Organoid intelligence (OI) is an emerging field of study in computer science and biology that develops and studies biological 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]
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.
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. [6]
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. [7]
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. [8]
Neuroscience is the scientific study of the nervous system, its functions and disorders. It is a multidisciplinary science that combines physiology, anatomy, molecular biology, developmental biology, cytology, psychology, physics, computer science, chemistry, medicine, statistics, and mathematical modeling to understand the fundamental and emergent properties of neurons, glia and neural circuits. The understanding of the biological basis of learning, memory, behavior, perception, and consciousness has been described by Eric Kandel as the "epic challenge" of the biological sciences.
In machine learning, a neural network is a model inspired by the structure and function of biological neural networks in animal brains.
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 (AC), also known as machine consciousness (MC), 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. The same terminology can be used with the term "sentience" instead of "consciousness" when specifically designating phenomenal consciousness.
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.
An artificial neuron is a mathematical function conceived as a model of biological neurons in a neural network. Artificial neurons are the elementary units of artificial neural networks. The artificial neuron is a function that receives one or more inputs, applies weights to these inputs, and sums them to produce an output.
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. The implementation of neuromorphic computing on the hardware level can be realized by oxide-based memristors, spintronic memories, threshold switches, transistors, among others. Training software-based neuromorphic systems of spiking neural networks can be achieved using error backpropagation, e.g., using Python based frameworks such as snnTorch, or using canonical learning rules from the biological learning literature, e.g., using BindsNet.
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. In addition to neuronal and synaptic state, SNNs incorporate the concept of time into their operating model. The idea is that neurons in the SNN do not transmit information at each propagation cycle, but rather transmit information only when a membrane potential—an intrinsic quality of the neuron related to its membrane electrical charge—reaches a specific value, called the threshold. When the membrane potential reaches the threshold, the neuron fires, and generates a signal that travels to other neurons which, in turn, increase or decrease their potentials in response to this signal. A neuron model that fires at the moment of threshold crossing is also called a spiking neuron model.
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.
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.
A hippocampus prosthesis is a type of cognitive prosthesis. Prosthetic devices replace normal function of a damaged body part; this can be simply a structural replacement or a rudimentary, functional replacement.
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.
Suffering risks, or s-risks, are risks involving an astronomical amount of suffering; much more than all of the suffering that has occurred on Earth thus far. They are sometimes categorized as a subclass of existential risks.
