Science & Tech

Integrating Brain Tissue and Electronics in Computing

  • Researchers have successfully fused brain-like tissue with electronics to create a ‘organoid neural network.’
  • This breakthrough represents a big step forward in neuromorphic computing by directly embedding brain tissue into computer systems.

Brain Tissues in Computers

  • Scientists from Indiana University, the University of Cincinnati, Cincinnati Children’s Hospital Medical Centre, and the University of Florida collaborated to achieve a breakthrough in brain tissue technology for computers.
  • Publication: The study, published on December 11, represents the convergence of tissue engineering, electrophysiology, and neural computation, broadening the scope of scientific and engineering disciplines.

Context of artificial intelligence (AI)

  • AI’s foundation: AI is built on artificial neural networks, which are silicon-based representations of the human brain that can analyse large datasets.
  • Memory and Processing Separation: Traditional AI hardware divides memory and processing units, resulting in inefficiencies when transmitting data between them.

Introducing Biological Neural Networks.

  • Biocomputing Emergence: Scientists are investigating biological neural networks made up of live brain cells as an alternative. These networks can handle both memory and data processing.
  • Energy Efficient: Brain cells store memory and process data without physically separating them.

Organoid Neural Network

  • Brain organoids, three-dimensional collections of brain cells, were used to form a ‘organoid neural network.’
  • Human pluripotent stem cells were differentiated into a variety of brain cells, including neuron progenitor cells, early-stage neurons, mature neurons, and astrocytes.
  • The network was integrated into a reservoir computer, which included input, reservoir, and output layers.

Brainoware’s Capabilities

  • Brainoware can anticipate complex mathematical functions, like the Henon map.
  • Voice Recognition: The system correctly identified Japanese vowels voiced by participants with a 78% accuracy rate.
  • Efficiency: Brainoware demonstrated equivalent accuracy to artificial neural networks while requiring minimal training.

Promising insights and limitations

  • Foundational insights: The work sheds light on learning mechanisms, brain development, and cognitive aspects of neurodegenerative illnesses.
  • Challenges: Brainware requires technical competence and infrastructure. Organoids have diverse cell populations and must be optimised for homogeneity.
  • Ethical considerations: The merging of organoids and AI poses ethical problems concerning consciousness and dignity.

Future studies 

  • It could focus on enhancing input encoding methods and ensuring homogeneity in organoids for longer tests.
  • Complex Computing Problems: Researchers want to tackle more difficult computing tasks.
  • Ethical Discourse: The argument over organoid awareness and dignity will continue to progress.
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