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

Parallel Processing01:20

Parallel Processing

809
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...
809
The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

4.0K
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....
4.0K
Neuronal Communication01:28

Neuronal Communication

3.8K
Neurons, the fundamental units of the brain and nervous system, communicate through complex electrochemical signals that underpin all cognitive and bodily functions. This communication is primarily facilitated by a process involving the generation and propagation of an action potential along the axon of the neuron. When the internal electrical charge of a neuron surpasses a certain threshold, an action potential is triggered. This rapid change in voltage travels swiftly along the axon to the...
3.8K
Neural Circuits01:25

Neural Circuits

3.0K
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...
3.0K
Spinal Cord: Information Processing01:10

Spinal Cord: Information Processing

3.9K
The spinal cord is an integral hub for motor and sensory information that enables the brain to communicate with the peripheral nervous system (PNS). This communication consists of relaying sensory data and transmission of motor commands.
Sensory Information Processing
Sensory information processing begins at the sensory receptors located in the skin and other tissues, which detect somatic sensory stimuli such as touch, temperature, or pain. These receptors function as catalysts, initiating...
3.9K
Concepts and Prototypes01:24

Concepts and Prototypes

589
The human nervous system handles vast amounts of information by translating sensory stimuli into neural impulses, which the brain processes, creating thoughts expressed through language or stored as memories. The brain also synthesizes information from emotions and memories, which significantly influence thoughts and behaviors. This intricate process creates a comprehensive mental picture.
The brain organizes this information using concepts, which are mental categories grouping linguistic data,...
589

You might also read

Related Articles

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

Sort by
Same author

Anxiety sensitivity and intolerance of uncertainty track distinct neurobehavioral dimensions of avoidance in anxiety-related disorders.

Molecular psychiatry·2026
Same author

Motion-corrected eye tracking improves gaze accuracy during visual fMRI experiments.

Nature communications·2025
Same author

Processing of natural scenes in the human pulvinar.

Nature communications·2025
Same author

Mnemonic maps of visual space in human prefrontal cortex.

bioRxiv : the preprint server for biology·2025
Same author

Single pulse electrical stimulation in white matter modulates iEEG visual responses in human early visual cortex.

bioRxiv : the preprint server for biology·2025
Same author

Motion-corrected eye tracking (MoCET) improves gaze accuracy during visual fMRI experiments.

bioRxiv : the preprint server for biology·2025
Same journal

Lifespan Trajectories of the Brain's Functional Complexity Characterized by Multiscale Sample Entropy.

NeuroImage·2026
Same journal

Pleasant fragrance modulates dyadic social sharing of positive emotion: Sharer-centered socioemotional enhancement effect and its neural couplings.

NeuroImage·2026
Same journal

Altered Functional Hierarchical and Sequential Organization in Individuals with Schizophrenia during Auditory Processing.

NeuroImage·2026
Same journal

Mechanical Deformation Explains Distinct Neuroimaging Patterns and Etiologies in Brain Trauma.

NeuroImage·2026
Same journal

Ventral striatum temporal interference brain stimulation enhances the reward-positivity event-related potential and reduces anxiety.

NeuroImage·2026
Same journal

NeuroHarm‑Kit: An Open‑Source Toolbox for Benchmarking Deep‑Learning Harmonization of Multi‑Site T1‑Weighted MRI.

NeuroImage·2026
See all related articles

Related Experiment Video

Updated: Feb 25, 2026

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
10:50

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

2.2K

Principles for models of neural information processing.

Kendrick N Kay1

  • 1Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Twin Cities, Minneapolis, MN, USA.

Neuroimage
|August 10, 2017
PubMed
Summary
This summary is machine-generated.

This perspective discusses formal models for understanding brain function in cognitive neuroscience. While deep neural networks show promise, further research is needed to clarify their explanatory power and mechanisms.

More Related Videos

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

10.9K
A Fully Automated Rodent Conditioning Protocol for Sensorimotor Integration and Cognitive Control Experiments
09:43

A Fully Automated Rodent Conditioning Protocol for Sensorimotor Integration and Cognitive Control Experiments

Published on: April 15, 2014

11.1K

Related Experiment Videos

Last Updated: Feb 25, 2026

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
10:50

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

2.2K
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

10.9K
A Fully Automated Rodent Conditioning Protocol for Sensorimotor Integration and Cognitive Control Experiments
09:43

A Fully Automated Rodent Conditioning Protocol for Sensorimotor Integration and Cognitive Control Experiments

Published on: April 15, 2014

11.1K

Area of Science:

  • Cognitive Neuroscience
  • Computational Neuroscience
  • Neuroscience

Background:

  • Understanding how the brain performs mental operations is a central goal of cognitive neuroscience.
  • The brain's complexity necessitates formal models for studying neural information processing.
  • Distinguishing between functional and mechanistic models is crucial for scientific progress.

Purpose of the Study:

  • To provide a perspective on formal models of neural information processing in cognitive neuroscience.
  • To define, explain the utility of, and establish criteria for evaluating these models.
  • To assess the merit of deep neural network models within this framework.

Main Methods:

  • Review and conceptual analysis of formal models in cognitive neuroscience.
  • Definition and differentiation of functional versus mechanistic models.
  • Application of proposed evaluation criteria to deep neural network models.

Main Results:

  • Formal models are essential tools for cognitive neuroscience research.
  • Neuroanatomy offers valuable insights into brain function.
  • Deep neural networks are promising but require further investigation regarding their explanatory scope and underlying mechanisms.

Conclusions:

  • Formal models, including deep neural networks, are vital for advancing cognitive neuroscience.
  • Clear criteria are needed to evaluate the explanatory power and mechanistic understanding provided by these models.
  • Continued research is essential to refine our understanding of deep neural networks in the context of brain function.