Neural Network Driven Artificial Intelligence: Decision Making Based On Fuzzy Logic

Bahman Zohuri and Masoud Moghaddam
Galaxy Advanced Engineering, San Mateo, CA, USA

Series: Computer Science, Technology and Applications, Mathematics Research Developments
BISAC: COM014000

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With today’s growing and overloading volume of information, it is becoming tremendously difficult to analyze the huge amounts of data that contain this information. It makes it very strenuous and inconvenient to introduce an appropriate methodology of decision-making fast enough to the point that it can be considered as real-time. The demand for real-time processing information and related data – both structured and unstructured – is on the rise and consequently makes it harder and harder to implement correct decision making at the enterprise level to keep the organization robust and resilient against either manmade threats or natural disasters.

Neural networking and fuzzy systems combined show how Artificial Intelligence (AI) can be driven by these combinations as a trainable system that is more dynamic than static when it comes to machine and deep learning language to deal with both adversary and friendly events in real-time. Dynamic systems of AI that are built around such an innovative approach allows the robots of the future to be more adaptive with mechanisms such as principle adoption, self-organization, and the convergence of global stability from the viewpoint of business and intelligence security needed in today’s cyber world.

To deal with uncertainty, vagueness, and imprecision, Lofti A. Zadeh introduced fuzzy sets and fuzzy logic. In the present book, fuzzy classification is applied to extend portfolio analysis, scoring methods, customer segmentation and performance measurement, and thus improves managerial decisions. As an integral part of the book, case studies show how, fuzzy classification – with its query facilities – can extend customer equity, enable mass customization, and refine marketing campaigns

This book shows interoperability between the two sciences/techniques show how:

1) To utilize fuzzy theory of the first and second kind to an adaptive control; and
2) How, to invent a structured fuzzy system and robots of future, with unsupervised neural network techniques to face an unstructured world of big data, and unpredictable global events all in real-time

An important aspect of this approach is to examine biological neural systems and study how artificial neural networks are, how they are based on them, and how they are driven by them as well. Key areas discussed include:

1) Structural diversity;
2) Temporal lobe;
3) Origins of artificial neural systems;
4) Brain structure and function;
5) Biological nerve cells;
6) Synapses;
7) Random and fixed positions in the brain’s neural networks; and
8) How biological systems really compare to computational neural networks. (Imprint: Nova)

Preface

Acknowledgement

About the Authors

Chapter 1. Knowledge is Power

Chapter 2. Fuzzy Logic Concept

Chapter 3. Neural Network Concept

Chapter 4. Structured and Unstructured Data Processing

Chapter 5. Artificial Intelligence Systems and Robots of Tomorrow

Chapter 6. Computational Neuroscience

Chapter 7. Cable and Compartmental Models of Dendritic Trees

Chapter 8. Dynamics of Cerebral Cortical Networks

Chapter 9. Neural Networks and Fuzzy Logic Systems

Chapter 10. The Extraordinary Future of Artificial Intelligence

Appendix. Fluorescence Microscopy

Index

Chapter 1

[1] Bahman Zohuri and Masoud Moghaddam, Business Resilience System (BRS): Driven Through Boolean, Fuzzy Logics and Cloud Computation: Real and Near Real Time Analysis and Decision Making System 1st ed. 2017 Edition.
[2] David Derbyshire for Mail Online http://www.dailymail.co.uk/sciencetech/article-1355892/Each-person-inundated-174-newspapers-worth-information-EVERY-DAY. html.
[3] Lisa Quast, 'Why Knowledge Management Is Important To The Success
Of Your Company' http://www.forbes.com/sites/lisaquast/2012/08/20/why-knowledge-management-is-important-to-the-success-of-your-company/#414146855e1d.
[4] David Garvin,” Learning in Action: A Guide to Putting the Learning Organization to Work,” Harvard Business Review Press (March 25, 2003).
[5] Pamela Babcock, “Shedding Light on Knowledge Management,” https://www.shrm. org/hr-today/news/hr-magazine/Pages/0504covstory.aspx.

