Organoid intelligence (OI) is an emerging field of study in computer science and biology that develops and studies biological wetware computing using 3D cultures of human brain cells (or brain organoids) and brain-machine interface technologies. [1] Such technologies may be referred to as OIs.
As opposed to traditional non-organic silicon-based approaches, OI seeks to use lab-grown cerebral organoids to serve as "biological hardware." Scientists hope that such organoids can provide faster, more efficient, and more powerful computing power than regular silicon-based computing and AI while requiring only a fraction of the energy. However, while these structures are still far from being able to think like a regular human brain and do not yet possess strong computing capabilities, OI research currently offers the potential to improve the understanding of brain development, learning and memory, potentially finding treatments for neurological disorders such as dementia. [2]
Thomas Hartung, [3] a professor from Johns Hopkins University, argues that "while silicon-based computers are certainly better with numbers, brains are better at learning." Furthermore, he claimed that with "superior learning and storing" capabilities than AIs, being more energy efficient, and that in the future, it might not be possible to add more transistors to a single computer chip, while brains are wired differently and have more potential for storage and computing power, OIs can potentially harness more power than current computers. [4]
Some researchers claim that even though human brains are slower than machines at processing simple information, they are far better at processing complex information as brains can deal with fewer and more uncertain data, perform both sequential and parallel processing, being highly heterogenous, use incomplete datasets, and is said to outperform non-organic machines in decision-making. [1]
Training OIs involve the process of biological learning (BL) as opposed to machine learning (ML) for AIs. BL is said to be much more energy efficient than ML. [1]
OI generates complex biological data, necessitating sophisticated methods for processing and analysis. [5] Bioinformatics provides the tools and techniques to decipher raw data, uncovering the patterns and insights. A Python interface is currently available for processing and interaction with brain organoids. [6]
Brain-inspired computing hardware aims to emulate the structure and working principles of the brain and could be used to address current limitations in artificial intelligence technologies. However, brain-inspired silicon chips are still limited in their ability to fully mimic brain function, as most examples are built on digital electronic principles. One study performed OI computation (which they termed Brainoware) by sending and receiving information from the brain organoid using a high-density multielectrode array. By applying spatiotemporal electrical stimulation, nonlinear dynamics, and fading memory properties, as well as unsupervised learning from training data by reshaping the organoid functional connectivity, the study showed the potential of this technology by using it for speech recognition and nonlinear equation prediction in a reservoir computing framework. [7]
While researchers are hoping to use OI and biological computing to complement traditional silicon-based computing, there are also questions about the ethics of such an approach. Examples of such ethical issues include OIs gaining consciousness and sentience as organoids and the question of the relationship between a stem cell donor (for growing the organoid) and the respective OI system. [8]
Enforced amnesia and limits on duration of operation without memory reset have been proposed as a way to mitigate the potential risk of silent suffering in brain organoids. [9]
Mind uploading is a speculative process of whole brain emulation in which a brain scan is used to completely emulate the mental state of the individual in a digital computer. The computer would then run a simulation of the brain's information processing, such that it would respond in essentially the same way as the original brain and experience having a sentient conscious mind.
Artificial consciousness, also known as machine consciousness, synthetic consciousness, or digital consciousness, is the consciousness hypothesized to be possible in artificial intelligence. It is also the corresponding field of study, which draws insights from philosophy of mind, philosophy of artificial intelligence, cognitive science and neuroscience.
Computational neuroscience is a branch of neuroscience which employs mathematics, computer science, theoretical analysis and abstractions of the brain to understand the principles that govern the development, structure, physiology and cognitive abilities of the nervous system.
Bio-inspired computing, short for biologically inspired computing, is a field of study which seeks to solve computer science problems using models of biology. It relates to connectionism, social behavior, and emergence. Within computer science, bio-inspired computing relates to artificial intelligence and machine learning. Bio-inspired computing is a major subset of natural computation.
Neuromorphic computing is an approach to computing that is inspired by the structure and function of the human brain. A neuromorphic computer/chip is any device that uses physical artificial neurons to do computations. In recent times, the term neuromorphic has been used to describe analog, digital, mixed-mode analog/digital VLSI, and software systems that implement models of neural systems. Recent advances have even discovered ways to mimic the human nervous system through liquid solutions of chemical systems.
