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المراجع

الفصل الأول: الأبقار الكروية

  • Abbott, L. F., 2008, Theoretical neuroscience rising, Neuron 60(3):489–95 doi:10.1016/j.neuron.2008.10.019.
  • Cajal, S. R. y., 2004, Advice for a Young Investigator, MIT Press, Massachusetts, USA.
  • Lazebnik, Y., 2002, Can a biologist fix a radio Or, what I learned while studying apoptosis, Cancer Cell 2(3):179–82 doi:10.1016/s1535-6108(02)00133-2.
  • Nakata, K., 2013, Spatial learning affects thread tension control in orb-web spiders, Biology Letters 9(4) doi:10.1098/rsbl.2013.0052.
  • Russell, B., 2009, The Philosophy of Logical Atomism, Routledge, London.

الفصل الثاني: آلية إطلاق جهد الفعل في الخلايا العصبية

  • Branco, T., et al., 2010, Dendritic discrimination of temporal input sequences in cortical neurons, Science 329(5999):1671–75 doi:10.1126/science.1189664.
  • Bresadola, M., 1998, Medicine and science in the life of Luigi Galvani (1737–98), Brain Research Bulletin 46(5):367–80 doi:10.1016/s0361-9230(98)00023-9.
  • Brunel, N., & van Rossum, M. C. W., 2007, Lapicque’s 1907 paper: From frogs to integrate-and-fire, Biological Cybernetics, 9(5):337–39 doi:10.1007/s00422-007-0190-0.
  • Burke, R. E., 2006, John Eccles’ pioneering role in understanding central synaptic transmission, Progress in Neurobiology 78(3):173–88 doi:10.1016/j.pneurobio.2006.02.002.
  • Cajori, F., 1962, History of Physics, Dover Publications, New York, USA.
  • Finkelstein, G., 2013, Emil Du Bois-Reymond: Neuroscience, Self, and Society in Nineteenth-Century Germany, MIT Press, Massachusetts, USA.
  • Finkelstein, G., 2003, M. Du Bois-Reymond goes to Paris, The British Journal for the History of Science 36(3):261–300 www.jstor.org/stable/4028156. JSTOR.
  • Volta, A. & Banks, J., 1800, On the electricity excited by the mere contact of conducting substances of different kinds, The Philosophical Magazine 7(28):289–311 doi:10.1080/14786440008562590.
  • Huxley, A. F., 1964, Excitation and conduction in nerve: quantitative analysis, Science 145(3637):1154–59 doi:10.1126/science.145.3637.1154.
  • Bynum, W. F. & Porter, R., 2006, Johannes Peter Müller, Oxford Dictionary of Scientific Quotations, OUP, Oxford.
  • Kumar, A., et al., 2011, The role of inhibition in generating and controlling parkinson’s disease oscillations in the basal ganglia, Frontiers in Systems Neuroscience 5 doi:10.3389/fnsys.2011.00086.
  • Tyndall, J., 1876, Lessons in electricity IV, Popular Science Monthly 9, Wikisource.
  • Markram, H., et al., 2015, Reconstruction and simulation of neocortical microcircuitry, Cell 163(2):456–92 doi:10.1016/j. cell.2015.09.029.
  • McComas, A., 2001, Galvani’s Spark: The Story of the Nerve Impulse, Oxford University Press, USA.
  • Piccolino, M., 1998, Animal electricity and the birth of electrophysiology: the legacy of Luigi Galvani, Brain Research Bulletin 46(5):381–407 doi:10.1016/s0361-9230(98)00026-4.
  • Schuetze, S. M., 1983, The discovery of the action potential, Trends in Neurosciences 6:164–68 doi:10.1016/0166-2236(83)90078-4.
  • Squire, L. R., editor, 1998, The History of Neuroscience in Autobiography, Volume 1, Academic Press, Cambridge, Massachusetts, USA.
  • Squire, L. R., editor, 2003, The History of Neuroscience in Autobiography, Volume 4, Academic Press, Cambridge, Massachusetts, USA.
  • Squire, L. R., editor, 2006, The History of Neuroscience in Autobiography, Volume 5, Academic Press, Cambridge, Massachusetts, USA.

