Nano brain
A nano brain[1] is a conceptual device with massively parallel computational abilities, following the information processing principles of the human brain. This machine assembly would serve as an intelligent decision making unit for nanorobots[clarification needed].
Necessity
Computing in the 20th century was confined inside a box, or machine, called a computer or supercomputer; now, several parameters around us compute (Internet of Things). Earlier, we used to have small amounts of information stored in a book or server, and used to upload and download as required. In this and the next century, this could reverse, since the locations where we store information are becoming astronomically large. Without extending computing beyond serial logic, we could get isolated in the information domain without getting connected to the desired point. The human brain follows pattern-based computing like chaos, cellular automaton wherein millions of pixels of a particular image are processed at a time. The mechanism appears extremely slow and inaccurate. However, as the complexity of information increases, it performs more credibly than man-made supercomputers. A human brain can read captcha letters in seconds; a supercomputer cannot do that in a finite time. Exponential increment of information[2] generates a serious challenge for command, control and processing when connectivity among this information also increases exponentially.[3] Since software uses a sequential approach to analyze connectivity, to shrink infinite complexity into a finite limit, the mechanism of processing infinite information has to be embedded inside the hardware. A nano brain is such a device that physically addresses nearly infinite possible connections in seconds, alleviating the singularity in the software. This concept has the potential to solve at least three bottlenecks of human civilization, providing necessary intelligence to the robots, executing jobs without conventional power supply and finally, resolving the many-body problems which are in abundance in nature.
Conceptual novelty of the hardware
Historically, equivalent circuits have been proposed for neurons and even for the central nervous system. Creating an equivalent circuit is a reliable means to understand the electronics of a complex device as it defines the device in terms of fundamental circuit elements. The functional principle of a nano brain architecture is to exhibit "one-to-many communication at a time" among the constituent decision making units. By conventional circuit theory, it is parallel circuiting of elements. Since the conformation of wiring path changes along with electronic charge transport in the circuit, the equivalent circuit would change continuously. The possible combination of such circuits is astronomically large therefore instead of defining a function for an evolving equivalent circuit, the concept of cellular automaton has been introduced. In addition, due to spherical design, information spreads out from the center of the sphere and again reflects back to the center from the outer surface. Every single atom in the spherical nano-brain experiences a continuous interference of a feed forward information wave. Thus, the concept of circuit is violated here as collective evolution[4] of a potential distribution in a 3D space at a time cannot be represented as a linear sequence of events in discrete times.
Evolving hardware
Biological neural networks in the human brain evolve continuously throughout the lifespan, allowing the brain to form folds. There are several attempts to realize evolutionary circuits;[5] however, the majority of these attempts assemble a few static circuits and choose one of them during computation.[6] The human brain's evolution is functionally different: neurons change connections to make short-cuts. These routes lead to faster decision making (referred to as increasing efficiency through learning). A nano-brain changes connections between different sub-processors in a very similar fashion; therefore, it learns with experience. Since no hardware restriction is imposed in the nano-brain, the possibilities of changing are enormously large (not astronomical since restriction due to resource limitation imposes an upper limit); however, that number ranges in the order of millions compared to tens in the present evolving hardwares.
Multilayered decision making
There are several layers of subprocessors one top of another that constitute the nano brain, the bottom-most layer connects to the external machines or sensors and the top-most layer carry the fundamental rules that are never changed during nano brain computation. If a nano brain is made of cellular automaton then the number of cells decreases in every layer as computation transits upward. The embedded cellular automaton cluster that represent the entire nano brain, follows two different classes of cellular automaton rules. The first class of rules are those which are followed in the cellular automaton grid, and the other class of rules are basically the transition rules between two cellular automaton layers; each layer is termed as a sub-processor.
See also
References and citations
- ↑ Bandyopadhyay, A.; Acharya, A. (2008). "A 16 bit parallel processing in a molecular assembly" (pdf). PNAS. 105 (10): 3668–3672. doi:10.1073/pnas.0703105105.
- ↑ Cowlan, B. (1979). "A Revolution in Personal Communications: The Explosive Growth of Citizens Band Radio". In Gumpert, G.; Cathcart, R. Inter/Media Interpersonal Communication in a Media World. Oxford University Press. pp. 116–121. ISBN 9780195025057. OCLC 607201400. Search this book on
- ↑ Whittaker, S.; Terveen, L.; Hill, W.; Cherny, L. (1998). "The Dynamics of Mass Interaction". Proceedings of CSCW '98 (pdf). pp. 257–264. Search this book on
- ↑ Namatame, Akira (January 2006). "Collective Intelligence and Evolution". ERCIM News 64. Retrieved October 2, 2016.
- ↑ Stoica, A.; Zebulum, R. S.; Guo, X.; Keymeulen, D.; Ferguson, M. I.; Duong, V. (2004). "Taking evolutionary circuit design from experimentation to implementation: Some useful techniques and a silicon demonstration". Computers and Digital Techniques, IEE Proceedings. 151 (4): 295–300. doi:10.1049/ip-cdt:20040503.
- ↑ Miller, J. F.; Job, D.; Vassilev, V. K. (2000). "Principles in the Evolutionary Design of Digital Circuits—Part I". Genetic Programming and Evolvable Machines. 1 (1–2): 7–35. doi:10.1023/A:1010016313373.
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