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

Propagation of Action Potentials01:23

Propagation of Action Potentials

5.9K
The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
5.9K
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
Spinal Cord: Information Processing01:10

Spinal Cord: Information Processing

1.3K
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...
1.3K
Neuroplasticity01:01

Neuroplasticity

377
Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.
377
The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

3.2K
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....
3.2K
Parallel Processing01:20

Parallel Processing

159
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...
159

You might also read

Related Articles

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

Sort by
Same author

Implicit hierarchical temporal-spatial residual model for long-term video prediction.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Competitive thiolation kinetics of antimony, arsenic, and tungsten controlling antimony speciation in sulfidic hot springs.

Environmental pollution (Barking, Essex : 1987)·2025
Same author

Effective treatment of thioantimonates-bearing waters by nanocrystalline iowaite, an iron-based layered double hydroxide.

Environmental pollution (Barking, Essex : 1987)·2025
Same author

Advancements and prospects of landsenses ecology research based on bibliometric analysis.

Heliyon·2024
Same author

Accurate and Efficient Event-Based Semantic Segmentation Using Adaptive Spiking Encoder-Decoder Network.

IEEE transactions on neural networks and learning systems·2024
Same author

Learning with sparse reward in a gap junction network inspired by the insect mushroom body.

PLoS computational biology·2024

Related Experiment Video

Updated: Jul 13, 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

10.3K

Predictive Coding as a Neuromorphic Alternative to Backpropagation: A Critical Evaluation.

Umais Zahid1, Qinghai Guo2, Zafeirios Fountas3

  • 1Huawei Technologies R&D, London N19 3HT, U.K. umais.zahid@huawei.com.

Neural Computation
|October 16, 2023
PubMed
Summary
This summary is machine-generated.

Predictive coding (PC) may not replace backpropagation in deep learning as initially hoped. Current PC variants have computational complexity bounds lower-bounded by backpropagation, limiting their use in neuromorphic systems.

More Related Videos

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

11.5K
Studying the Coding Profiles of Somatic Stimulation on Cardiac-locked Neuronal Responses in the Rat Spinal Dorsal Horn
07:13

Studying the Coding Profiles of Somatic Stimulation on Cardiac-locked Neuronal Responses in the Rat Spinal Dorsal Horn

Published on: May 23, 2025

117

Related Experiment Videos

Last Updated: Jul 13, 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

10.3K
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

11.5K
Studying the Coding Profiles of Somatic Stimulation on Cardiac-locked Neuronal Responses in the Rat Spinal Dorsal Horn
07:13

Studying the Coding Profiles of Somatic Stimulation on Cardiac-locked Neuronal Responses in the Rat Spinal Dorsal Horn

Published on: May 23, 2025

117

Area of Science:

  • Computational Neuroscience
  • Deep Learning
  • Machine Learning Algorithms

Background:

  • Backpropagation is the standard algorithm for credit assignment in deep learning.
  • Predictive coding (PC) variants have emerged as potential alternatives due to similar parameter updates.
  • PC's neurobiological plausibility and potential for neuromorphic systems have been highlighted.

Purpose of the Study:

  • To investigate the claims that predictive coding (PC) can serve as a viable alternative to backpropagation.
  • To analyze the computational complexity and neurobiological plausibility of contemporary PC variants.
  • To clarify the relationship between PC and backpropagation within the context of deep learning.

Main Methods:

  • Analysis of time complexity bounds for various contemporary predictive coding (PC) variants.
  • Comparison of these bounds against the complexity of backpropagation.
  • Examination of PC variants' properties concerning neurobiological plausibility and variational Bayes interpretations.

Main Results:

  • Time complexity bounds for PC variants were found to be lower-bounded by backpropagation.
  • Key properties of PC variants were identified with implications for their neurobiological interpretations.
  • The direct replacement of backpropagation by current PC forms appears more limited than previously suggested.

Conclusions:

  • Current predictive coding (PC) variants exhibit computational complexities that do not surpass backpropagation.
  • These findings temper expectations for PC as a direct, advantageous replacement for backpropagation in deep learning and neuromorphic applications.
  • Further research is needed to fully understand the potential and limitations of PC in artificial intelligence.