الفصل الثالث: تعلُّم الحَوْسَبة

  • Le, Q. V. & Schuster, M., 2016, A neural network for machine translation, at production scale, Google AI Blog, ai.googleblog.com/2016/09/a-neural-network-for-machine.html. Accessed 13 April 2020.
  • Albus, J. S., 1971, A theory of cerebellar function, Mathematical Biosciences 10(1):25–61 doi:10.1016/0025-5564(71)90051-4.
  • Anderson, J. A. & Rosenfeld, Edward, 2000, Talking Nets: An Oral History of Neural Networks, MIT Press, Massachusetts, USA.
  • Arbib, M. A., 2000, Warren McCulloch’s search for the logic of the nervous system, Perspectives in Biology and Medicine 43(2):193–216 doi:10.1353/pbm.2000.0001.
  • Bishop, G. H., 1946, Nerve and synaptic conduction, Annual Review of Physiology 8:355–74 doi:10.1146/annurev.ph.08.030146.002035.
  • Garcia, K. S., et al., 1999, Cerebellar cortex lesions prevent acquisition of conditioned eyelid responses, Journal of Neuroscience 19(24):10940–47 doi:10.1523/JNEUROSCI.19-24-10940.1999.
  • Gefter, A., 2015, The man who tried to redeem the world with logic, Nautilushttp://nautil.us/issue/21/information/the-man-who-tried-to-redeem-the-world-with-logic.
  • Hartell, N. A., 2002, Parallel fiber plasticity, Cerebellum 1(1):3–18 doi:10.1080/147342202753203041.
  • Linsky, B. & Irvine, A. D., 2019, Principia Mathematica, The Stanford Encyclopedia of Philosophy, edited by Zalta, E. N., Metaphysics Research Lab, Stanford University https://plato.stanford.edu/archives/fall2019/entries/principia-mathematica/.
  • McCulloch, W. S., 2016, Embodiments of Mind, MIT Press, Massachusetts, USA.
  • Papert, S., 1988, One AI or many? Daedalus 117(1):1–14 www.jstor.org/stable/20025136. JSTOR.
  • Piccinini, G., 2004, The first computational theory of mind and brain: a close look at Mcculloch and Pitts’s logical calculus of ideas immanent in nervous activity, Synthese 141(2):175–215 doi:10.1023/B:SYNT.0000043018.52445.3e.
  • Rosenblatt, F., 1957, The Perceptron, a Perceiving and Recognizing Automaton Project Para, Cornell Aeronautical Laboratory, New York, USA.
  • Russell, B., 2014, The Autobiography of Bertrand Russell, Routledge, London.
  • Schmidhuber, J., 2015, Who invented backpropagation? http://people.idsia.ch/∽juergen/who-invented-backpropagation.html. Accessed 13 April 2020.

الفصل الرابع: تكوين الذكريات والاحتفاظ بها

  • Bogacz, R., et al., 2001, A familiarity discrimination algorithm inspired by computations of the perirhinal cortex. Emergent Neural Computational Architectures Based on Neuroscience: Towards Neuroscience-Inspired Computing, Springer-Verlag, Switzerland 428–441.
  • Brown, R. E. & Milner, P. M., 2003, The legacy of Donald O. Hebb: more than the Hebb synapse, Nature Reviews Neuroscience 4(12):1013–19 doi:10.1038/nrn1257.
  • Chumbley, J. R., et al., 2008, Attractor models of working memory and their modulation by reward, Biological Cybernetics 98(1):11–18 doi:10.1007/s00422-007-0202-0.
  • Cooper, S. J., 2005, Donald O. Hebb’s synapse and learning rule: a history and commentary, Neuroscience and Biobehavioral Reviews 28(8):851–74 doi:10.1016/j.neubiorev.2004.09.009.
  • Fukuda, K., et al., 2010, Discrete capacity limits in visual working memory, Current Opinion in Neurobiology 20(2):177–82 doi:10.1016/j.conb.2010.03.005.
  • Fuster, J. M. & Alexander, G. E., 1971, Neuron activity related to short-term memory, Science (New York, USA) 173(3997):652–54 doi:10.1126/science.173.3997.652.
  • Hopfield, J. J., 2014, Whatever happened to solid state physics? Annual Review of Condensed Matter Physics 5(1):1–13 doi:10.1146/annurev-conmatphys-031113-133924.
  • Hopfield, J. J., 2018, Now what? Princeton Neuroscience Institute https://pni.princeton.edu/john-hopfield/john-j.-hopfield-now-what. Accessed 13 April 2020.
  • Kim, Sung Soo, et al., 2017, Ring attractor dynamics in the drosophila central brain, Science (New York, USA) 356(6340):849–53 doi:10.1126/science.aal4835.
  • Lechner, H. A., et al., 1999, 100 years of consolidation-remembering Müller and Pilzecker, Learning & Memory 6,(2):77–87 doi:10.1101/lm.6.2.77.
  • MacKay, D. J. C., 2003, Information Theory, Inference and Learning Algorithms, Cambridge University Press, UK.
  • Martin, S. J. & Morris, R. G. M., 2002, New life in an old idea: the synaptic plasticity and memory hypothesis revisited, Hippocampus 12(5):609–36 doi:10.1002/hipo.10107.
  • Pasternak, T. & Greenlee, M. W., 2005, Working memory in primate sensory systems, Nature Reviews Neuroscience 6(2):97–107 doi:10.1038/nrn1603.
  • Zhang, K., 1996, Representation of spatial orientation by the intrinsic dynamics of the head-direction cell ensemble: a theory, Journal of Neuroscience. www.jneurosci.org/content/16/6/2112. Accessed 13 April 2020.
  • Roberts, A. C. & Glanzman, D. L., 2003, Learning in aplysia: looking at synaptic plasticity from both sides, Trends in Neurosciences 26(12):662–70 doi:10.1016/j.tins.2003.09.014.
  • Sawaguchi, T. & Goldman-Rakic, P. S., 1991, D1 dopamine receptors in prefrontal cortex: involvement in working memory, Science 251(4996):947–50 doi:10.1126/science.1825731.
  • Schacter, D. L., et al., 1978, Richard Semon’s theory of memory, Journal of Verbal Learning and Verbal Behavior 17(6):721–43 doi:10.1016/S0022-5371(78)90443-7.
  • Skaggs, W. E., et al., 1995, A model of the neural basis of the rat’s sense of direction, Advances in Neural Information Processing Systems 7, edited by G. Tesauro et al., MIT Press, Massachusetts, USA 173–180. http://papers.nips.cc/paper/890-a-model-of-the-neural-basis-of-the-rats-sense-of-direction.pdf.
  • Tang, Y. P., et al., 1999, Genetic enhancement of learning and memory in mice, Nature 401(6748):63–69 doi:10.1038/43432.
  • Wills, T. J., et al., 2005, Attractor dynamics in the hippocampal representation of the local environment, Science (New York, USA) 308(5723):873–76 doi:10.1126/science.1108905.