Chapter 2

[1] B. Zohuri and M. Moghaddam, Business Resilience System (BRS): Driven Through Boolean, Fuzzy Logics and Cloud Computation: Real and Near Real Time Analysis and Decision Making System 1st ed. 2017 Edition.
[2] Exis, LLC http://www.fuzzy-logic.com/.
[3] Jose Galindo, Handbook of Research on Fuzzy Information Processing in Databases 1st Edition, Published by Information Science Reference; (May 30, 2008).
[4] Srdjan Škrbić, Miloš Racković, Aleksandar Takači, Prioritized fuzzy logic based information processing in relational databases, Elsevier, Knowledge-Based Systems, Volume 38, January 2013, Pages 62-73.
[5] L. A. Zadeh, “The Concept of a Linguistic Variable and Its Application to Approximate Reasoning–1,” Information Sciences, vol. 8, pp. 199–249, 1975.
[6] J. M. Mendel, Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions, Prentice-Hall, Upper-Saddle River, NJ, 2001.
[7] Chengdong Li, Li Wang, Zixiang Ding, and Guiqing Zhang, School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan China.
[8] Jang, Sun, Mizutani (1997) – Neuro-Fuzzy and Soft Computing – Prentice Hall, pp 335–368.
[9] D. Rumelhart, G. Hinton, and Williams R., “Learning representations by back-propagating errors,” Nature, vol. 323, pp. 533-536, 1986.
[10] Kurt Hornick, Maxwell Stinchcombe, and Halbert White, “Multilayer feed forward networks are universal approximators,” Neural Networks, vol. 2, pp. 359-366, 1989.
[11] Michael D. Richard and Richard P. Lippmann, “Neural network classifiers estimate Bayesian a posteriori probabilities,” Neural Computation, vol. 3, no. 4, pp. 461-483, 1991.
[12] E.B. Baum and D. Haussler, “What size net gives valid generalization?,” Neural Computation, vol. 1, no. 1, pp. 151-160, 1989.
[13] Belur V. Dasarathy, Nearest neighbor (NN) norms: NN pattern classification techniques, IEEE Computer Society Press; IEEE Computer Society Press Tutorial, Los Alamitos, Calif. Washington, 1991.
[14] T.M. Cover and P.E. Hart, “Nearest neighbor pattern classification,” IEEE Transactions on Information Theory, vol. IT-13, pp. 21-27, 1967.
[15] David Poole and Alan Mackworth, “Artificial Intelligence, Foundation of Computational Agents,” Published by Cambridge University Press, 2010.
[16] Rebane, G. and Pearl, J., “The Recovery of Causal Poly-trees from Statistical Data,” Proceedings, 3rd Workshop on Uncertainty in AI, (Seattle, WA) pages 222–228, 1987.
[17] Friedman, Nir; Linial, Michal; Nachman, Iftach; Pe'er, Dana (August 2000). “Using Bayesian Networks to Analyze Expression Data.” Journal of Computational Biology. 7 (3-4): 601–620. doi:10.1089/106652700750050961. PMID 11108481. Retrieved 24 February 2015.
[18] Cussens, James (2011). “Bayesian network learning with cutting planes.” Proceedings of the 27th Conference Annual Conference on Uncertainty in Artificial Intelligence: 153–160.
[19] Walley, P.: Statistical Reasoning with Imprecise Probabilities. Chapman and Hall, London (1991).
[20] Norman Fenton and Martin Neil, “MANAGING RISK IN THE MODERN WORLD Applications of Bayesian Networks,” A Knowledge Transfer Report from the London Mathematical Society and the Knowledge Transfer Network for Industrial Mathematics, London Mathematical Society De Morgan House, 57/58 Russell Square London WC1B 4HS, November 2007.
[21] Klir, G. J., and T. A. Folger, 1988: Fuzzy Sets, Uncertainty and Information, Prentice Hall, 355 pp.
[22] Kosko, B., 1992: Neural Networks and Systems: A Dynamical Systems Approach to Machine Intelligence, Prentice Hall, 449 pp.

