Title: Deciphering the Low Level Reliability Schemes of the Brain

Abstract:

Since over a decade the semiconductor industry has been facing several challenges. One was power consumption followed a bit later by reliability (due to increased sensitivities to noises and variations). In 2013 some of these permeated into IEEE Rebooting Computing and, in late 2015, matured into A Nanotechnology-Inspired Grand Challenge for Future Computing aiming to create “a new type of computer that can proactively interpret and learn from data, solve unfamiliar problems using what it has learned, and operate with the energy efficiency of the human brain.”

This presentation will focus on reliability, and follow on von Neumann’s prescient The Computer and the Brain (1958). In particular, we will take steps for deciphering the low-level reliability schemes used by neurons. The original tale from The Computer and the Brain will be reversed, i.e., we shall start from the Brain, and, relying on the latest discoveries, try to get a (much) better understanding of highly reliable computing schemes. In this context, it is fascinating to remember what Robert N. Noyce, co-founder of Intel and co-inventor of the integrated circuit, mentioned: “Until now we have been going the other way; that is, in order to understand the brain we have used the computer as a model of it. Perhaps it is time to reverse this reasoning: to understand where we should go with the computer, we should look to the brain for some clues.” (“The Next 100 Years,” IEEE Centenary, 1984).

The presentation will begin by shedding light on the gated ion channels (the elementary nano-devices of the Brain) and argue about the different ways they communicate. Deciphering the reliability of arrays of gated ion channels will link to results published by Abraham de Moivre in 1738. These will be complemented by fresh reliability analyses of the transport networks formed by microtubules. We will show that these can be seen as 3D generalizations of the 2D hammock networks introduced by Edward F. Moore and Claude E. Shannon in 1956. Our preliminary results will reveal the very high reliability achievable by both schemes at very small redundancy factors even when devices are completely random. These reliability schemes are not following on the well-established approaches introduced by John von Neumann in 1956 (based on voting), while we plan to continue investigating them under the just started Novel Bio-Inspired Cellular Nano-Architectures (BioCell-NanoART) project.

The main conclusion is that the novel computational models presented here expose one of the reasons why our current silicon-based approaches are falling short of doing what the Brain does, and implicitly chart directions for research―both for the computing and for the VLSI/nano communities.