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In neuroscience, researchers have developed informal notions of what it meansto reverse engineer a system, e.g., being able to model or simulate a system insome sense. A recent influential paper of Jonas and Kording, that examines amicroprocessor using techniques from neuroscience, suggests that commontechniques to understand neural systems are inadequate. Part of the difficulty,as a previous work of Lazebnik noted, lies in lack of formal language. Weprovide a theoretical framework for defining reverse engineering ofcomputational systems, motivated by the neuroscience context. Of specificinterest are recent works where, increasingly, interventions are being made toalter the function of the neural circuitry to both understand the system andtreat disorders. Starting from Lazebnik's viewpoint that understanding a systemmeans you can ``fix it'', and motivated by use-cases in neuroscience, wepropose the following requirement on reverse engineering: once an agent claimsto have reverse-engineered a neural circuit, they subsequently need to be ableto: (a) provide a minimal set of interventions to change the input/output (I/O)behavior of the circuit to a desired behavior; (b) arrive at this minimal setof interventions while operating under bounded rationality constraints (e.g.,limited memory) to rule out brute-force approaches. Under certain assumptions,we show that this reverse engineering goal falls within the class ofundecidable problems. Next, we examine some canonical computational systems andreverse engineering goals (as specified by desired I/O behaviors) where reverseengineering can indeed be performed. Finally, using an exemplar network, the``reward network'' in the brain, we summarize the state of currentneuroscientific understanding, and discuss how computer-science andinformation-theoretic concepts can inform goals of future neuroscience studies.

A Minimal Intervention Definition of Reverse Engineering a Neural Circuit