An artificial brain is software and hardware with cognitive abilities similar to those of the animal or human brain.
A wetware computer is an organic computer composed of organic material "wetware" such as "living" neurons. Wetware computers composed of neurons are different than conventional computers because they use biological materials, and offer the possibility of substantially more energy-efficient computing. While a wetware computer is still largely conceptual, there has been limited success with construction and prototyping, which has acted as a proof of the concept's realistic application to computing in the future. The most notable prototypes have stemmed from the research completed by biological engineer William Ditto during his time at the Georgia Institute of Technology. His work constructing a simple neurocomputer capable of basic addition from leech neurons in 1999 was a significant discovery for the concept. This research was a primary example driving interest in creating these artificially constructed, but still organic brains.
Neuroinformatics is the emergent field that combines informatics and neuroscience. Neuroinformatics is related with neuroscience data and information processing by artificial neural networks. There are three main directions where neuroinformatics has to be applied:
Spiking neural networks (SNNs) are artificial neural networks (ANN) that more closely mimic natural neural networks. These models leverage timing of discrete spikes as the main information carrier.
Biological computers use biologically derived molecules — such as DNA and/or proteins — to perform digital or real computations.
Michelle Anne Mahowald was an American computational neuroscientist in the emerging field of neuromorphic engineering. In 1996 she was inducted into the Women in Technology International Hall of Fame for her development of the Silicon Eye and other computational systems. She died by suicide at age 33.
The evolution of the brain refers to the progressive development and complexity of neural structures over millions of years, resulting in the diverse range of brain sizes and functions observed across different species today, particularly in vertebrates.
In the field of computational neuroscience, brain simulation is the concept of creating a functioning computer model of a brain or part of a brain. Brain simulation projects intend to contribute to a complete understanding of the brain, and eventually also assist the process of treating and diagnosing brain diseases. Simulations utilize mathematical models of biological neurons, such as the hodgkin-huxley model, to simulate the behavior of neurons, or other cells within the brain.
Wetware is a term drawn from the computer-related idea of hardware or software, but applied to biological life forms.
Kwabena Adu Boahen is a Ghanaian-born Professor of Bioengineering and Electrical Engineering at Stanford University. He previously taught at the University of Pennsylvania.
A cognitive computer is a computer that hardwires artificial intelligence and machine learning algorithms into an integrated circuit that closely reproduces the behavior of the human brain. It generally adopts a neuromorphic engineering approach. Synonyms include neuromorphic chip and cognitive chip.
The Three Rs (3Rs) are guiding principles for more ethical use of animals in product testing and scientific research. They were first described by W. M. S. Russell and R. L. Burch in 1959. The 3Rs are:
A neural, or brain organoid, describes an artificially grown, in vitro, tissue resembling parts of the human brain. Neural organoids are created by culturing pluripotent stem cells into a three-dimensional culture that can be maintained for years. The brain is an extremely complex system of heterogeneous tissues and consists of a diverse array of neurons and glial cells. This complexity has made studying the brain and understanding how it works a difficult task in neuroscience, especially when it comes to neurodevelopmental and neurodegenerative diseases. The purpose of creating an in vitro neurological model is to study these diseases in a more defined setting. This 3D model is free of many potential in vivo limitations. The varying physiology between human and other mammalian models limits the scope of animal studies in neurological disorders. Neural organoids contain several types of nerve cells and have anatomical features that recapitulate regions of the nervous system. Some neural organoids are most similar to neurons of the cortex. In some cases, the retina, spinal cord, thalamus and hippocampus. Other neural organoids are unguided and contain a diversity of neural and non-neural cells. Stem cells have the potential to grow into many different types of tissues, and their fate is dependent on many factors. Below is an image showing some of the chemical factors that can lead stem cells to differentiate into various neural tissues; a more in-depth table of generating specific organoid identity has been published. Similar techniques are used on stem cells used to grow cerebral organoids.
This article presents a detailed timeline of events in the history of computing from 2020 to the present. For narratives explaining the overall developments, see the history of computing.
Oshiorenoya Eghierua Agabi, simply referred to as Osh., is a Nigerian-Swiss-American physicist, computational neuroscientist, bioengineer, and entrepreneur.
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