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Related Experiment Video

Updated: May 7, 2026

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array
09:44

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array

Published on: March 8, 2024

Finding a roadmap to achieve large neuromorphic hardware systems.

Jennifer Hasler1, Bo Marr

  • 1School of Electrical and Computer Engineering, Georgia Institute of Technology Atlanta, GA, USA.

Frontiers in Neuroscience
|September 24, 2013
PubMed
Summary

Neuromorphic systems offer energy-efficient computing by mimicking neural structures. This roadmap outlines their technology evolution for enhanced performance, efficiency, and applications.

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Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of the problem,...

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Area of Science:

  • Computer Engineering
  • Neuroscience
  • Materials Science

Background:

  • Current CMOS digital computing faces physical limitations.
  • Neuromorphic systems offer a promising alternative, mimicking biological neural networks for energy efficiency.
  • These systems are crucial for both engineering solutions and understanding neural computation.

Purpose of the Study:

  • To provide a technology evolution roadmap for neuromorphic systems.
  • To offer engineers foresight similar to the benefits derived from Moore's Law in IC design.
  • To guide the development and application of neuromorphic technologies.

Main Methods:

  • Analysis of current neuromorphic system capabilities.
  • Projection of future trends in energy efficiency, performance, and size scaling.
Keywords:
FPAASimulinkneuromorphic engineeringreconfigurable analog

Related Experiment Videos

Last Updated: May 7, 2026

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array
09:44

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array

Published on: March 8, 2024

  • Examination of the evolving implementation and application landscape.

    • Main Results:

    • Anticipated advancements in scaling energy efficiency, performance, and size.
    • Projected evolution in the implementation and application domains of neuromorphic systems.
    • A clear technological trajectory for neuromorphic engineering.

    Conclusions:

    • Neuromorphic systems are poised for significant growth and impact.
    • The outlined roadmap will aid engineers in navigating future developments.
    • Continued innovation in neuromorphic systems will address limitations of current computing paradigms.