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

EmbBERT: Attention under 2 MB memory.

Neural networks : the official journal of the International Neural Network Society·2026

Same author

Big Data, Machine Learning, and Personalization in Health Systems: Ethical Issues and Emerging Trade-Offs.

Science and engineering ethics·2025

Same author

Mixture-of-experts graph transformers for interpretable particle collision detection.

Scientific reports·2025

Same author

Micrographia in Parkinson's Disease: Automatic Recognition through Artificial Intelligence.

Movement disorders clinical practice·2025

Same author

Adaptive token selection for scalable point cloud transformers.

Neural networks : the official journal of the International Neural Network Society·2025

Same author

Position: Topological Deep Learning is the New Frontier for Relational Learning.

Proceedings of machine learning research·2025

Same journal

Hidden Data Recovery and Forecasting via Next-Generation Reservoir Computing With Multiscale Delay Selection.

IEEE transactions on neural networks and learning systems·2026

Same journal

CAFF-CIL: Causality-Aware Freedom Forgetting Approach for Class-Incremental Learning.

IEEE transactions on neural networks and learning systems·2026

Same journal

Harmonic Autoencoding Framework for Multiple Tasks in Magnetic Particle Imaging Reconstruction.

IEEE transactions on neural networks and learning systems·2026

Same journal

A Survey on Human-Centric Voice-Face Multimodal Learning.

IEEE transactions on neural networks and learning systems·2026

Same journal

Vision-Assisted Foundation Model for Solving Multitask Vehicle Routing Problems.

IEEE transactions on neural networks and learning systems·2026

Same journal

FP3O: Enabling Proximal Policy Optimization in Multiagent Cooperation With Parameter-Sharing Versatility.

IEEE transactions on neural networks and learning systems·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 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

NACHOS: Neural Architecture Search for Hardware-Constrained Early-Exit Neural Networks.

Matteo Gambella, Jary Pomponi, Simone Scardapane

IEEE Transactions on Neural Networks and Learning Systems

|July 21, 2025

Summary

This summary is machine-generated.

This study introduces NACHOS, a novel neural architecture search framework for designing efficient early-exit neural networks (EENNs). NACHOS automates the joint design of EENNs, optimizing accuracy and computational efficiency under hardware constraints.

More Related Videos

Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits

Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits

Published on: April 15, 2015

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Related Experiment Videos

Last Updated: Sep 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

Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits

Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits

Published on: April 15, 2015

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Area of Science:

Artificial Intelligence
Computer Science

Background:

Early-exit neural networks (EENNs) offer efficiency and effectiveness by allowing predictions at intermediate stages.
Manual design of EENNs is complex, time-consuming, and requires expert knowledge for optimal configuration.
Automating EENN design using neural architecture search (NAS) is an active research area.

Purpose of the Study:

To present NACHOS, the first NAS framework for designing hardware-constrained EENNs.
To enable the joint optimization of EENN backbone and early-exit classifiers (EECs).
To satisfy constraints on accuracy and multiply-accumulate (MAC) operations.

Main Methods:

Developed NACHOS, a NAS framework for joint backbone and EEC design.
Incorporated hardware constraints (accuracy and MAC operations) into the search process.
Identified Pareto optimal EENN solutions balancing accuracy and computational cost.

Main Results:

NACHOS successfully designs EENNs that are competitive with state-of-the-art models.
The framework provides a set of admissible Pareto optimal solutions.
Investigated novel regularization techniques for auxiliary classifiers.

Conclusions:

NACHOS offers a fully automated approach to designing efficient EENNs.
The framework effectively balances accuracy and computational efficiency for hardware deployment.
Future work includes further exploration of regularization strategies for EENN optimization.