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

Convolution: Math, Graphics, and Discrete Signals

Convolution: Math, Graphics, and Discrete Signals

In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...

Convolution Properties I

Convolution Properties I

Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:

Convolution Properties II

Convolution Properties II

The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...

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...

Neural Regulation

Neural Regulation

Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.

The Role of Ion Channels in Neuronal Computation

The Role of Ion Channels in Neuronal Computation

A postsynaptic neuron usually receives numerous impulses from several other presynaptic neurons. The axon hillock of the postsynaptic neuron integrates all these signals and determines the likelihood of firing an action potential.
Sometimes a single EPSP is strong enough to induce an action potential in the postsynaptic neuron. However, multiple presynaptic inputs must often create EPSPs around the same time for the postsynaptic neuron to be sufficiently depolarized to fire an action potential....

You might also read

Related Articles

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

Sort by

Same author

Obstruction of the Biliary Limb of the Roux Loop After Successful Kasai Portoenterostomy for Biliary Atresia: Report of Two Cases.

The American journal of case reports·2026

Same author

A Portal-Rex Shunt Using Patent Proximal Main Portal Vein as Venous Inflow and Internal Jugular Vein as Conduit.

Children (Basel, Switzerland)·2026

Same author

In memoriam: Sir Roy Calne (1930-2024) - A life in surgery and impact on paediatric liver transplantation in Singapore.

Singapore medical journal·2025

Same author

Systematic Review and Meta-Analysis of the Outcomes After Hepatic Artery Reconstruction in Pediatric Liver Transplantation Using a Microscope Versus Surgical Loupe.

Transplantation proceedings·2024

Same author

Pediatric trauma during the COVID-19 lockdown: caregiver abuse and self-harm in a vulnerable population.

Pediatric surgery international·2024

Same author

Simple biliary atresia score-a validated diagnostic aid for infantile cholestasis.

Pediatric surgery international·2024

Same journal

DARUMA: a gateway to fast and easy prediction of intrinsically disordered regions.

PeerJ. Computer science·2026

Same journal

Alzheimer's disease detection using a quantum deep neural network with Haralick feature extraction and simulated annealing optimization.

PeerJ. Computer science·2026

Same journal

Network anomaly detection using Deep Autoencoder and parallel Artificial Bee Colony algorithm-trained neural network.

PeerJ. Computer science·2026

Same journal

An anomaly detection model for multivariate time series with anomaly perception.

PeerJ. Computer science·2026

Same journal

Retraction: A wormhole attack detection method for tactical wireless sensor networks.

PeerJ. Computer science·2026

Same journal

Evaluation of mental disorder with prioritization of its type by utilizing the bipolar complex fuzzy decision-making approach based on Schweizer-Sklar prioritized aggregation operators.

PeerJ. Computer science·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Nov 10, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Stochastic computing in convolutional neural network implementation: a review.

Yang Yang Lee¹, Zaini Abdul Halim¹

¹School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal, Penang, Malaysia.

Peerj. Computer Science

|April 5, 2021

Summary

This summary is machine-generated.

Stochastic computing (SC) offers efficient hardware for deep learning, particularly convolutional neural networks (CNNs). This review explores SC

Keywords:

Convolutional Neural Network Deep learning FPGA IoT Stochastic computing

Related Experiment Videos

Last Updated: Nov 10, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Area of Science:

Computer Engineering
Artificial Intelligence
Digital Logic Design

Background:

Stochastic computing (SC) leverages probability for arithmetic operations, offering an alternative to traditional binary computing.
SC experienced a resurgence due to its hardware implementation advantages for deep learning algorithms like convolutional neural networks (CNNs).
Binarized neural networks, an evolution of CNNs, are gaining traction in edge computing for their efficiency and compactness.

Purpose of the Study:

To review various hardware implementation methodologies for stochastic computing in convolutional neural networks.
To compare the advantages and disadvantages of different SC methods for CNN applications.
To provide an overview of SC in CNNs and suggest pathways for broader implementation.

Main Methods:

Review of fundamental concepts and circuit structures in stochastic computing.
Comparative analysis of different SC methodologies applied to CNN hardware.
Evaluation of SC's suitability for CNN algorithms and their variants.

Main Results:

Identified practical SC function blocks applicable to CNN algorithms.
Highlighted the potential of SC for efficient hardware implementation of CNNs and binarized neural networks.
Detailed comparison of the pros and cons of various SC techniques in the context of CNNs.

Conclusions:

Stochastic computing presents a viable and efficient approach for implementing CNNs, especially in resource-constrained edge computing environments.
Further research and development are needed to overcome limitations and facilitate widespread adoption of SC for deep learning hardware.
The study provides a comprehensive overview to guide future SC-CNN hardware implementations.