الفصل الخامس: الاستثارة والتثبيط

  • Albright, T. & Squire, L., editors, 2016, The History of Neuroscience in Autobiography, Volume 9, Academic Press, Massachusetts, USA.
  • Blair, E. A. & Erlanger, J., 1933, A comparison of the characteristics of axons through their individual electrical responses, American Journal of 106(3):524–64 doi:10.1152/ajplegacy.1933.106.3.524.
  • Börgers, C., et al., 2005, Background gamma rhythmicity and attention in cortical local circuits: a computational study. Proceedings of the National Academy of Sciences of the United States of America 102(19):7002–07 doi:10.1073/pnas.0502366102.
  • Brunel, N., 2000, Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons, Journal of Computational Neuroscience 8(3):183–208 doi:10.1023/A:1008925309027.
  • Fields, R. D., 2018, Do brain waves conduct neural activity like a symphony? Scientific American https://www.scientificamerican.com/article/do-brain-waves-conduct-neural-activity-like-a-symphony. Accessed 14 April 2020.
  • Florey, E., 1991, GABA: history and perspectives, Canadian Journal of Physiology and Pharmacology 69(7):1049–56 doi:10.1139/y91-156.
  • Fye, W. Bruce, Ernst, Wilhelm, and Eduard Weber, Clinical Cardiology 23(9):709–10 doi:10.1002/clc.4960230915.
  • Mainen, Z. F. & Sejnowski, T. J., 1995, Reliability of spike timing in neocortical neurons, Science 268(5216):1503–06, doi:10.1126/science.7770778.
  • Brown University, 2019, Neuroscientists discover neuron type that acts as brain’s metronome: by keeping the brain in sync, these long-hypothesized but never-found neurons help rodents to detect subtle sensations. ScienceDaily https://www.sciencedaily.com/releases/2019/07/190718112415.htm. Accessed 14 April 2020.
  • Poggio, G. F. & Viernstein, L. J., 1964, Time series analysis of impulse sequences of thalamic somatic sensory neurons, Journal of Neurophysiology 27(4):517–45 doi:10.1152/jn.1964.27.4.517.
  • Shadlen, M. N. & Newsome, W.T., 1994, Noise, neural codes and cortical organization, Current Opinion in Neurobiology 4(4):569–79 doi:10.1016/0959-4388(94)90059-0.
  • Softky, W. R. & Koch, C., 1993, The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs, The Journal of Neuroscience 13(1):334–50 doi:10.1523/JNEUROSCI.13-01-00334.1993.
  • Stevens, C. F. & Zador, A. M., 1998, Input synchrony and the irregular firing of cortical neurons, Nature Neuroscience 1(3):210–17 doi:10.1038/659.
  • Strawson, G., 1994, The impossibility of moral responsibility, Philosophical Studies: An International Journal for Philosophy in the Analytic Tradition 75(1/2):5–24 https://www.jstor.org/stable/4320507.JSTOR.
  • Tolhurst, D. J., et al., 1983, The statistical reliability of signals in single neurons in cat and monkey visual cortex, Vision Research 23(8):775–85 doi:10.1016/0042-6989(83)90200-6.
  • Wehr, M. & Zador, A. M., 2003, Balanced inhibition underlies tuning and sharpens spike timing in auditory cortex, Nature 426(6965):442–46 doi:10.1038/nature02116.

