
  • To identify neurons critical for stable decision-making.

Main Methods:

  • Utilized a channel attention bi-directional long short-term memory network (CA-BiLSTM).
  • Incorporated an attention mechanism to localize key neurons.
  • Applied the model to electrophysiology data from the International Brain Laboratory (IBL).

Main Results:

  • The CA-BiLSTM model demonstrated high accuracy in forecasting mouse decision-making behavior.
  • The attention mechanism successfully identified neurons important for decision stability.
  • The model effectively processed sparse neural spike data.

Conclusions:

  • The developed CA-BiLSTM model offers a powerful tool for decoding neural decision-making.
  • This approach provides a novel perspective on unraveling neural decision-making mechanisms.
  • The study highlights the potential of deep learning in analyzing complex neuroscience data.