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Neuroplasticity01:01

Neuroplasticity

Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.
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Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
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Neuromorphic adaptive plastic scalable electronics: analog learning systems.

Narayan Srinivasa1, Jose Cruz-Albrecht

  • 1Center for Neural and Emergent Systems, Information and System Sciences Department, HRL Laboratories, Malibu, California, USA. nsrinivasa@hrl.com

IEEE Pulse
|February 21, 2012
PubMed
Summary
This summary is machine-generated.

Programmable intelligent machines are inefficient in complex environments. The Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE) program aims to develop efficient, intelligent electronic neuromorphic machines for real-world applications.

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Area of Science:

  • Artificial Intelligence
  • Computer Engineering
  • Neuroscience

Background:

  • Programmable intelligent machines show limited utility in complex, real-world environments.
  • Current machines are 1 million to 1 billion times less efficient than biological systems.
  • Existing electronic neuromorphic machine technology lacks practical implementations.

Purpose of the Study:

  • To break the conventional programmable machine paradigm.
  • To define a new path for creating useful and intelligent machines.
  • To address the challenges in developing electronic neuromorphic machines for complex environments.

Main Methods:

  • Overview of the HRL Laboratories LLC project.
  • Discussion of progress made in the Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE) program.
  • Focus on developing electronic neuromorphic machine technology.

Main Results:

  • Limited utility of current programmable machines in real-world scenarios.
  • Significant efficiency gap between machines and biological systems.
  • Ongoing efforts to create practical neuromorphic machine implementations.

Conclusions:

  • Electronic neuromorphic machine technology is preferable for applications with infinite combinatorial complexity.
  • HRL Laboratories LLC is actively working on developing practical neuromorphic machines.
  • The SyNAPSE program aims to advance the field of intelligent machine development.