Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Fixed Action Patterns

Fixed Action Patterns

A fixed action pattern (FAP) is a specific, hard-wired sequence of behaviors that occurs in response to an external stimulus, called a sign stimulus. The behavior is “fixed” because it is essentially unchangeable—proceeding similarly across individuals of a species every time it occurs.

You might also read

Related Articles

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

Sort by

Same author

Towards a better understanding of real-life hearing impairments via electrophysiology.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same author

Temporal recurrence as a general mechanism to explain neural responses in the auditory system.

Communications biology·2025

Same author

A general model unifying the adaptive, transient and sustained properties of ON and OFF auditory neural responses.

PLoS computational biology·2024

Same author

SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence.

Science advances·2023

Same author

Brain-guided manifold transferring to improve the performance of spiking neural networks in image classification.

Journal of computational neuroscience·2023

Same author

Optical flow estimation from event-based cameras and spiking neural networks.

Frontiers in neuroscience·2023

Same journal

A Model-Free Reinforcement Learning Implementation of Decision Making Under Uncertainty by Sequential Sampling.

Neural computation·2026

Same journal

DROP: Distributional and Regular Optimism and Pessimism for Reinforcement Learning.

Neural computation·2026

Same journal

Hierarchical Active Inference Using Successor Representations.

Neural computation·2026

Same journal

W-Kernel and Its Principal Space for Frequentist Evaluation of Bayesian Estimators.

Neural computation·2026

Same journal

A Hidden Markov Model-Inspired Sequence Classification Method for Hyperdimensional Computing.

Neural computation·2026

Same journal

Sparse Graphical Modeling for Electrophysiological Phase-Based Connectivity Using Circular Statistics.

Neural computation·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 20, 2026

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

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

Published on: March 25, 2014

Competitive STDP-based spike pattern learning.

Timothée Masquelier¹, Rudy Guyonneau, Simon J Thorpe

¹Centre de Recherche Cerveau et Cognition, Université Toulouse 3, Centre National de la Recherche Scientifique, Faculté de Médecine de Rangueil, Toulouse 31062, France. timothee.masquelier@alum.mit.edu

Neural Computation

|September 1, 2009

Summary

This summary is machine-generated.

This study shows how multiple neurons with spike-timing-dependent plasticity (STDP) can self-organize to detect and learn various spike patterns. This competitive network enables efficient distributed coding and temporal coding in neural systems.

More Related Videos

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Related Experiment Videos

Last Updated: Jun 20, 2026

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

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

Published on: March 25, 2014

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Area of Science:

Computational Neuroscience
Neural Networks
Machine Learning

Background:

Single neurons with spike-timing-dependent plasticity (STDP) can learn arbitrary spatiotemporal spike patterns.
Previous work demonstrated robust detection of a single pattern by a single STDP neuron.

Purpose of the Study:

To extend the single-pattern detection model to a multi-pattern scenario with competing STDP neurons.
To investigate how neural populations self-organize to encode multiple repeating patterns.
To explore the emergence of distributed coding and temporal coding mechanisms.

Main Methods:

Simulations of multiple STDP neurons receiving common input spike trains.
Implementation of a one-winner-take-all mechanism with lateral inhibition between neurons.
Analysis of neural population activity and coding strategies.

Main Results:

Neurons self-organized to specialize in detecting different repeating patterns.
A distributed coding scheme emerged, where patterns are represented across multiple neurons.
The competitive network prevented neurons from learning identical patterns, promoting specialization.

Conclusions:

STDP-based neural networks can effectively learn and represent multiple complex spike patterns.
Competitive dynamics and lateral inhibition facilitate the emergence of distributed and temporal coding.
This model provides insights into how biological neural systems encode and decode information using spike timing.