Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

A three-component dynamical index of consciousness-related neural organisation.

Biological cybernetics·2026
Same author

Subject-Wise Depression Screening from Eight-Channel Resting-State EEG Using Asymmetry-Aware Spectral Features and Connectivity Ablation.

Sensors (Basel, Switzerland)·2026
Same author

Comparative analysis of energy transfer mechanisms for neural implants.

Frontiers in neuroscience·2024
Same author

Gene cloning and characterization of a novel recombinant 40-kDa heat shock protein from Mesobacillus persicus B48.

World journal of microbiology & biotechnology·2023
Same author

Social distancing enhanced automated optimal design of physical spaces in the wake of the COVID-19 pandemic.

Sustainable cities and society·2021
Same author

The Effectiveness of Image Augmentation in Deep Learning Networks for Detecting COVID-19: A Geometric Transformation Perspective.

Frontiers in medicine·2021

Related Experiment Video

Updated: May 10, 2025

Author Spotlight: Streamlined Brain and Skull Modeling for Enhanced Neurosurgical Planning in NHP Research
06:33

Author Spotlight: Streamlined Brain and Skull Modeling for Enhanced Neurosurgical Planning in NHP Research

Published on: February 9, 2024

1.0K

Neuromorphic algorithms for brain implants: a review.

Wiktoria Agata Pawlak1, Newton Howard1

  • 1ni2o, Washington, DC, United States.

Frontiers in Neuroscience
|April 28, 2025
PubMed
Summary

Neuromorphic computing offers efficient algorithms for brain implants. This review highlights algorithmic progress, aiming to advance neural implants and related fields like medical diagnostics and robotics.

Keywords:
biohybrid interfacesbrain implantsbrain-computer interfaces (BCIs)data compressionmixed-signal designneurocomputational modelsneuromorphic computingspiking neural networks (SNNs)

More Related Videos

Syringe-injectable Mesh Electronics for Stable Chronic Rodent Electrophysiology
09:58

Syringe-injectable Mesh Electronics for Stable Chronic Rodent Electrophysiology

Published on: July 21, 2018

23.0K
Surgical Training for the Implantation of Neocortical Microelectrode Arrays Using a Formaldehyde-fixed Human Cadaver Model
08:11

Surgical Training for the Implantation of Neocortical Microelectrode Arrays Using a Formaldehyde-fixed Human Cadaver Model

Published on: November 19, 2017

11.3K

Related Experiment Videos

Last Updated: May 10, 2025

Author Spotlight: Streamlined Brain and Skull Modeling for Enhanced Neurosurgical Planning in NHP Research
06:33

Author Spotlight: Streamlined Brain and Skull Modeling for Enhanced Neurosurgical Planning in NHP Research

Published on: February 9, 2024

1.0K
Syringe-injectable Mesh Electronics for Stable Chronic Rodent Electrophysiology
09:58

Syringe-injectable Mesh Electronics for Stable Chronic Rodent Electrophysiology

Published on: July 21, 2018

23.0K
Surgical Training for the Implantation of Neocortical Microelectrode Arrays Using a Formaldehyde-fixed Human Cadaver Model
08:11

Surgical Training for the Implantation of Neocortical Microelectrode Arrays Using a Formaldehyde-fixed Human Cadaver Model

Published on: November 19, 2017

11.3K

Area of Science:

  • Neuroscience
  • Computer Science
  • Biomedical Engineering

Background:

  • Neuromorphic computing is emerging as a transformative technology.
  • Current research predominantly focuses on hardware advancements.
  • Algorithmic development for neuromorphic systems, especially for neural implants, requires focused attention.

Purpose of the Study:

  • To review recent algorithmic progress in neuromorphic computing for brain implants.
  • To explore neurocomputational models suitable for neuromorphic hardware.
  • To inspire the development of next-generation neural implants and related applications.

Main Methods:

  • Literature review of algorithmic advances in neuromorphic computing.
  • Analysis of current and emerging neurocomputational models.
  • Discussion of potential implementation on neuromorphic hardware.

Main Results:

  • Significant progress in algorithms tailored for neuromorphic hardware.
  • Identification of neurocomputational models with potential for enhanced efficiency.
  • Demonstration of potential for algorithms to match or exceed traditional methods.

Conclusions:

  • Algorithmic innovation is crucial for realizing the potential of neuromorphic brain implants.
  • Advanced algorithms can improve computational efficiency and performance in neural implants.
  • These advancements have implications for medical diagnostics, robotics, and future neural interface technologies.