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
Long-term Potentiation01:25

Long-term Potentiation

2.7K
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.
Hebbian LTP
LTP can occur when...
2.7K
Neuroplasticity01:01

Neuroplasticity

303
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.
303

You might also read

Related Articles

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

Sort by
Same author

The neural coding framework for learning generative models.

Nature communications·2022
See all related articles

Related Experiment Video

Updated: Jun 9, 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

Contrastive signal-dependent plasticity: Self-supervised learning in spiking neural circuits.

Alexander G Ororbia1

  • 1Department of Computer Science, Rochester Institute of Technology, 1 Lomb Memorial Dr, Rochester, NY 14623, USA.

Science Advances
|October 23, 2024
PubMed
Summary

Researchers developed a new method for training spiking neural networks, inspired by the brain. This contrastive signal-dependent plasticity approach improves adaptability and energy efficiency in artificial intelligence models.

More Related Videos

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
05:19

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

7.0K
Slice Patch Clamp Technique for Analyzing Learning-Induced Plasticity
11:56

Slice Patch Clamp Technique for Analyzing Learning-Induced Plasticity

Published on: November 11, 2017

15.3K

Related Experiment Videos

Last Updated: Jun 9, 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
Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
05:19

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

7.0K
Slice Patch Clamp Technique for Analyzing Learning-Induced Plasticity
11:56

Slice Patch Clamp Technique for Analyzing Learning-Induced Plasticity

Published on: November 11, 2017

15.3K

Area of Science:

  • Neuroscience and Artificial Intelligence
  • Computational Neuroscience
  • Machine Learning

Background:

  • Modern deep neural networks often lack biological plausibility and energy efficiency.
  • Spiking neural networks (SNNs) offer a promising alternative by mimicking biological neurons.
  • Developing effective learning rules for SNNs remains a significant challenge.

Purpose of the Study:

  • To propose a novel, neurobiologically motivated synaptic plasticity rule for SNNs.
  • To enhance the adaptability and learning capabilities of event-based neuronal architectures.
  • To improve the energy efficiency of artificial intelligence models.

Main Methods:

  • Introduced contrastive signal-dependent plasticity (CSDP), a self-supervised learning-inspired mechanism.
  • Applied CSDP to facilitate local adaptation in parallel, event-based neuronal layers.
  • Trained recurrent spiking networks using the proposed CSDP method.

Main Results:

  • CSDP demonstrated a consistent advantage over existing biologically plausible learning rules.
  • The method effectively trains recurrent spiking networks.
  • CSDP obviates the need for additional complex structures like feedback synapses.

Conclusions:

  • Contrastive signal-dependent plasticity is a viable and effective method for training SNNs.
  • This approach advances brain-inspired machine intelligence by improving adaptability and efficiency.
  • CSDP offers a simpler, yet powerful, alternative for developing advanced neural network architectures.