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

Orthogonal Trajectories

Orthogonal Trajectories

Orthogonal trajectories describe the geometric relationship between two families of curves that intersect each other at right angles. One illustrative case involves a family of parabolas that open sideways along the x-axis. These curves share a common shape but differ by a scaling parameter, resulting in a set of curves that all pass through the origin and widen at different rates.Determining Orthogonal TrajectoriesTo identify the orthogonal trajectories for these parabolas, the first step...

Classification of Signals

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...

Sequence Networks of Rotating Machines

Sequence Networks of Rotating Machines

A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...

Classification of Systems-I

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:

Oscillations about an Equilibrium Position

Oscillations about an Equilibrium Position

Stability is an important concept in oscillation. If an equilibrium point is stable, a slight disturbance of an object that is initially at the stable equilibrium point will cause the object to oscillate around that point. For an unstable equilibrium point, if the object is disturbed slightly, it will not return to the equilibrium point. There are three conditions for equilibrium points—stable, unstable, and half-stable. A half-stable equilibrium point is also unstable, but is named so because...

Classification of Systems-II

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,

You might also read

Related Articles

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

Sort by

Same author

A method to analyze low signal-to-noise ratio functional magnetic resonance imaging data.

Journal of integrative neuroscience·2015

Same author

Image and emotion: from outcomes to brain behavior.

HERD·2012

Same author

Dynamic multi-channel TMS with reconfigurable coil.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2012

Same author

A computational model for generation of the P300 evoked potential component.

Journal of integrative neuroscience·2012

Same author

Sensory gating-out and gating-in in normal and schizophrenic participants.

Clinical EEG and neuroscience·2012

Same author

Impact of visual art on patient behavior in the emergency department waiting room.

The Journal of emergency medicine·2012

Same journal

Harmonic memory in phasor neural networks.

Biological cybernetics·2026

Same journal

Correction: Decreased spinal inhibition leads to undiversified locomotor patterns.

Biological cybernetics·2026

Same journal

Foundational issues of network models in biology.

Biological cybernetics·2026

Same journal

Dynamical mechanisms for coordinating long-term working memory based on the precision of spike-timing in cortical neurons.

Biological cybernetics·2026

Same journal

Distinct dopaminergic spike-timing-dependent plasticity rules are suited to different functional roles.

Biological cybernetics·2026

Same journal

Fluctuation-response relations for a two-stage population of spiking neurons stimulated by common noise.

Biological cybernetics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 30, 2026

Photodiode-Based Optical Imaging for Recording Network Dynamics with Single-Neuron Resolution in Non-Transgenic Invertebrates

Photodiode-Based Optical Imaging for Recording Network Dynamics with Single-Neuron Resolution in Non-Transgenic Invertebrates

Published on: July 9, 2020

Oscillatory neural network for pattern recognition: trajectory based classification and supervised learning.

Vonda H Miller¹, Ben H Jansen

¹The Boeing Company, 13100 Space Center Blvd, MC 2-10, Houston, TX, 77059, USA. vonda.h.miller@boeing.com

Biological Cybernetics

|September 23, 2008

Summary

This summary is machine-generated.

This study introduces a novel trajectory-based distance metric for category discrimination in oscillatory neural networks. Supervised learning significantly improved pattern recognition in spatiotemporal data.

Related Experiment Videos

Last Updated: Jun 30, 2026

Photodiode-Based Optical Imaging for Recording Network Dynamics with Single-Neuron Resolution in Non-Transgenic Invertebrates

Photodiode-Based Optical Imaging for Recording Network Dynamics with Single-Neuron Resolution in Non-Transgenic Invertebrates

Published on: July 9, 2020

Area of Science:

Computational Neuroscience
Machine Learning
Pattern Recognition

Background:

Human brain excels at generalization and invariant feature extraction for category matching, surpassing current computer algorithms.
Spatiotemporal activation patterns in the brain are hypothesized as the mechanism for encoding and matching stimuli to categories.

Purpose of the Study:

To develop and evaluate a trajectory-based distance metric for category discrimination within an oscillatory neural network model.
To demonstrate the efficacy of supervised learning, utilizing gradient descent, for improving classification accuracy.

Main Methods:

Utilized an oscillatory neural network model for pattern recognition tasks.
Employed a differentiable trajectory-based distance metric for classification.
Implemented a supervised learning algorithm based on gradient descent for training.

Main Results:

Successfully classified spatiotemporal frequency transitions based on predefined categories.
Demonstrated significant improvement in classification accuracy after supervised training.
Validated the utility of the spatiotemporal representation and distance metric for simple pattern recognition.

Conclusions:

The proposed spatiotemporal representation and trajectory-based distance metric are effective for simple pattern recognition tasks.
Supervised learning enhances the classification performance of the oscillatory neural network model.
Further research may explore more complex pattern recognition challenges with this approach.