= Neuron Bit Energy =

 .''Synaptic Energy Use and Supply'', Julia J. Harris, Renaud Jolivet, and David Attwell
 .'''Neuron''', Volume 75, Issue 5, p762–777, 6 September 2012
 . DOI: http://dx.doi.org/10.1016/j.neuron.2012.08.019
 . '''p764:'''  24,000 ATP per bit
  . ATP 30 kJ/mole, about 0.3 eV, about 7500 eV per bit
 . kT ln2 at room temperature 18 meV, Shannon efficiency 2.5e-6 

In fairness, the brain is a reaction machine, most of the information is stored as trained synaptic connection.  Neural energy is expended within a framework that already contains predefined courses of action that are optimized for productivity and survival.  Within that schema, a collection of neurons making a decision and generating muscle stimuli is using those bits far more efficiently (but with much less immediate flexibility) than a general purpose computer.   

A better analogy would be a silicon chip that can grow its own gates and wires.  A gate array can simulate changing its own wiring, but is far less efficient per bit processed than hard wired logic.  A system that could actually move physical wires and gates around to adapt to tasks would be far more efficient per bit than any existing general purpose electronic digital processor - and we call those systems ASICs (Application Specific Integrated Circuits) - moving the wires is a design and fabrication process that takes years.

So - assume that neural flexibility provides a factor of 1000 advantage over general purpose bits, so that the Shannon efficiency is closer to 2e-3.  Assume a nano-engineered system that can (after '''much''' engineering) escape Darwinian constraints can get close to 2e-2, in a context of continuously "rewire-able" connection systems.  A 60 Kelvin system can probably do as much with 0.4 watts as a 300 Kelvin brain can do with 20 watts.  

For comparison, see this recent Intel paper about integrated circuit logic energy:
 
 . Benchmarking of Beyond-CMOS Exploratory Devices for Logic Integrated Circuits
 . IEEE Journal on Exploratory Solid-State Computational Devices and Circuits  
  . DMITRI E. NIKONOV (Senior Member, IEEE), AND IAN A. YOUNG (Fellow, IEEE)
   . Components Research, Intel Corporation, Hillsboro, OR 97124 USA
 . DOI: http://dx.doi.org/10.1109/JXCDC.2015.2418033