if we conceive of a being whose faculties are so sharpened that he can follow every molecule in its course, such a being, whose attributes are as essentially finite as our own, would be able to do what is impossible to us. For we have seen that molecules in a vessel full of air at uniform temperature are moving with velocities by no means uniform, though the mean velocity of any great number of them, arbitrarily selected, is almost exactly uniform. Now let us suppose that such a vessel is divided into two portions, A and B, by a division in which there is a small hole, and that a being, who can see the individual molecules, opens and closes this hole, so as to allow only the swifter molecules to pass from A to B, and only the slower molecules to pass from B to A. He will thus, without expenditure of work, raise the temperature of B and lower that of A, in contradiction to the second law of thermodynamics.

The Devil We Know…a strange space, at once both abstract and real. Perhaps the demon is abstract enough to explore the realities of control and communication. What started out as a pure thought experiment inspired decades of speculation about its possible existence and then attempts to build an artificial demon. Maxwell’s demon inspired the crucial breakdowns that Donna Haraway identifies as part of postwar computer research, or what has been called the “cyborg sciences.”

Abstract-Jan 29 1993We have simulated numerically an automated Maxwell’s demon in- spired by Smoluchowski’s ideas of 1912. Two gas chambers of equal area are connected via an opening that is covered by a trapdoor. The trapdoor can open to the left but not to the right, and is intended to rectify naturally occurring variations in density between the two chambers. Our results confirm that though the trapdoor behaves as a rectifier when large density differences are imposed by external means, it can not extract useful work from the thermal motion of the molecules when left on its own.

Maxwell’s Demon, Rectifiers, and the Second Law: Computer Simulation of Smoluchowski’s Trapdoor

Abstract-4 Jun 2021With increasing interest in the control of systems at the nano- and mesoscopic scales, studies have been focused on the limit of the energy dissipation in an open system by refining the concept of the Maxwell’s demon. To uncover the underlying physical principle behind a system controlled by a demon, we prove a previously unexplored set of fluctuation theorems. These fluctuation theorems imply that there exists an intrinsic nonequilibrium state of the system, led by the nonnegative demon-induced dissipative information. A consequence of this analysis is that the bounds of both work and heat are tighter than the limits predicted by the Sagawa-Ueda theorem. We also suggest a possible experimental test of these work and heat bounds.

Abstract-April 27 2021Darwinian evolution tends to produce energy-efficient outcomes. On the other hand, energy limits computation, be it neural and probabilistic or digital and logical. Taking a particular energy-efficient viewpoint, we define neural computation and make use of an energy-constrained computational function. This function can be optimized over a variable that is proportional to the number of synapses per neuron. This function also implies a specific distinction between adenosine triphosphate (ATP)-consuming processes, especially computation per se vs. the communication processes of action potentials and transmitter release. Thus, to apply this mathematical function requires an energy audit with a particular partitioning of energy consumption that differs from earlier work. The audit points out that, rather than the oft-quoted 20 W of glucose available to the human brain, the fraction partitioned to cortical computation is only 0.1 W of ATP [L. Sokoloff, Handb. Physiol. Sect. I Neurophysiol. 3, 1843–1864 (1960)] and [J. Sawada, D. S. Modha, “Synapse: Scalable energy-efficient neurosynaptic computing” in Application of Concurrency to System Design (ACSD) (2013), pp. 14–15]. On the other hand, long-distance communication costs are 35-fold greater, 3.5 W. Other findings include 1) a 108-fold discrepancy between biological and lowest possible values of a neuron’s computational efficiency and 2) two predictions of N, the number of synaptic transmissions needed to fire a neuron (2,500 vs. 2,000).