الفصل السادس: مراحل الرؤية

  • Boden, M. A., 2006, Mind as Machine: A History of Cognitive Science, Clarendon Press, Oxford, UK.
  • National Physical Laboratory, 1959, Mechanisation of thought processes; proceedings of a symposium held at the National Physical Laboratory on 24th, 25th, 26th and 27th November 1958, H. M. Stationery Office, London, UK.
  • Buckland, M. K., 2006, Emanuel Goldberg and His Knowledge Machine, Greenwood Publishing Group, Connecticut, USA.
  • Cadieu, C. F., et al., 2014, Deep neural networks rival the representation of primate it cortex for core visual object recognition, PLOS Computational Biology 10(12) doi:10.1371/journal.pcbi.1003963.
  • Fukushima, K., 1970, A Feature extractor for curvilinear patterns: a design suggested by the mammalian visual system, Kybernetik 7(4):153–60 doi:10.1007/BF00571695.
  • Fukushima, K., 1980, Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position, Biological Cybernetics 36(4):193–202 doi:10.1007/BF00344251.
  • He, K., et al., 2015, Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. ArXiv:1502.01852 [Cs] http://arxiv.org/abs/1502.01852.
  • Hubel, D. H. & Wiesel, T. N., 1962, Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex, The Journal of Physiology 160(1):106–154.2 www.ncbi.nlm.nih.gov/pmc/articles/PMC1359523/.
  • Hull, J. J., 1994, A database for handwritten text recognition research, IEEE Computer Societyhttps://doi.org/10.1109/34.291440.
  • Husbands, P., et al., An Interview with Oliver Selfridge, The MIT Press, Massachusetts, USA. https://mitpress.universitypressscholarship.com/view/10.7551/mitpress/9780262083775.001.0001/upso-9780262083775-chapter-17. Accessed 14 April 2020.
  • Interview with Kunihiko Fukushima. 2015. CIS Oral History Project. IEEE.Tv https://ieeetv.ieee.org/video/interview-with-fukushima-2015. Accessed 14 Apr. 2020.
  • Khaligh-Razavi, S. M. & Kriegeskorte, N., 2014, Deep supervised, but not unsupervised, models may explain it cortical representation. PLOS Computational Biology 10(11):e1003915 doi:10.1371/journal.pcbi.1003915.
  • Krizhevsky, A., et al., 2017, ImageNet classification with deep convolutional neural networks. Association for Computing Machinery https://doi.org/10.1145/3065386.
  • LeCun,Y., et al., 1989, Backpropagation applied to handwritten zip code recognition. Neural Computation 1(4):541–51 doi:10.1162/neco.1989.1.4.541.
  • Papert, S. A., 1966, The summer vision project. https://dspace.mit.edu/handle/1721.1/6125.
  • Squire, L. R., editor, 1998, The History of Neuroscience in Autobiography, Volume 1, Academic Press, Massachusetts, USA.
  • Uhr, L., 1963, Pattern recognition computers as models for form perception. Psychological Bulletin 60:40–73 doi:10.1037/h0048029.

