A wetware computer is an organic computer (which can also be known as an artificial organic brain or a neurocomputer) composed of organic material such as living neurons. Wetware computers composed of neurons are significantly different than conventional computers because they are thought to be capable in a way of "thinking for themselves", because of the dynamic nature of neurons. While wetware 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 examples of prototyping 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 acted as a primary example driving interest in the creation of these artificially constructed, but still organic brains.
The concept of wetware is an application of specific interest to the field of computer manufacturing. A specific application of wetware of interest in the computer industry is in regards to Moore’s law. This observation by Gordon Moore which states that the number of transistors, which can be placed on a silicon chip, is doubled roughly every two years. Moore's law has acted as a goal for the industry for decades, but as the size of computers continues to get smaller, the ability to meet this goal has become much more difficult, threatening to reach a plateau. Due to the difficulty in reducing the size of computers because of size limitations of transistors and integrated circuits wetware provides an unconventional alternative. A wetware computer composed of neurons is an ideal concept because, unlike conventional materials which operate in binary (on/ off) a neuron can shift between thousands of states, constantly altering its chemical conformation, and redirecting electrical pulses through over 200,000 channels in any of its many synaptic connections. Because of this large difference in the possible settings for any one neuron compared to the binary limitations of conventional computers the space limitations are far less.
The word wetware is a distinct and unconventional concept which draws slight resonance with both hardware and software from conventional computers. While “hardware” is understood as the physical architecture of tradition computational devices, built from the electrical circuitry and silicone plates, software is the conceptual opposite of hardware, it represents the encoded architecture of storage and instructions. Wetware is a separate concept which utilizes the formation of organic molecules, mostly complex cellular structures (such as neurons) to create a computational device such as a computer. In wetware the idea of hardware and software are intertwined and interdependent. The molecular, and chemical composition of the organic or biological structure would represent not only the physical structure of the wetware but also the "software", being continually reprogrammed by the discrete shifts in electrical pulses and chemical concentration gradients as the molecules change their structures to communicate signals. The responsiveness of a cell, proteins, and molecules to changing conformations both within their own structures and around them tie the idea of internal programming, and external structure together in a way which is completely alien to the current model of conventional computer architecture.
The structure of wetware represents a model where the external structure and internal programming are interdependent and unified; meaning that changes to the programming or internal communication between molecules of the device would represent a physical change in the structure. The dynamic nature of wetware borrows from the function of complex cellular structures in biological organisms. The combination of “hardware” and “software” into one dynamic, and interdependent system which utilizes organic molecules and complexes to create an unconventional model for computational devices is a specific example of applied biorobotics.
Cells in many ways can be seen as their own form of naturally occurring wetware, similar to the concept that the human brain is the preexisting model system for complex wetware. In his book Wetware: A Computer in Every Living Cell (2009) Dennis Bray explains his theory that cells, which are the most basic form of life, are just a highly complex computational structure, like a computer. To simplify one of his arguments a cell can be seen as a type of computer, utilizing its own structured architecture. In this architecture, much like a traditional computer many smaller components operate in tandem to receive input, process the information, and compute an output. In an overly simplified, and non-technical analysis cellular function can be broken into the following components. Information and instructions for execution are stored as DNA in the cell, RNA acts as a source for distinctly encoded input which processed by ribosomes and other transcription factors to access and process the DNA and to output a protein. Bray's argument in favor of viewing cells and cellular structures as models of natural computational devices is important when considering the more applied theories of wetware in relation to biorobotics.
Wetware and biorobotics are closely related concepts, which both borrow from similar overall principles. A biorobotic structure can be defined as a system modeled from a preexisting organic complex or model such as cells (neurons) or more complex structures like organs (brain) or whole organisms. Unlike wetware the concept of biorobotics is not always a system composed of organic molecules, but instead could be composed of conventional material which is designed and assembled in a structure similar or derived from a biological model. Biorobotics have many applications, and are used to address the challenges of conventional computer architecture. Conceptually, designing a program, robot, or computational device after a preexisting biological model such as a cell, or even a whole organism provides the engineer or programmer the benefits of incorporating into the structure the evolutionary advantages of the model.
