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

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...

Neural Circuits

Neural Circuits

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...

Propagation of Action Potentials

Propagation of Action Potentials

The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...

You might also read

Related Articles

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

Sort by

Same author

The Smart Inertial Device Data from Human Activities dataset.

Scientific data·2026

Same author

Author Correction: ON-OFF neuromorphic ISING machines using Fowler-Nordheim annealers.

Nature communications·2025

Same author

Heterogeneity and evolution of DNA mutation rates in microsatellite stable colorectal cancer.

Science translational medicine·2025

Same author

ON-OFF neuromorphic ISING machines using Fowler-Nordheim annealers.

Nature communications·2025

Same author

The neurobench framework for benchmarking neuromorphic computing algorithms and systems.

Nature communications·2025

Same author

Neuromorphic intermediate representation: A unified instruction set for interoperable brain-inspired computing.

Nature communications·2024

Search research articles

Related Experiment Video

Updated: Aug 14, 2025

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

Spike encoding techniques for IoT time-varying signals benchmarked on a neuromorphic classification task.

Evelina Forno¹, Vittorio Fra¹, Riccardo Pignari¹

¹Politecnico di Torino, Electronic Design Automation (EDA) Group, Turin, Italy.

Frontiers in Neuroscience

|January 9, 2023

Summary

This study compares spike encoding techniques for Spiking Neural Networks (SNNs) in edge AI. It provides a practical guide for developers to select optimal methods for diverse sensor data, enhancing neuromorphic computing applications.

Keywords:

IoT applications Spiking Neural Network benchmarking event based encoding neuromorphic computing neuromorphic encoding techniques spatio-temporal pattern recognition time-varing signals

More Related Videos

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

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

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

Published on: November 12, 2019

Related Experiment Videos

Last Updated: Aug 14, 2025

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

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

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

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

Published on: November 12, 2019

Area of Science:

Neuromorphic Engineering
Machine Learning
Signal Processing

Background:

Spiking Neural Networks (SNNs) offer low-energy advantages for edge AI.
Standard digital sensor data requires encoding into spike trains for SNN processing.
Efficient encoding is crucial for deploying SNNs in resource-constrained embedded systems.

Purpose of the Study:

To benchmark and compare various spike encoding techniques for time-varying signals.
To evaluate these techniques on audio (FSD) and human activity (WISDM) datasets.
To provide a practical tool for selecting appropriate encoding methods for neuromorphic applications.

Main Methods:

Developed a comprehensive benchmarking pipeline for spike encoding techniques.
Utilized a Spiking Convolutional Neural Network (sCNN) for time-dependent signal classification.
Incorporated signal preprocessing (cochlear-inspired filters), sonogram feature extraction, and transfer learning from ANNs.
Applied model compression schemes for resource optimization.

Main Results:

Detailed performance comparison of different spike encoding methods across two distinct datasets.
Demonstrated the effectiveness of the proposed pipeline in evaluating encoding techniques.
Identified optimal encoding strategies based on data type and processing requirements.

Conclusions:

The study offers a valuable resource for developers integrating SNNs into embedded systems.
The findings facilitate the selection of suitable spike encoding techniques for diverse IoT and industrial applications.
This work expands the practical applicability of neuromorphic computing in edge environments.