Abstract
Searle is right, of course—and the reason is quite obvious. Compared to other natural sciences such as physics or astronomy, neuroscience is only a very young discipline. Surgeons have been repairing broken skulls since the Neolithic Era and even treating mental disorders with trepanation, but they had only naked-eye phenomenological evidence to work with.
The result is that the philosophy of mind is unique among contemporary philosophical subjects, in that all of the most famous and influential theories are false.
John Searle, Mind, 2004
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Notes
- 1.
In an address delivered at the International Congress of Physiology, Richet (1910) comments on the theory of humorism as follows: But what is truly extraordinary, what surpasses our wildest dreams, is the fact that for sixteen hundred years all physicians and all physiologists remained bound in the shackles of this incomprehensible error of the four cardinal humours. By what miracle was the spirit of conservatism or of routine able to hide the truth to such a degree? The men of science and the doctors of former times were no less intelligent than those of to-day. Nevertheless they accepted without a shadow of proof these childish theories; they could not see most simple facts, and they saw most complicated things which not only did not exist but which were not even probable.
- 2.
See Feynman et al. (2011) for an intuitive explanation of the Bohr van Leeuwen theorem.
- 3.
Yes, even for Mercury, for which the amount of the perihelion precession caused by general relativity amounts to less than an arc-minute per century (Clemence 1947).
- 4.
This does not mean that the spatial structure of the dendritic tree has no computational properties. We make this clear in Sect. 2.1.4. However, for the theoretical and computational models discussed here, we choose the point neuron model as an appropriate level of abstraction. Any statements about biology must, of course, be validated with appropriate data.
- 5.
The required precision can admittedly be the subject of dispute. However, just as the motion of individual molecules is usually not considered in air flow simulations, it is usually possible to find a general consensus of what can be considered irrelevant imperfections of the assumed model.
- 6.
We shall later discern between simulation and emulation when referring to conventional (von Neumann) computing architectures and physical-model neuromorphic hardware, respectively.
- 7.
This has been used to argue in favor of the necessity of “biomorphic” hardware, which represents a detailed physical model of biological neural circuits.
References
O. Breitwieser, Investigation of a cortical attractor-memory network. Bachelor thesis, Ruprecht-Karls-Universität Heidelberg, 2011. HD-KIP 11-173
O. Breitwieser, Towards a neuromorphic implementation of spike-based expectation maximization. Master thesis, Ruprecht-Karls-Universität Heidelberg, 2015
D. Brüderle, M.A. Petrovici, S. Jeltsch, B. Vogginger, S. Friedmann, M. Schmuker, J. Kremkow, T. Clayton, V. Petkov, J. Bill, M. Albert, A. Hartel, J. Partzsch, E. Müller, L. Muller, O. Bichler, J. Schemmel, K. Meier. Simulator-like Exploration of Network Architectures with the Facets Hardware Systems and PYNN (2010). url http://www.kip.uni-heidelberg.de/cms/groups/vision/galleries_media/cne2010_experiments/
I. Bytschok, From shared input to correlated neuron dynamics: Development of a predictive framework. Ph.D. thesis, Diploma thesis, University of Heidelberg, 2011
G.M. Clemence, The relativity effect in planetary motions. Rev. Mod. Phys. 19(4), 361 (1947)
R.C. Decharms, A. Zador, Neural representation and the cortical code. Ann. Rev. Neurosci. 23(1), 613–647 (2000)
R.P. Feynman, R.B. Leighton, M. Sands, Feynman lectures on physics, in Mainly Electromagnetism and Matter, vol. ii–the new millennium edn. (2011)
J. Jordan, I. Bytschok, T. Tetzlaff, T. Pfeil, O. Breitwieser, J. Bill, M. Diesmann, A. Gruebl, J. Schemmel, M. Petrovici et al., Neural networks as sources of uncorrelated noise for functional neural systems, Technical report, Computational and Systems Neuroscience (2014)
T. Korcsak-Gorzo, Firing states of recurrent leaky integrate-and-fire networks, January 2015. Bachelor thesis
L. Leng, Deep learning architectures for neuromorphic hardware. Master thesis, Ruprecht-Karls-Universität Heidelberg, 2014. HD-KIP 14-26
P. Müller. Distortions of neural network models induced by their emulation on neuromorphic hardware devices. Diploma thesis, Ruprecht-Karls-Universität Heidelberg, 2011. HD-KIP-11-172
V. Petkov, Toward belief propagation on neuromorphic hardware. Diploma thesis, Ruprecht-Karls-Universität Heidelberg, 2012. HD-KIP 12-23
M.A. Petrovici, S. Millner, M.O. Schwartz, T. Pfeil, S. Jeltsch, A. Hartel, J. Schemmel, K. Meier. Experiments on Brainscales Hardware Systems (2012). url https://capocaccia.ethz.ch/capo/wiki/2012/bssexperiments12
M.A. Petrovici, T. Pfeil, S. Friedmann, J. Partzsch, S. Jeltsch, M.O. Schwartz, B. Vogginger, A. Grübl, D. Husmann, J. Schemmel, K. Meier, Exploring Network Architectures with the Facets/brainscales Hardware and PYNN (2011). url https://capocaccia.ethz.ch/capo/wiki/2011/bsshw11
D. Probst, A neural implementation of probabilistic inference in binary probability spaces. Master thesis, Ruprecht-Karls-Universität Heidelberg, 2014
C. Richet, An address on ancient humorism and modern humorism: delivered at the international congress of physiology held in vienna, september 27th to 30th. Br. Med. J. 2(2596), 921 (1910)
B. Rivkin, On the memory characteristic of a cortical atractor network. Bachelor thesis, Ruprecht-Karls-Universität Heidelberg, 2014
M. Roth, Predictive stochastic inference - from abstract models to neuromorphic implementation. Bachelor thesis, Ruprecht-Karls-Universität Heidelberg, 2014
J.R. Searle, Mind: A Brief Introduction, vol. 259 (Oxford University Press, Oxford, 2004)
D. Stöckel, Boltzmann sampling with neuromorphic hardware. Bachelor thesis, Ruprecht-Karls-Universität Heidelberg, 2015
C. Weilbach, An online learning algorithm for lif-based boltzmann machines. Bachelor thesis, Ruprecht-Karls-Universität Heidelberg, 2015
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Petrovici, M.A. (2016). Prologue. In: Form Versus Function: Theory and Models for Neuronal Substrates . Springer Theses. Springer, Cham. https://doi.org/10.1007/978-3-319-39552-4_1
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