Extending, changing, and explaining the brain

Abstract

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.

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

“…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.…”

Memori Episodik sebagai Terra Incognita yang Membatasi Neuroteknologi

“…Karena neuroteknologi merupakan bagian dari human enhancement, maka ia juga memiliki dua fungsi: 1) sebagai enhancement as improvement, yakni digunakan dalam tujuan pengobatan seperti halnya di dalam bidang psikiatri yang berkaitan dengan pengontrolan operasi neuron, dan 2) sebagai enhancement for reshaping human being, yakni memperluas fungsi saraf, misalnya dengan melibatkan bagian korteks dalam pengendalian tungkai robotik (Chirimuuta, 2013). Dari dua fungsi ini, diturunkan pengembangan lainnya, yakni BMI (Brain-machine Interfaces) dan BCI (Brain-computer Interfaces).…”

“…BCI juga dapat diterapkan pada mereka yang mengalami gangguan motorik, yakni dengan menghasilkan input berupa pemerosesan data dari komputer. Oleh karena itu, BCI tidak boleh dianggap hanya sebagai antarmuka tambahan (seperti halnya BMI), tetapi juga sebagai sesuatu yang mengubah cara kerja bagian dalam otak dan dengan demikian menambah fungsi baru (Chirimuuta, 2013).…”

Prediction versus understanding in computationally enhanced neuroscience

“…(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.…”