On the Role of Information Transfer’s Speed in Technological and Biological Computations

Abstract

Information is commonly considered as a mathematical quantity that forms the basis of computing. In mathematics, information can propagate instantly, so its transfer speed is not the subject of information science. In all kinds of implementations of computing, whether technological or biological, some material carrier for the information exists, so the information’s propagation speed cannot exceed the speed of the carrier. Because of this limitation, for any implementation, one must consider the transfer time between computing units. We need a different mathematical method to take this limitation into account: classic mathematics can only describe infinitely fast and infinitely small computing system implementations. The difference between the mathematical handling methods leads to different descriptions of the behavior of the systems. The correct handling also explains why biological implementations can have lifelong learning and technological ones cannot. The conclusion about learning evidences matches others’ experimental evidence, both in technological and biological computing.

Revising the Classic Computing Paradigm and Its Technological Implementations

“…It is just a subtlety of the technological development that today's electronic computing devices work using timing relations more similar to our brains than vacuum tubes (and the mathematical abstraction based on it). Of course, there exists a resemblance in their operations, but handling temporal behavior also manifests in crucial differences [14]; among others-that our brain works asynchronously and autosynchronized, while our computing systems work (mostly) in a synchronized way. These differences also explain why biology can store information natively (without needing a special memory unit), why biological systems can natively learn, and why technological systems cannot [15]; furthermore, why using technological systems for understanding learning and behavior in biological systems are mostly irrelevant [16].…”

“…For its physical (and maybe philosophical) relevance, a more detailed explanation see in [58]. Giving an explicit role to time also reveals why information storing and learning have a quite different implementation and behavior in biological and technological neural computing [14]. Furthermore, it explains that methods of learning and machine learning are orthogonal [16], and so are intelligence and machine intelligence.…”

“…Given that we use the time as the primary variable, we can use the formalism to describe neuronal operation (where the conduction velocity is modulated) with time rather than position. However, in the latter case, the formalism is less straightforward (and enables us to understand how information is stored and learning occurs in biology [14]). To illustrate the effect of introducing transfer time into computing, we describe a thought computing experiment resembling Einstein's example of a moving observer in physics.…”

“…Neurons are "off" until their synapses receive some input, then for some time, they get "open", after some time "inactivated", and after some time "off" again. As discussed in [14], the three-state operation mode is desirable from several points of view. The present design point of view, replacing the "inactivated" state with a "down" edge, enables reaching higher operating frequency [63].…”