الفصل السابع: فك الشفرة العصبية

  • Barlow, H., 2001, Redundancy reduction revisited, Network (Bristol, England) 12(3):241–53.
  • Barlow, H. B., 2012, Possible principles underlying the transformations of sensory messages. Sensory Communication, edited by Walter A. Rosenblith, The MIT Press, Massachusetts, USA. 216–34 doi:10.7551/mitpress/9780262518420.003.0013.
  • Barlow, H. B., 1972, Single units and sensation: a neuron doctrine for perceptual psychology? Perception 1(4):371–94 doi:10.1068/p010371.
  • Engl, E. & Attwell, D., 2015, Non-signalling energy use in the brain. The Journal of Physiology 593(16):3417–29 doi:10.1113/jphysiol.2014.282517.
  • Fairhall, A. L., et al., 2001, Efficiency and ambiguity in an adaptive neural code. Nature 412(6849):787–92 doi:10.1038/35090500.
  • Foster, M., 1870, The velocity of thought, Nature doi:10.1038/002002a0. Accessed 14 April 2020.
  • Gerovitch, S., 2004, From Newspeak to Cyberspeak: A History of Soviet Cybernetics, MIT Press, Massachusetts, USA.
  • Gross, C. G., 2002, Genealogy of the ‘grandmother cell’, The Neuroscientist: A Review Journal Bringing Neurobiology, Neurology and Psychiatry 8(5):512–18 doi:10.1177/107385802237175.
  • Hodgkin, A., 1979, Edgar Douglas Adrian, Baron Adrian of Cambridge, 30 November 1889–4 August 1977, Biographical Memoirs of Fellows of the Royal Society, Royal Society, Great Britain. 25:1–73 doi:10.1098/rsbm.1979.0002.
  • Horgan, J., 2017, Profile of claude shannon, inventor of information theory. Scientific American Blog Network https://blogs.scientificamerican.com/cross-check/profile-of-claude-shannon-inventor-of-information-theory. Accessed 14 April 2020.
  • Husbands, P., et al., 2008, An interview with Horace Barlow. The MIT Press https://mitpress.universitypressscholarship.com/view/10.7551/mitpress/9780262083775.001.0001/upso-9780262083775-chapter-18. Accessed 14 April 2020.
  • Joris, P. X., et al., 1998, Coincidence detection in the auditory system: 50 years after Jeffress, Neuron 21(6):1235–38 doi:10.1016/s0896-6273(00)80643-1.
  • Lewicki, M. S., 2002, Efficient coding of natural sounds, Nature Neuroscience 5(4):356–63 doi:10.1038/nn831.
  • Perkel, D. H., 1968, Neural coding: a report based on an nrp work session organized by Theodore Holmes bullock and held on January 21–23, 1968, Neurosciences Research Program.
  • Smeds, L., et al., 2019, Paradoxical rules of spike train decoding revealed at the sensitivity limit of vision, Neuron 104(3):576–587. e11 doi:10.1016/j.neuron.2019.08.005.
  • Stein, R. B., 1967, The information capacity of nerve cells using a frequency code, Biophysical Journal 7(6):797–826 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1368193.
  • The Hospital Nursing Supplement 1892. The Hospital 12(309):153–60 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5281805.
  • Von Foerster, H., 2013, The Beginning of Heaven and Earth Has No Name: Seven Days with Second-Order Cybernetics, Fordham University Press, New York, USA.

الفصل الثامن: الحركة بأبعاد محدودة

  • Ashe, J., 2005, What is Coded in the Primary Motor Cortex? Motor Cortex in Voluntary Movements: A Distributed System for Distributed Functions, CRC Press, Massachusetts, USA doi:10.1201/9780203503584.ch5.
  • Carr, L., 2012, The neural rhythms that move your body, The Atlantic, www.theatlantic.com/health/archive/2012/06/the-neural-rhythms-that-move-your-body/258094.
  • Churchland, M. M., et al., 2010, Cortical preparatory activity: representation of movement or first cog in a dynamical machine? 68(3):387–400 doi:10.1016/j.neuron.2010.09.015.
  • Clar, S. A & Cianca, J. C., 1998, Intracranial tumour masquerading as cervical radiculopathy: a case study, Archives of Physical Medicine and Rehabilitation 79(10):1301–02 doi:10.1016/S0003-9993(98)90279-9.
  • Evarts, E. V., 1968, Relation of pyramidal tract activity to force exerted during voluntary movement, Journal of Neurophysiology 31(1):14–27 doi:10.1152/jn.1968.31.1.14.
  • Ferrier, D., 1876, The Functions of the Brain, Smith, Elder & Co, London, archive.org/details/functionsofbrain1876ferr.
  • Fetz, E. E., 1992, Are movement parameters recognizably coded in the activity of single neurons? Behavioral and Brain Sciences 15(4):679–90.
  • Finger, S., et al., 2009, History of Neurology, Elsevier, Amsterdam, Netherlands.
  • Georgopoulos, A. P., 1998, Interview with Apostolos P. Georgopoulos, Journal of Cognitive Neuroscience 10(5):657–61 doi:10.1162/089892998562951.
  • Kalaska, J. F., 2009, From intention to action: motor cortex and the control of reaching movements, Advances in Experimental Medicine and Biology 629:139–78 doi:10.1007/978-0-387-77064-2-8.
  • Kaufman, M. T., et al., 2014, Cortical Activity in the null space: permitting preparation without movement, Nature Neuroscience 17(3):440–48 doi:10.1038/nn.3643.
  • Rioch, D. M., 1938, Certain aspects of the behavior of decorticate cats, Psychiatry 1(3):339–45 doi:10.1080/00332747.1938.11022202.
  • Shenoy, K. V., et al., 2013, Cortical control of arm movements: a dynamical systems perspective, Annual Review of Neuroscience 36:337–59 doi:10.1146/annurev-neuro-062111-150509.
  • Squire, L. R., editor, 2009, The History of Neuroscience in Autobiography. Volume 6, Oxford University Press, USA.
  • Taylor, C. S. R. & Gross, C. G., 2003, Twitches versus movements: a story of motor cortex, The Neuroscientist: A Review Journal Bringing Neurobiology, Neurology and Psychiatry 9(5):332–42 doi:10.1177/1073858403257037.
  • Venkataramanan, M., 2015, A chip in your brain can control a robotic arm, Welcome to BrainGate, Wired UK www.wired.co.uk/article/braingate.
  • Whishaw, I. Q. & Kolb, Bryan, 1983, Can male decorticate rats copulate? Behavioral Neuroscience 97(2):270–79 doi:10.1037/0735-7044.97.2.270.
  • Wickens, A. P., 2014, A History of the Brain: From Stone Age Surgery to Modern Neuroscience, Psychology Press, East Sussex, UK.

