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Simulation experiments in bionics: a regulative methodological perspective

Abstract

Bionic technologies connecting biological nervous systems to computer or robotic devices for therapeutic purposes have been recently claimed to provide novel experimental tools for the investigation of biological mechanisms. This claim is examined here by means of a methodological analysis of bionics-supported experimental inquiries on adaptive sensory-motor behaviours. Two broad classes of bionic systems (regarded here as hybrid simulations of the target biological system) are identified, which differ from each other according to whether a component of the biological target system is replaced by an artificial component, or else a component of an artificial system is replaced by a biological component. The role of these hybrid systems in the modelling of adaptive sensory-motor biological behaviours is discussed with reference to bionics-supported experiments on the mechanisms of body stabilization in lampreys. Methodological problems emerging from these case studies often arise in computer-based and biorobotic simulations of biological behaviours too. Accordingly, the present analysis contributes to identifying a more general regulative methodological framework for the machine-based modelling of biological systems.

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Notes

  1. 1.

    Research is currently focusing on invasive and non-invasive human-machine interfaces. In the first case, electrodes are positioned in the near proximity of peripheral nerves (Navarro et al. 2005) or brain neurons (Schwartz 2004). Extensive research on brain control of artificial devices through invasive interfaces has been carried out on humans (Hochberg et al. 2006), monkeys (Carmena et al. 2003) and rats (Chapin et al. 1999). Non-invasive interfaces with the central nervous system (based on electroencephalography) and with the peripheral nervous system (based on electromyography) do allow for robust, though more limited, control of computer applications and prostheses (Millán et al. 2004). Limitations of non-invasive interfaces for this purpose are mainly related to the poor spatial resolution of the electrodes (Lebedev and Nicolelis 2006).

  2. 2.

    As pointed out by Lebedev and Nicolelis (2006, p. 536), “less than a decade ago, hardly anyone could have predicted that attempts to build direct functional interfaces between brains and artificial devices, such as computers and robotic limbs, would have succeeded so readily”. Since 1999, when a striking demonstration of brain-control of a robotic manipulator through an invasive interface was presented by Chapin et al. (1999), “a continuous stream of research papers has kindled an enormous interest in BMIs [brain machine interfaces] among the scientific community and the lay public” (Lebedev and Nicolelis 2006, p. 536).

  3. 3.

    Experimental potentialities of bionic systems, as far as recording, analysis and manipulation of neural activity are concerned, are explored by Nicolelis (2003). Invasive experiments of this kind may enable one to study “the cellular properties of complex neural circuits” and to understand “how different populations of neurons in a neural circuit contribute to the encoding of motor parameters”. With the support of bionic technologies, “the dynamics of several neural circuits can be measured simultaneously” and, provided that no biocompatibility problem affects the invasive interface in the long run, one may also “quantify the physiological changes that take place in different components of a neural circuit as animals learn various sensorimotor and cognitive tasks” (Nicolelis 2003, p. 418).

  4. 4.

    Theoretical models of this kind readily accommodate with various accounts of functional-mechanistic modelling of biological behaviours discussed in the philosophy of science. According to Cummins (1975) biological capacities can be explained by the analytical strategy, which proceeds by identifying components of the target system whose coordinated manifestation results in the behaviour to be explained. An explanatory strategy based on modular and causal accounts of system behaviours is defended by Glennan (2005, p. 445), according to which “a mechanism for a behaviour is a complex system that produces that behaviour by the interaction of a number of parts, where the interactions between parts can be characterized by direct, invariant, change-relating generalizations”. Similar views are advocated by Woodward (2002, 2003); Bechtel and Richardson (1993); Craver (2007); Nagel (1961), chapter 12), Rosenblueth and Wiener (1945).

  5. 5.

    “A theoretical model consists of a set of assumptions about some object or system. […] A theoretical model describes a type of object or system by attributing to it what might be called an inner structure, composition, or mechanism, reference to which will explain various properties exhibited by that object or system” (Achinstein 1965, p. 103).

  6. 6.

    As the presently examined case-studies illustrate, simulation bionic systems have been used to address key issues related to the study of lamprey sensory-motor mechanisms, as they are stated by Orlovsky et al. (1992, p. 479): “We wanted to understand (1) What signals are coming from the vestibular sensory organs when the orientation of the animal in the gravity field is changing; (2) How this information is processed in the brainstem and what commands the brainstem sends to the spinal cord; (3) How the spinal motor mechanisms respond to commands coming from the brain, and what motor pattern is used for correcting the body orientation”.

  7. 7.

    Note that, in this case, the reticulo-spinal pathway and the motor organs have not been materially removed from the animal; rather, their contribution to body stabilization is inhibited by the platform.

  8. 8.

    One may object that the two methodologies are identical, insofar as model MH in Fig. 6 actually results from the artificial replacement of biological components a2 and a3, as in the ArB case. However, they appear to be different if one looks at the relationship between the target component and the structure of the bionic system: while in the ArB strategy one investigates the behaviour of component b1 by replacing it with an artificial component, in the BrA strategy one studies the behaviour of b1 by including it in an artificial system.

  9. 9.

    More interesting results on the behaviour of the vestibulo-reticular component have been obtained by successive experiments which have been carried out with the same kind of systems. See the Conclusions for a sketch of the strategy which has been followed there.

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Acknowledgments

I am grateful to Guglielmo Tamburrini for stimulating discussions on the methodological issues addressed here. I wish to thank Franco Giorgi, Federico Laudisa, and an anonymous referee, for their valuable comments and criticisms on earlier versions of this paper.

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Correspondence to Edoardo Datteri.

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Datteri, E. Simulation experiments in bionics: a regulative methodological perspective. Biol Philos 24, 301–324 (2009). https://doi.org/10.1007/s10539-008-9133-y

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Keywords

  • Bionics
  • Biorobotic experiments
  • Cybernetics
  • Methodology of simulations
  • Theoretical models of adaptive behaviours