?? BIOCOMPUTING for BUSINESS

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?? #BIOCOMPUTING for #BUSINESS

??? Are you ready for #biological #computers and #robots?

??? The convergence of #digital and #biogenetic sciences will lead us to Biocomputing as well as biogenetic #humanoids.

??? Indeed, biocomputing could be faster, more efficient, and more powerful than silicon-based computing and #AI, and only require a fraction of the energy.

??? Somewhat similar to AI, #Organoid #Intelligence (#OI) is a form of biocomputing that harnesses brain #organoids using scientific and bioengineering.

??? Recent advances in human stem cell-derived brain organoids promise to replicate critical molecular and cellular aspects of #learning, #memory, and #cognition.

??? Standardized, 3D, myelinated brain organoids can now be produced with high cell density, enriched levels of glial cells, and gene expression critical for learning.

??? OI is an emerging multidisciplinary field working to develop biocomputing using 3D cultures of human brain organoids and #brain-#machine #interface tech.

??? OI requires scaling up brain organoids into complex, durable 3D structures enriched with cells and genes associated with learning, and connecting these to next-generation input/output devices and AI/machine learning systems.

??? OI requires new models, #algorithms, and interface tech to communicate with brain organoids, understand how they learn and compute, and process and store the massive amounts of data they will generate.

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?? ABOUT the DIAGRAM

??? As the diagram shows, at the core of OI is the 3D brain cell culture (organoid) that performs the computation.

??? The learning potential of the organoid is optimized by culture conditions and enrichment by cells and genes critical for learning (including IEGs).

??? The scalability, viability, and durability of the organoid are supported by integrated microfluidic systems.

??? Various types of input can be provided to the organoid, including electrical and chemical signals, synthetic signals from machine sensors, and natural signals from connected sensory organoids (e.g., retinal).

??? AI and machine learning (#ML) are used throughout to encode and decode signals and to develop hybrid biocomputing solutions, in conjunction with a suitable big-data management system.

??? The diagram thus presents an architecture and blueprint for an OI development and implementation program designed to:

● Determine the #biofeedback characteristics of existing human brain organoids caged in microelectrode shells, potentially using AI to analyze recorded response patterns to electrical and chemical (neurotransmitters and their corresponding receptor agonists and antagonists) stimuli.

● Empirically test, refine, and, where needed, develop neuro-computational theories that elucidate the basis of in vivo biological intelligence and allow us to interact with and harness an OI system.

● Scale up the brain organoid model to increase the quantity of biological matter, the complexity of brain organoids, the number of electrodes, algorithms for real-time interactions with brain organoids, and the connected input sources and output devices; and to develop big-data warehousing and machine learning methods to accommodate the resulting brain-directed computing capacity.

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?? IN SUM …

??? OI has the potential to unlock new neuro-mimetic AI algorithms (with the potential to overcome current AI limitations) and aid the development of new brain-computer-interface technology in numerous business use cases.

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Adapted from Source Link:? ?https://www.frontiersin.org/journals/science/articles/10.3389/fsci.2023.1017235/full

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