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

1.1K
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...
1.1K
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
Encoding01:19

Encoding

156
Information enters the brain through encoding, which is the input of information into the memory system. Once sensory information is received from the environment, the brain labels or codes it. The information is then organized with similar information and connected to existing concepts. Encoding occurs through automatic processing and effortful processing.
Automatic processing involves the encoding of details like time, space, frequency, and the meaning of words, usually done without conscious...
156
Neuronal Communication01:28

Neuronal Communication

828
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...
828
Neurons as Communicators of the Brain01:22

Neurons as Communicators of the Brain

1.2K
Neurons, the fundamental units of the brain and nervous system, function as the primary transmitters of information throughout the body. Their ability to communicate through electrical and chemical signals is vital for every bodily function, from regulating the heartbeat to processing complex thoughts. Each neuron has three main components: the cell body (soma), dendrites, and an axon, each specialized to facilitate swift and efficient neural communication.
Cell Body
The cell body, also known...
1.2K

You might also read

Related Articles

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

Sort by
Same author

Sources of imprecision in integrated value comparisons.

Cognition·2026
Same author

Endogenous precision of the number sense.

eLife·2026
Same author

Specialized structure of neural population codes in parietal cortex outputs.

Nature neuroscience·2025
Same author

Random compressed coding with neurons.

Cell reports·2025
Same author

Efficient coding in biophysically realistic excitatory-inhibitory spiking networks.

eLife·2025
Same author

Efficient numerosity estimation under limited time.

PLoS computational biology·2025
Same journal

Detection, communication, and individual identification with deep audio embeddings: A case study with North Atlantic right whales.

PLoS computational biology·2026
Same journal

Exploring the structural lexicon of the Proteome via Metric Geometry.

PLoS computational biology·2026
Same journal

Linking retinal sampling in neural encoding models to temporal profiles of visual processing in humans.

PLoS computational biology·2026
Same journal

CAdir: Joint clustering of cells and genes for single-cell transcriptomics with visualization-driven cluster quality assessment.

PLoS computational biology·2026
Same journal

Systematic design of auxotrophic strains and media conditions to probe metabolic functions in E. coli.

PLoS computational biology·2026
Same journal

Neuronal excitability and parameter variability in the Hodgkin-Huxley model.

PLoS computational biology·2026
See all related articles

Related Experiment Video

Updated: Jun 21, 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

Jointly efficient encoding and decoding in neural populations.

Simone Blanco Malerba1,2, Aurora Micheli1, Michael Woodford3

  • 1Laboratoire de Physique de l'Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université de Paris, Paris, France.

Plos Computational Biology
|July 10, 2024
PubMed
Summary
This summary is machine-generated.

Neural systems jointly optimize encoding and decoding for accurate sensory representation. This new framework, inspired by variational autoencoders, generalizes efficient coding and learns from data, improving generalization.

More Related Videos

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.0K
Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

43.3K

Related Experiment Videos

Last Updated: Jun 21, 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
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.0K
Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

43.3K

Area of Science:

  • Computational Neuroscience
  • Information Theory
  • Machine Learning

Background:

  • Existing theories propose neural systems optimize either sensory information encoding or decoding.
  • Efficient coding formalizes encoding as a constrained optimal process.
  • Generative models formalize decoding by assuming neural systems instantiate a model of the sensory world.

Purpose of the Study:

  • To propose a normative framework that unifies and generalizes existing approaches by jointly optimizing neural encoding and decoding.
  • To characterize the resulting family of models and their relationship to neural population properties and sensory statistics.
  • To explore the implications for understanding neural representations and their biological constraints.

Main Methods:

  • Developed a normative framework based on variational autoencoders, optimizing both encoding and decoding simultaneously.
  • Analyzed the family of resulting encoding-decoding models, indexed by neural activity deviation from marginal distributions.
  • Investigated the relationship between neural population properties (tuning curves) and sensory statistics.
  • Evaluated model performance using stimulus reconstruction error and generative model accuracy.

Main Results:

  • The framework yields a family of encoding-decoding models with equally accurate generative models.
  • Each model predicts specific relationships between neural properties and sensory world statistics.
  • Solutions are learned from data samples, with constraints acting as regularizers for generalization.
  • Broad tuning curves, observed experimentally, yield low reconstruction error and robust generative models.

Conclusions:

  • The proposed framework offers a unified view of neural representation, integrating efficient coding and generative modeling principles.
  • It provides a principled way to link neural population structure to sensory statistics and function.
  • The findings suggest that neural systems may jointly optimize encoding and decoding for robust and generalizable sensory processing.