الفصل التاسع: من البنية إلى الوظيفة

  • Fornito, A., et al. editors, 2016, Chapter 8-Motifs, Small Worlds, and Network Economy, Fundamentals of Brain Network Analysis, Academic Press, London, UK., 257–301 doi:10.1016/B978-0-12-407908-3.00008-X.
  • Garcia-Lopez, P., et al., 2010, The histological slides and drawings of Cajal, Frontiers in Neuroanatomy 4 doi:10.3389/neuro.05.009.2010.
  • Griffa, A., et al., 2013, Structural connectomics in brain diseases, NeuroImage 80: 515–26 doi:10.1016/j.neuroimage.2013.04.056.
  • Hagmann, P., et al., 2007, Mapping human whole-brain structural networks with diffusion MRI. PLOS ONE 2(7):e597 doi:10.1371/journal.pone.0000597.
  • Heuvel, M. P. van den, & Sporns, Olaf, 2013, Network hubs in the human brain, Trends in Cognitive Sciences 17(12):683–96 doi:10.1016/j.tics.2013.09.012.
  • Humphries, M. D., et al., 2006, The brainstem reticular formation is a small-world, not scale-free, network, Biological Sciences 273(1585):503–11 doi:10.1098/rspb.2005.3354.
  • Marder, E. & Taylor, A. L., 2011, Multiple models to capture the variability in biological neurons and networks, Nature Neuroscience 14(2):133–38 doi:10.1038/nn.2735.
  • Milgram, Stanley, 1967, The small world problem, Psychology Today 2:60–67.
  • Mohajerani, M. H. & Cherubini, E., 2006, Role of giant depolarizing potentials in shaping synaptic currents in the developing hippocampus, Critical Reviews in Neurobiology 18(1–2):13–23 doi:10.1615/critrevneurobiol.v18.i1-2.30.
  • Morrison, K. & Curto, C., 2019, Chapter 8-Predicting Neural Network Dynamics via Graphical Analysis, Algebraic and Combinatorial Computational Biology, edited by Robeva, Raina & Macauley, M, Academic Press, London, UK., 241–77 doi:10.1016/B978-0-12-814066-6.00008-8.
  • Muldoon, S. F., et al., 2016, Stimulation-based control of dynamic brain networks, PLOS Computational Biology 12(9):e1005076 doi:10.1371/journal.pcbi.1005076.
  • Navlakha, S., et al., 2018, Network design and the brain, Trends in Cognitive Sciences, 22:64–78 doi:10.1016/j.tics.2017.09.012.
  • Servick, K., 2019, This physicist is trying to make sense of the brain’s tangled networks, Science/AAAS, www.sciencemag.org/news/2019/04/physicist-trying-make-sense-brain-s-tangled-networks.
  • Sporns, Olaf, Chialvo, Dante R., et al., 2004, Organization, development and function of complex brain networks, Trends in Cognitive Sciences 8(9):418–25 doi:10.1016/j.tics.2004.07.008.
  • Sporns, Olaf, Tononi, Giulio, et al., 2005, The human connectome: a structural description of the human brain, PLOS Computational Biology 1(4):e42 doi:10.1371/journal.pcbi.0010042.
  • Squire, L. R. & Albright, T. D. editors, 2008, The History of Neuroscience in Autobiography, Volume 9, Oxford University Press, New York, USA.
  • Squire, L. R. & Albright, T. D. editors, 2008, The History of Neuroscience in Autobiography Volume 10, Oxford University Press, New York, USA.
  • Tau, G. Z. & Peterson, B. S., 2010, Normal development of brain circuits, Neuropsychopharmacology 35(1):147–68 doi:10.1038/npp.2009.115.
  • Towlson, E. K., et al, 2013, The rich club of the C. Elegans neuronal connectome. The Journal of Neuroscience 33(15):6380–87 doi:10.1523/JNEUROSCI.3784-12.2013.
  • Watts, D. J. & Strogatz, S. H., 1998, Collective dynamics of ‘small-world’ networks, Nature 393(6684):440–42 doi:10.1038/30918.

