Simulation experiments in bionics: a regulative methodological perspective

Datteri, Edoardo

doi:10.1007/s10539-008-9133-y

Cited by 8 publications

(7 citation statements)

References 28 publications

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“…I discuss how such technologies can contribute to basic neuroscience as well as neuroengineering, asking if changing the brain can help in the project of explaining the brain. My answer will be roundly positive, in contrast to the views of two philosophers of neuroscience, Edoardo Datteri (2009) and Carl Craver (2010), who have challenged scientists' claims that BCI's can provide important insights into brain function which are not accessible via other techniques. Datteri and Craver's criticisms centre around the fact that hybrid BCI systems diverge from the natural systems basic neuroscience aims to model because of plasticity induced by prosthetic implants.…”

Section: Introduction -Extending the Brainmentioning

confidence: 90%

“…Craver (2010) calls these a kind of prosthetic. I often use Datteri's (2009) preferred terms, bionic and hybrid. For the purposes of this article, the terms prosthetic, bionic, or hybrid should be considered interchangeable in reference to models and experiments.…”

Section: Introduction -Extending the Brainmentioning

confidence: 99%

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confidence: 96%

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Extending, changing, and explaining the brain

Chirimuuta

2013

Biol Philos

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This paper addresses concerns raised recently by Edouardo Datteri (2009) and Carl Craver (2010) about the use of brain-extending prosthetics in experimental neuroscience. Since the operation of the implant induces plastic changes in neural circuits, it is reasonable to worry that operational knowledge of the hybrid system will not be an accurate basis for generalisation when modelling the unextended brain. I argue, however, that Datteri's no-plasticity constraint unwittingly rules out numerous experimental paradigms in behavioural and systems neuroscience which also elicit neural plasticity.Furthermore, I propose that Datteri and Craver's arguments concerning the limitations of prosthetic modelling in basic neuroscience, as opposed to neuroengineering, rests on too narrow a view of the ways models in neuroscience should be evaluated, and that a more pluralist approach is needed. I distinguish organisational validity of models from mechanistic validity. I argue that while prosthetic models may be deficient in the latter of these explanatory virtues because of neuroplasticity, they excel in the former since organisational validity tracks the extent to which a model captures coding principles that are invariant with plasticity. Changing the brain, I conclude, is one viable route towards explaining the brain.

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Section: Introduction -Extending the Brainmentioning

confidence: 90%

Section: Introduction -Extending the Brainmentioning

confidence: 99%

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confidence: 96%

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Extending, changing, and explaining the brain

Chirimuuta

2013

Biol Philos

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“…These technologies may enable the restoration of communication, sensory and motor abilities lost due to accidents, stroke, or other causes of severe injury (for example, see Hochberg et al, 2006 ). In addition, leading researchers have claimed that bionics technologies can provide unique and new experimental tools to discover brain mechanisms (e.g., see Chirimuuta, 2013 ) and that from an epistemological point of view, such systems are assumed to be useful in various ways to discover hidden or unknown aspects of biological mechanisms (see Datteri, 2009 ; Datteri and Tamburrini, 2007 ). Based on the distinction proposed in Datteri (2017) between “stimulation–connection” and “simulation–replacement methodology” for the study of the brain I will show in the following how the use of the MCG supports the argument attributing the lack of a direct explanatory account of the artificial components used in such systems.…”

Section: Two Types Of Simulations In Hybrid Bionic Systems and Minima...mentioning

confidence: 99%

Analyzing the Explanatory Power of Bionic Systems With the Minimal Cognitive Grid

Lieto

2022

Front. Robot. AI

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In this article, I argue that the artificial components of hybrid bionic systems do not play a direct explanatory role, i.e., in simulative terms, in the overall context of the systems in which they are embedded in. More precisely, I claim that the internal procedures determining the output of such artificial devices, replacing biological tissues and connected to other biological tissues, cannot be used to directly explain the corresponding mechanisms of the biological component(s) they substitute (and therefore cannot be used to explain the local mechanisms determining an overall biological or cognitive function replicated by such bionic models). I ground this analysis on the use of the Minimal Cognitive Grid (MCG), a novel framework proposed in Lieto (Cognitive design for artificial minds, 2021) to rank the epistemological and explanatory status of biologically and cognitively inspred artificial systems. Despite the lack of such a direct mechanistic explanation from the artificial component, however, I also argue that the hybrid bionic systems can have an indirect explanatory role similar to the one played by some AI systems built by using an overall structural design approach (but including the partial adoption of functional components). In particular, the artificial replacement of part(s) of a biological system can provide i) a local functional account of that part(s) in the context of the overall functioning of the hybrid biological–artificial system and ii) global insights about the structural mechanisms of the biological elements connected to such artificial devices.

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“…(1) Neural Response = f (external state) 6 For technical details see Nicolas-Alonso and Gomez-Gil (2012) and references therein; for philosophical discussion see Datteri (2008Datteri ( ) (2017, Craver (2010) and Chirimuuta (2013). 7 There are BMI's which record from other areas of the motor system; for ease of presentation I refer only to M1 interfaces.…”

Section: Describementioning

confidence: 99%

Prediction versus understanding in computationally enhanced neuroscience

Chirimuuta

2020

Synthese

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The use of machine learning instead of traditional models in neuroscience raises significant questions about the epistemic benefits of the newer methods. I draw on the literature on model intelligibility in the philosophy of science to offer some benchmarks for the interpretability of artificial neural networks (ANN's) used as a predictive tool in neuroscience. Following two case studies on the use of ANN's to model motor cortex and the visual system, I argue that the benefit of providing the scientist with understanding of the brain trades off against the predictive accuracy of the models. This trade-off between prediction and understanding is better explained by a non-factivist account of scientific understanding.

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Simulation experiments in bionics: a regulative methodological perspective

Cited by 8 publications

References 28 publications

Extending, changing, and explaining the brain

Extending, changing, and explaining the brain

Analyzing the Explanatory Power of Bionic Systems With the Minimal Cognitive Grid

Prediction versus understanding in computationally enhanced neuroscience

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