Brainoware | 03 Feb 2024

Source: TH

Why in News?

Recently, scientists have seamlessly integrated brain-like tissue with electronics to create Brainoware, an 'organoid neural network (ONN)' capable of recognising voices and solving complex mathematical problems.

• This innovative system extends neuromorphic computing, to a new level by directly integrating brain tissue into a computer.

What is Brainoware?

• About:
  • Brainoware is an innovative computing system that melds brain-like tissue with electronics.
  • Brainoware integrates brain organoids with microelectrodes, forming an 'organoid neural network (ONN)' that directly incorporates living brain tissue into the computing process.
    • Brain organoids are 3D tissues that simulate the structure and function of the human brain. They are derived from human embryonic stem cells, and are able to self-organize.
    • Brain organoids are similar to the brain's cell composition and structure, and can reflect the brain's developmental process. They are used as models to study human brain development and brain-related diseases.
  • ONNs are different from artificial neural networks, which are made of silicon chips because they use biological neurons that can adapt and learn from their environment.

• Operational Mechanism:
  • Three-Layered Architecture: Input, Reservoir, and Output
    • Input Signals Processing:
      • Input signals, in the form of electrical stimulation, are processed through the ONNs.
    • Reservoir:
      • The reservoir, acting as a black-box, converts signals into mathematical entities that the computer can efficiently process, eliminating the need for constant back-and-forth data transfer.
    • Output Readout:
      • The output layer, modified conventional computer hardware, interprets Brainoware's neural activity, providing a tangible result.
• Advantages over Traditional Neuromorphic Computing:
  • Memory and Processing Separation:
    • Traditional neural networks face a challenge where memory units and data processing units are separate, leading to increased time and energy demands for complex problem-solving.
    • Previous attempts to improve efficiency involved neuromorphic chips with short-term memory. However, these chips could only partially mimic brain functions and required further enhancements in processing capability and energy efficiency.
  • Biological Neural Network Integration:
    • Brainoware utilizes a biological neural network, comprising live brain cells, to address the inefficiencies in traditional neuromorphic computing.
      • Unlike AI hardware, brain cells store memory and process data without physically separating the two, resulting in significantly lower energy consumption.
• Challenges and Considerations:
  • The system does face challenges, including the technical expertise and infrastructure required to maintain a biological neural network.
    • Ethical concerns also arise, questioning the consciousness of organoids and their use in a mechanistic way.
• Future Prospects:
  • While Brainoware is in its early stages, continued study of the 'organoid neural network' could provide foundational insights into learning mechanisms, neural development, and the cognitive implications of neurodegenerative diseases.
    • This could potentially contribute to advancements in neuroscience and medical research.
    • It opens possibilities at the intersection of tissue engineering, electrophysiology, and neural computation.