Darwinian 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).

This chapter describes a multidisciplinary faculty self-study about reciprocity in service-learning.The study began with each co-author participating in a Decoding interview. We describe how Decoding combined with collaborative self-study had a positive impact on our teaching practice. The experience of this group of students and their professors, two co-authors of this chapter, is all too common and not an isolated incident. This is not surprising given that it has been argued that "service learning pedagogy requires and fosters learning-often transformational, paradigm-shifting learning-on the part of everyone involved, including faculty" (Clayton, Bringle & Hatcher 2013, 245). Indeed, given that service-learning necessitates faculty giving up control and working reciprocally with partners, sometimes much more than bridges need to be shifted and changed. Recognizing this, and due to our commitment to developing our teaching practice, we, the authors of this article, set out to investigate our own thinking with regard to reciprocity through a collaborative self-study, which included the use of a Decoding interview (Pace & Middendorf 2004). Building Bridges from the Decoding Interview to Teaching PracticeOur initial research examined how the Decoding interview followed by our self-study process generated learning about reciprocity specifically (Miller-Young, Dean, Rathburn, Pettit, Underwood, Gleeson, Lexier, Calvert, and Clayton 2015). In this chapter we report how Decoding had an impact on four areas of our teaching practice: 1) our identity and role as teachers, especially in an experiential learning setting; 2) the discovery of similarities and differences we shared with colleagues from diverse disciplines; 3) new strategies for forging meaningful and truly reciprocal relationships with partners in global service-learning field schools; and finally, 4) our design, delivery and assessment in field schools. Background and Methodology

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Computation in the human cerebral cortex uses less than 0.2 watts yet this great expense is optimal when considering communication costsDarwinian evolution tends to produce energy-efficient outcomes. On the other hand, energy limits computation, be it neural and probabilistic or digital and logical. After establishing an energyefficient viewpoint, we define computation and construct an energyconstrained, computational function that can be optimized. This function implies a specific distinction between ATP-consuming processes, especially computation per se vs action potentials and other costs of communication. As a result, the partitioning of ATPconsumption here differs from earlier work. A bits/J optimization of computation requires an energy audit of the human brain. Instead of using the oft-quoted 20 watts of glucose available to the brain (1, 2), the partitioning and audit reveals that cortical computation consumes 0.2 watts of ATP while long-distance communication costs are over 20-fold greater. The bits/joule computational optimization implies a transient information rate of more than 7 bits/sec/neuron. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 energy-efficient | evolution | maximum entropy | axonal leak T his paper examines neural computation from the perspec-1 tive that Nature favors efficiency. To do so requires first 2 quantifying a defined form of information generation that is 3 common to evolved cortical computation. Second, we quan-4 tify cortical costs. Given the context of Darwinian fitness, a 5 bits/joule optimization justifies our definition of computation 6 as Nature's perspective, as opposed to an ad hoc definition by 7 engineers. Nevertheless, some effort is expended on aligning 8 our definition with a particular first-principles derivation of 9 energy-optimized computation arising from statistical mechan-10 ics. 11 Key functions in our investigation are ATP production 12 and usage, processes which are dependent on glucose and 13 oxygen. Thus, our energy optimized function can be expressed 14 in terms of joules (J) per cycle, watts (W≡J/sec), moles of 15 ATP, oxygen, or glucose per operation or per sec. Often, 16 computer scientists, e.g. REFS, make a generic comparison 17 between the power expenditure of computers vs the ≈20 watts18 of glucose consumed by the human brain. To further facilitate 19 comparisons between the brain and engineered computers, we 20 offer a partitioning of the human brain energy budget in a 21 form homologous to traditional computing. The finding is that 22 neural computation consumes 0.17 watts of ATP and cortical 23 communication consumes 4.6 watts of ATP.24 To measure computational costs requires a definition of 25 computation. However taking the perspective of analog com-26 putation, any transformation qualifies as a computation. Like-27 wise, any such transformation can be quantified using a variety 28 of measures, arguably the most popular being Shannon's mu-29 tual information (3). Without denying the acceptability of this 30 most general perspective, we addend an ad...