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

Long-term Potentiation01:35

Long-term Potentiation

57.1K
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.
57.1K

You might also read

Related Articles

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

Sort by
Same author

Adaptive behavior with stable synapses.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

Towards biologically plausible model-based reinforcement learning in recurrent spiking networks by dreaming new experiences.

Scientific reports·2024
Same author

A modular and adaptable analysis pipeline to compare slow cerebral rhythms across heterogeneous datasets.

Cell reports methods·2024
Same author

Beyond spiking networks: The computational advantages of dendritic amplification and input segregation.

Proceedings of the National Academy of Sciences of the United States of America·2023
Same author

Simulations approaching data: cortical slow waves in inferred models of the whole hemisphere of mouse.

Communications biology·2023
Same author

Error-based or target-based? A unified framework for learning in recurrent spiking networks.

PLoS computational biology·2022

Related Experiment Video

Updated: Nov 17, 2025

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

10.1K

Target spike patterns enable efficient and biologically plausible learning for complex temporal tasks.

Paolo Muratore1, Cristiano Capone2, Pier Stanislao Paolucci2

  • 1SISSA-International School for Advanced Studies, Trieste, Italy.

Plos One
|February 16, 2021
PubMed
Summary

Researchers developed a novel biologically inspired learning rule for recurrent spiking neural networks (RSNNs) that rapidly and precisely solves complex temporal tasks by mimicking target spike patterns, outperforming existing methods.

More Related Videos

Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning
11:20

Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning

Published on: June 2, 2014

12.2K
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

7.3K

Related Experiment Videos

Last Updated: Nov 17, 2025

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

10.1K
Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning
11:20

Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning

Published on: June 2, 2014

12.2K
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

7.3K

Area of Science:

  • Computational Neuroscience
  • Artificial Intelligence
  • Machine Learning

Background:

  • Recurrent spiking neural networks (RSNNs) offer energy-efficient computation and rapid learning from few examples, mimicking biological brain efficiency.
  • Developing biologically plausible learning rules for RSNNs that can solve complex temporal tasks remains a significant challenge.
  • Existing error-based learning rules require extensive training to minimize errors, unlike more direct biological learning mechanisms.

Purpose of the Study:

  • To derive a biologically plausible, synapse-local learning rule for RSNNs based on likelihood maximization.
  • To introduce a novel target-based learning scheme for RSNNs that rapidly mimics desired spatio-temporal spike patterns.
  • To enhance understanding of brain computation and improve the efficiency of artificial intelligence through bio-inspired learning.

Main Methods:

  • Derived a synapse-local learning rule from the principle of maximizing the likelihood of task completion.
  • Implemented a target-based learning scheme where external signals define desired spike patterns.
  • Proposed and compared spike-dependent and voltage-dependent versions of the learning rule, utilizing gradient ascent approximations.

Main Results:

  • The novel target-based learning scheme significantly accelerates and enhances the precision of RSNN training.
  • The voltage-dependent learning rule demonstrated superior learning speed and robustness to noise compared to spike-dependent versions.
  • The model successfully learned multidimensional trajectories and solved the temporal XOR benchmark, outperforming state-of-the-art algorithms.

Conclusions:

  • The derived likelihood-maximization-based learning rule offers a powerful and biologically plausible approach for training RSNNs.
  • Target-based learning facilitates rapid and precise acquisition of complex temporal tasks by directly guiding network activity.
  • The voltage-dependent rule shows promise for efficient and robust AI, with potential for experimental validation in biological systems.