الفصل العاشر: اتخاذ قرارات عقلانية

  • Stix, G., 2014, A conversation with Dora Angelaki, Cold Spring Harbor Symposia on Quantitative Biology 79:255–57 doi:10.1101/sqb.2014.79.02.
  • Adams, W. J., et al., 2004, Experience can change the ‘light-from-above’ prior, Nature Neuroscience 7(10):1057–58 doi:10.1038/nn1312.
  • Aitchison, L., et al., 2015, Doubly Bayesian analysis of confidence in perceptual decision-making, PLoS Computational Biology 11(10) doi:10.1371/journal.pcbi.1004519.
  • Anderson, J. R., 1991, Is human cognition adaptive? Behavioral and Brain Sciences 14(3):471–85 doi:10.1017/S0140525X00070801.
  • Bowers, J. S. & Davis, C. J., 2012, Bayesian Just-so Stories in psychology and neuroscience, Psychological Bulletin 138(3):389–414 doi:10.1037/a0026450.
  • Cardano, G., 2002, The Book of My Life, New York Review Books, USA.
  • Curry, R. E., 1972, A Bayesian model for visual space perception, NASSP 281:187 https://ui.adsabs.harvard.edu/abs/1972NASSP.281…187C/abstract.
  • Fetsch, C. R., et al., 2009, Dynamic reweighting of visual and vestibular cues during self-motion perception, Journal of Neuroscience 29(49):15601–12 doi:10.1523/JNEUROSCI.2574-09.2009.
  • Fisher, R. A. & Russell, E. J., 1922, On the mathematical foundations of theoretical statistics, Philosophical Transactions of the Royal Society of London, Series A, Containing Papers of a Mathematical or Physical Character 222(594–604):309–68 doi:10.1098/rsta.1922.0009.
  • Gillies, D. A., 1987, Was Bayes a Bayesian? Historia Mathematica 14(4):325–46 doi:10.1016/0315-0860(87)90065-6.
  • Gorroochurn, P., 2016, Classic Topics on the History of Modern Mathematical Statistics: From Laplace to More Recent Times, John Wiley & Sons, New Jersey, USA.
  • Helmholtz, H. von & Southall, J. P. C., 2005, Treatise on Physiological Optics, Dover Publications, New York, USA.
  • Jaynes, E. T., 2003, Probability Theory: The Logic of Science: Principles and Elementary Applications Vol 1, Edited by G. Larry Bretthorst, Cambridge University Press, New York, USA.
  • Koenigsberger, L., 1906, Hermann von Helmholtz, Clarendon Press, Oxford, UK.
  • Mamassian, P., 2008, Ambiguities and conventions in the perception of visual art, Vision Research 48(20):2143–53 doi:10.1016/j. visres.2008.06.010.
  • Moreno-Bote, R., et al., 2011, Bayesian sampling in visual perception, Proceedings of the National Academy of Sciences 108(30):12491–96 doi:10.1073/pnas.1101430108.
  • Seriès, P. & Seitz, A. R., 2013, Learning what to expect (in visual perception), Frontiers in Human Neuroscience 7:668 doi:10.3389/fnhum.2013.00668.
  • Stigler, S. M., 1982, Thomas Bayes’s Bayesian inference, Journal of the Royal Statistical Society, Series A (General) 145(2):250–58 doi:10.2307/2981538. JSTOR.
  • Vilares, I. & Kording, K., 2011, Bayesian Models: the structure of the world, uncertainty, behavior, and the brain, Annals of the New York Academy of Sciences 1224(1):22–39 doi:10.1111/j.1749-6632.2011.05965.x.
  • Weiss, Y., et al., 2002, Motion illusions as optimal percepts, Nature Neuroscience 5(6):598–604 doi:10.1038/nn0602-858.

