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On reverse engineering in the cognitive and brain sciences

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Abstract

Various research initiatives try to utilize the operational principles of organisms and brains to develop alternative, biologically inspired computing paradigms and artificial cognitive systems. This article reviews key features of the standard method applied to complexity in the cognitive and brain sciences, i.e. decompositional analysis or reverse engineering. The indisputable complexity of brain and mind raise the issue of whether they can be understood by applying the standard method. Actually, recent findings in the experimental and theoretical fields, question central assumptions and hypotheses made for reverse engineering. Using the modeling relation as analyzed by Robert Rosen, the scientific analysis method itself is made a subject of discussion. It is concluded that the fundamental assumption of cognitive science, i.e. complex cognitive systems can be analyzed, understood and duplicated by reverse engineering, must be abandoned. Implications for investigations of organisms and behavior as well as for engineering artificial cognitive systems are discussed.

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Notes

  1. Cummin’s scheme evidently employs Frege’s principle of compositionality, well-known in computer science as ‘divide and conquer’.

  2. A corresponding class of models in mathematics is characterized by the superposition theorem for homogeneous linear differential equations stating that the sum of any two solutions is itself a solution.

  3. We must differentiate between the natural, complex system and its description using modeling techniques from linear system theory or nonlinear mathematics.

  4. Only recently, differences between proponents of reverse engineering on how it is appropriately to be accomplished became public. The heads of the two reverse engineering projects mentioned, Markram (2006) and Modha (Systems of Neuromorphic Adaptive Plastic Scalable Electronics, SyNAPSE 2008), disputed publicly as to what granularity of the modeling is needed to reach a valid simulation of the brain. Markram questioned the authenticity of Modha’s respective claims (Brodkin 2009).

  5. See (Schierwagen 2007) for discussion of the computational approaches (including the neurocomputational one) to brain function, and their shortcomings.

  6. In mathematics, a problem is called ill-posed if no solution or more than one solution exists, or if the solutions depend discontinuously upon the initial data.

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  1. Intelligent Systems Department, Institute for Computer Science, University of Leipzig, Leipzig, Germany

    Andreas Schierwagen

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  1. Andreas Schierwagen
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Correspondence to Andreas Schierwagen.

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Schierwagen, A. On reverse engineering in the cognitive and brain sciences. Nat Comput 11, 141–150 (2012). https://doi.org/10.1007/s11047-012-9306-0

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