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

Neuroplasticity01:01

Neuroplasticity

289
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.
289
Neural Circuits01:25

Neural Circuits

1.0K
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...
1.0K
Long-term Potentiation01:35

Long-term Potentiation

54.8K
Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre- and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
54.8K
Postsynaptic Potential (PSP)01:32

Postsynaptic Potential (PSP)

2.4K
Postsynaptic potential (PSP) refers to a change in the electrical potential of a neuron when neurotransmitters released by presynaptic neurons bind to postsynaptic receptors. This potential can either be excitatory, leading to depolarization and ultimately action potential generation, or inhibitory, leading to hyperpolarization and suppression of the postsynaptic neuron.
There are two types of receptors: ionotropic and metabotropic.
The ionotropic receptor is the membrane protein that has an...
2.4K
Integration of Synaptic Events01:28

Integration of Synaptic Events

1.4K
Synaptic integration mainly includes the summation of graded potentials. Graded potentials, regardless of their type, cause subtle alterations in membrane voltage, resulting in either depolarization or hyperpolarization. These incremental changes, when combined or summed, can propel the neuron toward its threshold. Consider, for example, a membrane experiencing a +15 mV shift, causing it to depolarize from -70 mV to -55 mV. In this scenario, graded potentials govern the membrane's ability...
1.4K
Excitatory and Inhibitory Effects of Neurotransmitters01:29

Excitatory and Inhibitory Effects of Neurotransmitters

9.8K
When an action potential reaches the presynaptic axon terminal, it releases neurotransmitters from the neuron into the synaptic cleft at a chemical synapse. The released neurotransmitter can be excitatory or inhibitory. The critical criteria commonly used to determine whether a molecule is a neurotransmitter at a chemical synapse are the molecule's presence in the presynaptic neuron. Second, its release is in response to strong presynaptic depolarization. And lastly, the presence of...
9.8K

You might also read

Related Articles

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

Sort by
Same author

Building the connectome of a small brain with a simple stochastic developmental generative model.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Decoding sexual dimorphism of the sex-shared nervous system at single-neuron resolution.

bioRxiv : the preprint server for biology·2025
Same author

Decoding sexual dimorphism of the sex-shared nervous system at single-neuron resolution.

Science advances·2025
Same author

Top-down modulation of the retinal code via histaminergic neurons of the hypothalamus.

Science advances·2024
Same author

Learning probabilistic neural representations with randomly connected circuits.

Proceedings of the National Academy of Sciences of the United States of America·2020
Same author

Social interactions drive efficient foraging and income equality in groups of fish.

eLife·2020

Related Experiment Video

Updated: Jun 5, 2025

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

11.4K

Homeostatic synaptic normalization optimizes learning in network models of neural population codes.

Jonathan Mayzel1, Elad Schneidman1

  • 1Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel.

Elife
|December 16, 2024
PubMed
Summary
This summary is machine-generated.

New Reshaped Random Projection (RP) models offer a biologically plausible and efficient way to understand neural population activity. These models optimize synaptic connections for improved accuracy and homeostasis in neural circuits.

Keywords:
efficient codinghomeostatic synaptic plasticitynetwork modelsneurosciencepopulation codingrhesus macaquesparse codingspiking models

More Related Videos

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays
10:45

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays

Published on: May 29, 2017

9.8K
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

10.2K

Related Experiment Videos

Last Updated: Jun 5, 2025

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

11.4K
Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays
10:45

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays

Published on: May 29, 2017

9.8K
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

10.2K

Area of Science:

  • Computational Neuroscience
  • Neural Coding
  • Machine Learning

Background:

  • Accurate statistical models are crucial for understanding neural population activity.
  • Random Projection (RP) models offer accuracy, efficiency, and scalability.
  • RP models can be implemented as biologically plausible shallow neural networks.

Purpose of the Study:

  • To introduce a novel class of RP models learned by optimizing sparse projections.
  • To evaluate the performance of these 'Reshaped RP' models compared to standard RP models.
  • To investigate the role of biological features and synaptic normalization in model optimization.

Main Methods:

  • Developed Reshaped RP models by optimizing sparse projections, mimicking synaptic connection changes.
  • Incorporated biological features and synaptic normalization into the learning process.
  • Compared Reshaped RP models against standard RP and fully connected neural networks using data from monkey cortical neurons.

Main Results:

  • Reshaped RP models demonstrated superior accuracy and efficiency compared to standard RP models.
  • Models incorporating biological features and synaptic normalization showed enhanced efficiency.
  • The developed models exhibited homeostasis in firing rates and synaptic weights.
  • Sparse homeostatic reshaped RP models outperformed fully connected neural network models.

Conclusions:

  • Reshaped RP models provide a scalable, efficient, and highly accurate approach to population coding.
  • Biological features, particularly synaptic normalization, optimize network performance and efficiency.
  • Synaptic normalization plays a dual role in maintaining neural homeostasis and enhancing information encoding.