Search Results

1 - 9 of 9 items :

  • "brain emulation" x
Clear All

‘Reilly Media. Richardson, L. F. 1948. Variation of the Frequency of Fatal Quarrels With Magnitude. Journal of the American Statistical Association . 43(244): 523-546. Ryan, M. D. 2013. Cloud computing security: The scientific challenge, and a survey of solutions, Journal of Systems and Software. 86(9):2263-2268. Sandberg, A. 2013. Feasibility of Whole Brain Emulation, in Philosophy and Theory of Artificial Intelligence, ed. Vincent Müller, SAPERE 5, 251-264. Sandberg, A. 2014. Ethics of brain emulations. Journal of Experimental & Theoretical Artificial Intelligence, special

References Cattell, R., and Parker, A. 2012. Challenges for Brain Emulation : Why is Building a Brain so Difficult ? Natural Intelligence 1(3). Deca, D. 2012. Available Tools for Whole Brain Emulation. International Journal of Machine Consciousness 04(01):67-86. Dortmans, P. J. 2005. Forecasting, backcasting, migration landscapes and strategic planning maps. Futures 37(4):273-285. Dunn, P. 2009. Why hasn’t commercial air travel gotten any faster since the 1960s? MIT Engineering. Fiala, J. 2002. Three-dimensional structure of synapses in the brain and on theWeb

online ahead of print: 12/16/2010) Kodandaramaiah, S. B. et al. 2012. Automated whole-cell patch-clamp electrophysiology of neurons in vivo. Nature Methods 9: 585 - 587. doi: doi:10.1038/nmeth.1993 Koene, Randal A. 2012a. Fundamentals of Whole Brain Emulation: State, Transition and Update Representations. International Journal of Machine Consciousness 4: 5 - 21. doi: 10.1142/S179384301240001X Koene, Randal A. 2012b. Experimental Research in Whole Brain Emulation: the need for Innovative in-vivo Measurement Techniques. International Journal of Machine Consciousness 4

electronically from http://www.pnas.org/content/109/27/11014. Deca, D. 2011. Available tools for whole brain emulation. Int. J. of Machine Consciousness 04:67. Demeyer, S.; Rysselberghe, F. V.; and et. al. 2005. The LAN-simulation: a refactoring teaching example. 8th Int. Workshop on Principles of Software Evolution, Lisbon 1:123-134. Code and teaching materials available from www.lore.ua.ac.be/Research/Artefacts/RefactoringLabSession. Eagleman, D. 2011. Incognito. New York: Pantheon Books. Eigen, M. 2013. From Strange Simplicity to Complex Familiarity. New York: Oxford

. 2011. Wiring specificity in the direction-selectivity circuit of the retina. Nature 471:183-188. Deca, D. 2012. Available Tools for Whole Brain Emulation. International Journal of Machine Consciousness 4:67. doi: 10.1142/S1793843012400045. Goertzel, B., and Pennachin, C. 2007. Artificial General Intelligence. Springer. Koene, R. 2012a. Experimental Research in Whole Brain Emulation: The Need for Innovative In-Vivo Measurement Techniques. Special Issue of the International Journal of Machine Consciousness 4(1). doi: 10.1142/S1793843012500047. Koene, R. 2012b. Toward

Abstract

A recent theory of physical information based on the fundamental principles of causality and thermodynamics has proposed that a large number of observable life and intelligence signals can be described in terms of the Causal Mathematical Logic (CML), which is proposed to encode the natural principles of intelligence across any physical domain and substrate. We attempt to expound the current definition of CML, the “Action functional” as a theory in terms of its ability to possess a superior explanatory power for the current neuroscientific data we use to measure the mammalian brains “intelligence” processes at its most general biophysical level. Brain simulation projects define their success partly in terms of the emergence of “non-explicitly programmed” complex biophysical signals such as self-oscillation and spreading cortical waves. Here we propose to extend the causal theory to predict and guide the understanding of these more complex emergent “intelligence Signals”. To achieve this we review whether causal logic is consistent with, can explain and predict the function of complete perceptual processes associated with intelligence. Primarily those are defined as the range of Event Related Potentials (ERP) which include their primary subcomponents; Event Related Desynchronization (ERD) and Event Related Synchronization (ERS). This approach is aiming for a universal and predictive logic for neurosimulation and AGi. The result of this investigation has produced a general “Information Engine” model from translation of the ERD and ERS. The CML algorithm run in terms of action cost predicts ERP signal contents and is consistent with the fundamental laws of thermodynamics. A working substrate independent natural information logic would be a major asset. An information theory consistent with fundamental physics can be an AGi. It can also operate within genetic information space and provides a roadmap to understand the live biophysical operation of the phenotype

. 2045 Strategic Social Initiative More, M. 2013. Use of Lifelog in Reanimation, Workshop on Cryonics Reanimation Developments. Portland. Oren, T.I. 2003. Personality Representation Processable in Fuzzy Logic for Human Behavior Simulation.In Proceedings of the 2003 Summer Computer Simulation Conference , 11-18. Montreal, Canada: University of Toronto. Sandberg, A.; and Bostrom, N. 2008 Whole Brain Emulation. A Roadmap, Technical Report, #2008 - 3, Oxford, England: Future of Humanity Institute, Oxford University. Seymour, L. G. 2005. Why and How CARPE Should be

. [online] [Retrieved November 22, 2018]. Available at: http://media.johnwiley.com.au/product_data/excerpt/10/11183343/1118334310-109.pdf SANDBERG, A. (2014): Ethics of brain emulation. In: Journal of Experimental & Theoretical Artificial Intelligence , 26(3). [online] [Retrieved November 22, 2018]. Available at: http://www.aleph.se/papers/Ethics%20of%20brain%20emulations%20draft.pdf SCIENTISTS’ OPEN LETTER ON CRYONICS. [online] [Retrieved December 11, 2018]. Available at: https://www.biostasis.com/scientists-open-letter-on-cryonics/ SHAW, D. M. (2009) Cryoethics

Supercomputing , 240–244. New York, NY, USA: ACM. Hume, D. 1748. An Enquiry Concerning Human Understanding . London. Hutter, M. 2005. Universal Artificial Intelligence: Sequential Decisions based on Algorithmic Probability . Berlin: Springer. Kirsh, D. 1991. Foundations of AI: the big issues. Artificial Intelligence 47:3–30. Koene, R., and Deca, D. 2013. Editorial: Whole Brain Emulation seeks to Implement a Mind and its General Intelligence through System Identification. Journal of Artificial General Intelligence 4:1–9. Kowalski, R. 1979. Logic for Problem Solving . New