In 1999 William Ditto and his team of researchers at Georgia Institute of technology and Emory University created a basic form of a wetware computer capable of simple addition by harnessing leech neurons. Leeches were used as a model organism due to the large size of their neuron, and the ease associated with their collection and manipulation.The computer was able to complete basic addition through electrical probes inserted into the neuron. The manipulation of electrical currents through neurons was not a trivial accomplishment, however. Unlike conventional computer architecture which is based on the binary on/ off states, neurons are capable of existing in thousands of states and communicate with each other through synaptic connections which each contain over 200,000 channels. Each can be dynamically shifted in a process called "self-organization" to constantly form and reform new connections. A conventional computer program called the "dynamic Clamp" was written by Eve Marder, a neurobiologist at Brandeis University that was capable of reading the electrical pulses from the neurons in real times, and interpreting them. This program was used to manipulate the electrical signals being input into the neurons to represent numbers, and to communicate with each other to return the sum. While this computer is a very basic example of a wetware structure it represents a small example with fewer neurons than found in a more complex organ. It is thought by Ditto that by increasing the amount of neurons present the chaotic signals sent between them will "self-organize" into a more structured pattern, such as the regulation of heart neurons into a constant heartbeat found in humans and other living organisms.
After his work creating a basic computer from leech neurons Ditto continued to work not only with organic molecules and wetware, but also on the concept of applying the chaotic nature of biological systems and organic molecules to conventional material and logic gates. Chaotic systems have advantages for generating patterns and computing higher order functions like memory, arithmetic logic, input/output operations. In his article Construction of a Chaotic Computer Chip Ditto discusses the advantages in programming of using chaotic systems, with their greater sensitivity to respond and reconfigure logic gates in his conceptual chaotic chip. The main difference between a chaotic computer chip, and a conventional computer chip is the reconfigurability of the chaotic system. A traditional computer chip, where a programmable gate array element must be reconfigured through the switching of many single-purpose logic gates, a chaotic chip is able to reconfigure all logic gates through the control of the pattern generated by the non-linear chaotic element.
The concept of cognitive biology evaluates cognition as a basic biological function. W. Tecumseh Fitch, a professor of cognitive biology at the University of Vienna is a leading theorist on ideas of cellular intentionality. The idea that not only do whole organisms have a sense of "aboutness" of intentionality, but that single cells also carry a sense of intentionality through cells ability to adapt and reorganize in response to certain stimuli. Fitch discusses the idea of nano-intentionality, specifically in regards to neurons, in their ability to adjust rearrangements in order to create neural networks. He discusses the ability of cells such as neurons to respond independently to stimuli such as damage to be what he considers "intrinsic intentionality" in cells, explaining that "[w]hile at a vastly simpler level than intentionality at the human cognitive level, I propose that this basic capacity of living things [response to stimuli] provides the necessary building blocks for cognition, and higher-order intentionality". Fitch describes the value of his research to specific areas of computer science such as artificial intelligence, and computer architecture. He states that "[I]f a researcher aims to make a conscious machine, doing it with rigid switches (whether vacuum tubes or static silicon chips) is barking up the wrong tree." Fitch believes that an important aspect of the development of areas such as artificial intelligence is wetware with nano-intentionalility, and autonomous ability to adapt and restructure itself.
In a review of the above-mentioned research conducted by Fitch, Daniel Dennett a professor at Tufts University discusses the importance of the distinction between the concept of hardware and software when evaluating the idea of wetware, and organic material such as neurons. Dennett discusses the value of observing the human brain, as a preexisting example of wetware. He sees the brain as having "the competence of a silicon computer to take on an unlimited variety of temporary cognitive roles". Dennett disagrees with Fitch on certain areas, such as the relationship of software/ hardware versus wetware, and what a machine with wetware might be capable of. Dennett highlights the importance of additional research into human cognition to better understand the intrinsic mechanism by which the human brain can operate, to better create an organic computer.
The subfield of organic computers and wetware is still largely hypothetical and in a preliminary stage. While there has yet to be major developments in the creation of an organic computer since the neuron based calculator developed by William Ditto in the 1990s the research mentioned in the sections above continues to push the field forward. Specific examples of research such as the modeling of chaotic pathways in silicon chips by William Ditto have made new discoveries in ways of organizing traditional silicon chips, and structuring computer Architecture to be more efficient, and better structured. Ideas emerging from the field of cognitive biology also help to continue to push discoveries in ways of structuring systems for artificial intelligence, to better imitate preexisting systems in humans.