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

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...

You might also read

Related Articles

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

Sort by

Same author

Technology Roadmap of Bioinspired Computing Hardware.

ACS nano·2026

Same author

Fabrication-friendly all-optical plasmonically-enhanced integrated phase-change photonic memory device.

Optics express·2025

Same author

An integrated photonics engine for unsupervised correlation detection.

Science advances·2022

Same author

Improving HfO<sub>2</sub>-Based Resistive Switching Devices by Inserting a TaO<sub></sub> Thin Film via Engineered In Situ Oxidation.

ACS applied materials & interfaces·2022

Same author

Editorial: Emerging Technologies and Systems for Biologically Plausible Implementations of Neural Functions.

Frontiers in neuroscience·2022

Same author

Standards for the Characterization of Endurance in Resistive Switching Devices.

ACS nano·2021

Search research articles

Related Experiment Video

Updated: Jul 5, 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

SHIP: a computational framework for simulating and validating novel technologies in hardware spiking neural networks.

Emanuele Gemo¹, Sabina Spiga¹, Stefano Brivio¹

¹CNR-IMM, Unit of Agrate Brianza, Agrate Brianza, Italy.

Frontiers in Neuroscience

|January 24, 2024

Summary

Simulating spiking neural networks (SNNs) is challenging. SHIP, a Spiking (neural network) Hardware In PyTorch tool, bridges model-driven and data-driven approaches for hardware development and validation.

Keywords:

compact model data flow neuromorphic engineering simulation platforms spiking neural network supervised training temporal progress

More Related Videos

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

Author Spotlight: Modular Neuronal Networks for Analyzing Brain Functions

Author Spotlight: Modular Neuronal Networks for Analyzing Brain Functions

Published on: June 7, 2024

Related Experiment Videos

Last Updated: Jul 5, 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

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

Author Spotlight: Modular Neuronal Networks for Analyzing Brain Functions

Author Spotlight: Modular Neuronal Networks for Analyzing Brain Functions

Published on: June 7, 2024

Area of Science:

Computational neuroscience
Neuromorphic engineering
Materials science

Background:

Spiking neural networks (SNNs) research spans neuroscience, computation, and hardware development.
Simulating SNNs presents computational challenges, often leading to specialized, non-unified platforms.
Existing simulation approaches create a dichotomy between detailed biological modeling and efficient data processing.

Purpose of the Study:

To address the need for a unified simulation environment for SNN hardware development.
To facilitate the modeling, analysis, and training of prospective SNN systems.
To explore numerical challenges in SNN simulation.

Main Methods:

Introduction of SHIP (Spiking neural network) Hardware In PyTorch, a numerical tool.
SHIP supports algorithmic definition of network component models.
Enables monitoring of system states/outputs and training of synaptic weights via learning rules or supervised techniques.

Main Results:

SHIP provides a flexible environment for investigating and validating SNN hardware components.
It integrates aspects of both model-driven and data-driven simulation approaches.
Facilitates user-defined unsupervised learning and conventional supervised training.

Conclusions:

SHIP is a valuable tool for researchers and developers in hardware-based SNNs.
It enables efficient simulation and validation of novel materials and devices for neuromorphic computing.
The tool aids in advancing the development of artificial hardware counterparts to biological neural networks.