الفصل الحادي عشر: كيف توجه المكافآت الأفعال

  • Bellman, R., 1984, Eye of the Hurricane, World Scientific, Singapore.
  • Bellman, R. E., 1954, The theory of dynamic programming www.rand.org/pubs/papers/P550.html.
  • Bergen, M., 2016, Google has found a business model for its most advanced artificial intelligence. Vox www.vox.com/2016/7/19/12231776/google-energy-deepmind-ai-data-centers.
  • Mnih,V., et al., 2013, Playing Atari with deep reinforcement learning. ArXiv:1312.5602 [Cs], http://arxiv.org/abs/1312.5602.
  • Redish, A. D., 2004, Addiction as a computational process gone awry, Science (New York) 306(5703):1944–47 doi:10.1126/science.1102384.
  • Rescorla, R. A. & Wagner, A., 1972, A theory of Pavlovian Conditioning: variations in the effectiveness of reinforcement and nonreinforcement. Classical Conditioning II: Current Research and Theory 2
  • Schultz, W., Dayan, P., et al., 1997, A neural substrate of prediction and reward, Science (New York) 275(5306):1593–99 doi:10.1126/science.275.5306.1593.
  • Schultz, W., Apicella, P., et al., 1993, Responses of monkey dopamine neurons to reward and conditioned stimuli during successive steps of learning a delayed response task, The Journal of Neuroscience: The Official Journal of the Society for Neuroscience 13(3):900–13.
  • Sejnowski, T. J., 2018, The Deep Learning Revolution, MIT Press, Massachusetts, USA.
  • Specter, Michael, 2014, Drool, The New Yorker, www.newyorker.com/magazine/2014/11/24/drool. Accessed 14 April 2020.
  • Story, G. W., et al., 2014, Does temporal discounting explain unhealthy behavior? a systematic review and reinforcement learning perspective, Frontiers in Behavioral Neuroscience 8 doi:10.3389/fnbeh.2014.00076.
  • Sutton, R. S., 1988, Learning to predict by the methods of temporal differences, Machine Learning 3(1):9–44 doi:10.1007/BF00115009.

الفصل الثاني عشر: النظريات الموحدة العظمى الخاصة بالدماغ

  • Anderson, M. L. & Chemero, T., 2013, The Problem with brain guts: conflation of different senses of ‘prediction’ threatens metaphysical disaster, The Behavioral and Brain Sciences 36(3):204–05 doi:10.1017/S0140525X1200221X.
  • Buxhoeveden, D. P. & Casanova, Manuel F., 2002, The minicolumn hypothesis in neuroscience, Brain 125(5):935–51 doi:10.1093/brain/awf110.
  • Clark, J., 2014, Meet the man building an ai that mimics our neocortex – and could kill off neural networks, www.theregister.co.uk/2014/03/29/hawkins_ai_feature.
  • Eliasmith, C., et al., 2012, A large-scale model of the functioning brain, Science 338(6111):1202–05 doi:10.1126/science.1225266.
  • Fridman, L., 2019, Jeff Hawkins: Thousand Brains Theory of intelligence, https://lexfridman.com/jeff-hawkins. Accessed 14 Apr. 2020.
  • Friston, K., 2019, A free energy principle for a particular physics, ArXiv:1906.10184 [q-Bio] http://arxiv.org/abs/1906.10184.
  • Friston, K., 2010, The free-energy principle: a unified brain theory? Nature Reviews Neuroscience 11(2):127–38 doi:10.1038/nrn2787.
  • Friston, K., Fortier, M. & Friedman, D. A., 2018, Of woodlice and men: A Bayesian account of cognition, life and consciousness, An interview with Karl Friston, ALIUS Bulletin, 2:17–43.
  • Hawkins, J., et al., 2019, A Framework for intelligence and cortical function based on grid cells in the neocortex, Frontiers in Neural Circuits 12 doi:10.3389/fncir.2018.00121.
  • Heilbron, M. & Chait, M., 2018, Great expectations: is there evidence for predictive coding in auditory cortex? Neuroscience 389:54–73 doi:10.1016/j.neuroscience.2017.07.061.
  • Metz, C., 2018, Jeff Hawkins is finally ready to explain his brain research, The New York Times, www.nytimes.com/2018/10/14/technology/jeff-hawkins-brain-research.html.
  • Michel, M., et al., 2018, An informal internet survey on the current state of consciousness science, Frontiers in Psychology 9 doi:10.3389/fpsyg.2018.02134.
  • Nanopoulos, D. V., 1979, Protons are not forever, High-Energy Physics in the Einstein Centennial Year, edited by Arnold Perlmutter et al., Springer US., 91–114 doi:10.1007/978-1-4613-3024-0_4.
  • Raviv, S., ‘The Genius Neuroscientist Who Might Hold the Key to True AI,’ Wired, https://www.wired.com/story/karl-friston-free-energy-principle-artificial-intelligence/, Accessed 14 Apr. 2020.
  • Rummell, B. P., et al., 2016, Attenuation of responses to self-generated sounds in auditory cortical neurons, Journal of Neuroscience 36(47):12010–26 doi:10.1523/JNEUROSCI.1564-16.2016.
  • Simonite, T., 2015, IBM Tests mobile computing pioneer’s controversial brain algorithms, MIT Technology Review, www.technologyreview.com/2015/04/08/11480/ibm-tests-mobile-computing-pioneers-controversial-brain-algorithms.
  • Tononi, G., et al., 2016, Integrated information theory: from consciousness to its physical substrate, Nature Reviews Neuroscience 17(7):450–61 doi:10.1038/nrn.2016.44.
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