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

2.4K
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...
2.4K
Observational Learning01:12

Observational Learning

722
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
722
Neural Regulation01:37

Neural Regulation

42.9K
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.
42.9K
Associative Learning01:27

Associative Learning

1.0K
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
1.0K
Nonconscious Mimicry01:13

Nonconscious Mimicry

5.0K
Nonconscious mimicry occurs when individuals alter their mannerisms to match the behaviors and expressions of those nearby, without intention.
5.0K
Implicit Memories01:24

Implicit Memories

342
Implicit memories, also known as non-declarative memories, are long-term memories that function outside of conscious awareness. These memories influence behavior and skills without explicit knowledge. This type of memory is evident in tasks like playing tennis, snowboarding, and texting. Implicit memory has three subsystems: procedural memory, conditioning, and priming. This type of memory is essential in various activities, from everyday tasks to specialized skills.
One key aspect of implicit...
342

You might also read

Related Articles

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

Sort by
Same author

Learning continuous chaotic attractors with a reservoir computer.

Chaos (Woodbury, N.Y.)·2022
Same author

Supervised chaotic source separation by a tank of water.

Chaos (Woodbury, N.Y.)·2020
Same author

Attractor reconstruction by machine learning.

Chaos (Woodbury, N.Y.)·2018
Same author

Using machine learning to replicate chaotic attractors and calculate Lyapunov exponents from data.

Chaos (Woodbury, N.Y.)·2018
Same author

Reservoir observers: Model-free inference of unmeasured variables in chaotic systems.

Chaos (Woodbury, N.Y.)·2017
Same journal

Exploring mechanisms for reversal of flow in tunicate hearts.

Chaos (Woodbury, N.Y.)·2026
Same journal

State estimation in spatiotemporal chaos via low-rank StatFEM.

Chaos (Woodbury, N.Y.)·2026
Same journal

Universal response functions in driven dissipative tunneling dynamics.

Chaos (Woodbury, N.Y.)·2026
Same journal

A network-based approach to characterize the dynamics of the coupling field of thermoacoustic oscillators in annular geometry.

Chaos (Woodbury, N.Y.)·2026
Same journal

Data-driven soliton manifold approximations for dark and bright waves: Some prototypical 1D case examples.

Chaos (Woodbury, N.Y.)·2026
Same journal

Gap junction architecture and synchronization clusters in the thalamic reticular nuclei.

Chaos (Woodbury, N.Y.)·2026
See all related articles

Related Experiment Video

Updated: Dec 16, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.7K

Invertible generalized synchronization: A putative mechanism for implicit learning in neural systems.

Zhixin Lu1, Danielle S Bassett1

  • 1Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA.

Chaos (Woodbury, N.Y.)
|July 3, 2020
PubMed
Summary
This summary is machine-generated.

Biological neural networks learn by imitating dynamical systems through invertible generalized synchronization (IGS). This framework explains cognitive functions like learning multiple dynamics and filling in missing information.

More Related Videos

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study
04:44

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Published on: July 21, 2021

4.7K
Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
08:08

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Published on: June 24, 2015

11.9K

Related Experiment Videos

Last Updated: Dec 16, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.7K
Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study
04:44

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Published on: July 21, 2021

4.7K
Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
08:08

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Published on: June 24, 2015

11.9K

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Biological and artificial neural systems are dynamical systems capable of learning to imitate unknown systems.
  • Reservoir computing networks (RCNs) learn complex dynamics from data via invertible generalized synchronization (IGS).
  • The role of IGS in biological learning remains an open question.

Purpose of the Study:

  • To propose a biologically feasible learning framework for neural systems based on IGS.
  • To investigate if IGS underlies learning in biological neural networks.
  • To explore emergent cognitive functions within this framework.

Main Methods:

  • Developed a general, biologically plausible learning framework utilizing IGS.
  • Constructed distinct neural network models as instantiations of the framework.
  • Employed a biologically feasible adaptation rule modulating synaptic strength.

Main Results:

  • Neural network models consistently learned to imitate dynamical processes.
  • Observed spontaneous emergence of four phenomena: learning multiple dynamics, dynamic system switching, filling-in missing variables, and deciphering superimposed inputs.
  • Provided theoretical explanations for these emergent phenomena.

Conclusions:

  • The proposed IGS-based framework supports the imitation of complex dynamics by neural systems.
  • Findings suggest IGS is a plausible mechanism for how biological neural networks learn environmental dynamics.
  • The framework offers insights into the neural basis of cognitive functions.