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

Neural Circuits01:25

Neural Circuits

1.3K
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...
1.3K
Neural Regulation01:37

Neural Regulation

39.5K
Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
39.5K
Parallel Processing01:20

Parallel Processing

181
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
181
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

129
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...
129
Neural Control of Respiration01:18

Neural Control of Respiration

2.6K
The neural regulation of respiration is a meticulously coordinated process primarily controlled by the respiratory centers located within the brainstem. These centers, composed of specialized neurons, transmit nerve impulses that control the contraction and relaxation of our respiratory muscles.
Respiratory Centers in the Brainstem
Two primary areas comprise the respiratory center: the medullary respiratory center in the medulla oblongata and the pontine respiratory group in the pons. The...
2.6K

You might also read

Related Articles

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

Sort by
Same author

Control of cortical population activity with patterned microstimulation.

bioRxiv : the preprint server for biology·2026
Same author

"Awe-scillations": EEG spectral and complexity representations of awe.

bioRxiv : the preprint server for biology·2025
Same author

Spontaneous Dynamics Predict the Effects of Targeted Intervention in Hippocampal Neuronal Cultures.

bioRxiv : the preprint server for biology·2025
Same author

Towards model-based design of causal manipulations of brain circuits with high spatiotemporal precision.

bioRxiv : the preprint server for biology·2025
Same author

Predicting the effect of micro-stimulation on macaque prefrontal activity based on spontaneous circuit dynamics.

Physical review research·2024
Same author

Probabilistic modeling reveals coordinated social interaction states and their multisensory bases.

bioRxiv : the preprint server for biology·2024

Related Experiment Video

Updated: Jul 19, 2025

Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms
08:28

Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms

Published on: March 3, 2023

1.1K

Multitasking via baseline control in recurrent neural networks.

Shun Ogawa1, Francesco Fumarola1, Luca Mazzucato2,3

  • 1Laboratory for Neural Computation and Adaptation, RIKEN Center for Brain Science, Wako, Saitama 351-0198, Japan.

Proceedings of the National Academy of Sciences of the United States of America
|August 7, 2023
PubMed
Summary
This summary is machine-generated.

Behavioral state changes, like arousal, modulate neural activity. This study shows baseline input control in reservoir computing enables multitasking and optimal memory, offering insights for brain-inspired AI.

Keywords:
decision-makingmean field theorymultitaskingrecurrent neural networksreservoir computing

More Related Videos

A Fully Automated and Highly Versatile System for Testing Multi-cognitive Functions and Recording Neuronal Activities in Rodents
09:13

A Fully Automated and Highly Versatile System for Testing Multi-cognitive Functions and Recording Neuronal Activities in Rodents

Published on: May 3, 2012

14.4K
Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning
11:20

Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning

Published on: June 2, 2014

12.0K

Related Experiment Videos

Last Updated: Jul 19, 2025

Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms
08:28

Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms

Published on: March 3, 2023

1.1K
A Fully Automated and Highly Versatile System for Testing Multi-cognitive Functions and Recording Neuronal Activities in Rodents
09:13

A Fully Automated and Highly Versatile System for Testing Multi-cognitive Functions and Recording Neuronal Activities in Rodents

Published on: May 3, 2012

14.4K
Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning
11:20

Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning

Published on: June 2, 2014

12.0K

Area of Science:

  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Behavioral states, including arousal and movement, significantly influence neural activity in sensory regions.
  • These modulations can be conceptualized as long-range projections that regulate the mean and variance of baseline input currents.

Purpose of the Study:

  • To explore the computational advantages of baseline input modulations.
  • To investigate how these modulations impact neural network dynamics and function within a brain-inspired reservoir computing framework.

Main Methods:

  • Utilized a recurrent neural network with random couplings in a reservoir computing setup.
  • Systematically varied quenched baseline inputs to analyze their effect on network dynamics.
  • Investigated network phases, including bistable states and phenomena like noise-driven chaos and neural hysteresis.

Main Results:

  • Baseline modulations were found to control the dynamical phase of the reservoir network, revealing diverse network phases.
  • Identified bistable phases characterized by coexistence of fixed points and chaos, or varying degrees of chaos.
  • Observed phenomena such as noise-enhanced chaos, ergodicity breaking, and neural hysteresis.
  • Demonstrated that different bistable phases facilitate distinct binary decision-making tasks.
  • Showcased fast task switching controlled by adjusting baseline input mean and variance.
  • Determined optimal memory performance occurs at first-order phase boundaries.

Conclusions:

  • Baseline input control allows for multitasking capabilities in neural networks without altering network couplings.
  • Provides a framework for understanding behavioral modulations of cortical activity.
  • Suggests new directions for developing brain-inspired artificial intelligence systems.