Chapter 3

[1] David Poole and Alan Mackworth, “Artificial Intelligence, Foundation of Computational Agents,” Published by Cambridge University Press, 2010.
[2] McCulloch and Pitts, “A logical calculus of the ideas immanent in nervous activity” in the Bulletin of Mathematical Biophysics 5:115-133.
[3] Hassoun, Mohamad H. “Fundamentals of Artificial Neural Networks.” The MIT Press, Cambridge, MA, 1995.
[4] Zurada, Jacek M. “Introduction to Artificial Neural System.” West Publishing Company, St. Paul, MN, 1992.
[5] Bose, N. K. and Liang, P. “Neural Network Fundamentals with Graphs, Algorithms, and Applications.” McGraw-Hill, New York, NY, 1996.
[6] Haykin, Simon. “Neural Networks: A Comprehensive Foundation, second edition.” Prentice-Hall, Upper Saddle River, NJ, 1999.
[7] Lerma Sanchez, Leonardo Octavio. “A Neural Network Approach to a Dimensionality Reduction Problem,” Master's thesis, The University of Texas at El Paso, El Paso, TX, 1991.
[8] Terrence J. Sejnowski and Charles R. Rosenberg, “NETtalk: a parallel network that learns to read aloud” The Johns Hopkins University Electrical Engineering and Computer Science Technical Report JHU/EECS-86/01, 32 pp.
[9] Lerma Sanchez, Leonardo Octavio. “A Neural Network Approach to a Dimensionality Reduction Problem.”. Master's thesis, The University of Texas at El Paso, El Paso, TX, 1991.
[10] Dayhoff, Judith E. “Neural Network Architecture: An Introduction.” Van Nostrand Reinhold, New York, NY, 1990.
[11] D. Rumelhart, G. Hinton, and Williams R., “Learning representations by back-propagating errors,” Nature, vol. 323, pp. 533-536, 1986.
[12] Kurt Hornick, Maxwell Stinchcombe, and Halbert White, “Multilayer feed forward networks are universal approximators,” Neural Networks, vol. 2, pp. 359-366, 1989.
[13] Bahman Zohuri and Masoud Moghaddam “Business Resilience System (BRS): Driven Through Boolean, Fuzzy Logics and Cloud Computation: Real and Near Real Time Analysis and Decision Making System” 1st ed. 2017, Springer Publishing Company.
[14] E.B. Baum and D. Haussler, “What size net gives valid generalization?,” Neural Computation, vol. 1, no. 1, pp. 151-160, 1989.
[15] Bach, M. “The Design of the UNIX Operating System.” Prentice-Hall, Englewood Cliffs, NJ, 1986.
[16] Beale, R. and Jackson, T. “Neural Computing: An Introduction.” Hilger, Philadelphia, PA, 1991.
[17] Velasquez, Guillermo. “A Distributed Approach to a Neural Network Simulation Program.” Master's thesis, The University of Texas at El Paso, El Paso, TX, 1998.
[18] Dayhoff, Judith E. “Neural Network Architecture: An Introduction.” Van Nostrand Reinhold, New York, NY, 1990.
[19] Gurney, Kevin. “An Introduction to Neural Networks.” University of Sheffield Press, London, UK, 1997.
[20] Haykin, Simon. “Neural Networks: A Comprehensive Foundation, second edition.” Prentice-Hall, Upper Saddle River, NJ, 1999.
[21] Robert Krulwich (2001-04-17). Cracking the Code of Life (Television Show). PBS.
[22] Economic Impact of the Human Genome Project – Battelle” (PDF format). Retrieved 1 August 2013.
[23] Human Genome Project Completion: Frequently Asked Questions.” genome.gov.
[24] Arbib, Michael A. “Brains, Machines, and Mathematics: Second Edition.” Springer-Verlag, New York, NY, 1987.
[25] Widrow, B., and Hoff, M. E., Jr., 1960, Adaptive switching circuits, in 1960 IRE WESCON Convention Record, Part 4, New York: IRE, pp. 96–104.
[26] M. L. Minsky and S. A. Papert, Perceptions: An Introduction to Computational Geometry expanded edition, The MIT Press, Cambridge, MA, 1988.
[27] Rumelhart, D. E., Hinton, G. E., and Williams, R. J., 1986, Learning internal representations by error propagation, in Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1, Foundations, (D. E. Rumelhart, J. L. McClelland, and PDP Research Group, Eds.), Cambridge, MA: MIT Press, chap. 8.
[28] Yates, J. S. (2004). Doing Social Science Research. London, Sage Publications in association with the Open University Press.

Chapter 4

[1] http://www.dataversity.net/expanding-master-data-management-with-big-data/.

Chapter 5

[1] David Leech Anderson, “Humans using machines, humans as machines: Implications for teaching and learning,” Humanities and Technology Review Fall 2008, Volume 27. Pages 1-23 ISSN 1076-7908.
[2] Davis Leech Anderson, http://www.mind.ilstu.edu/curriculum/modOverview.php? modGUI=239.
[3] Bahman Zohuri, Directed Energy Weapons: Physics of High Energy Lasers (HEL) 1st ed. 2016 Edition, Springer Publishing Company.
[4] David L. Anderson, http://www.mind.ilstu.edu/curriculum/functionalism_intro/ functionalism_intro.php?modGUI=44&compGUI=1945&itemGUI=3403.
[5] http://www.mind.ilstu.edu/curriculum/nature_of_computers/computer-types.php?modGUI=196&compGUI=1747&itemGUI=3016.
[6] Gears of war: When mechanical analog computers ruled the waves Ars Technica.
[7] E. Beggs & J. Tucker (2006). ‘Embedding infinitely parallel computation in Newtonian kinematics.’ Applied mathematics and computation

178 (1):25–43.
[8] E. Beggs & J. Tucker (2007). ‘Can Newtonian systems bounded in space, time, mass and energy compute all functions?’. Theoretical Computer Science

371 (1):4–19.
[9] O. Bournez & M. Cosnard (1995). ‘On the computational power and super-Turing capabilities of dynamical systems’. Tech. Rep. 95-30, Ecole Normal Superior de Lyons.
[10] Bahman Zohuri and Masoud Moghaddam, Business Resilience System (BRS): Driven Through Boolean, Fuzzy Logics and Cloud Computation: Real and Near Real Time Analysis and Decision Making System 1st ed. 2017 Edition.
[11] Mohamad H. Hassoun, Fundamentals of Artificial Neural Networks, MIT Press, Massachusetts Institute of Technology, Cambridge, Massachusetts, First Edition.
[12] Andrew Friedman, “The Fundamental Distinction between Brains and Turning Machines,” published paper with neuroscience special report, pp 28-33, 2002.
[13] Fei Su of Intel Corporation and Krishnendu Chakrabarty of Duke University, “High-Level Synthesis of Digital Microfluidic Biochips.” Duke University.
[14] M. Schena, D. Shalon, R. W. Davis, and P. O. Brown, “Quantitative monitoring of gene expression patterns with a complementary DNA microarray,” Science 270, pp. 467–470, 1995.
[15] G. MacBeath, A. N. Koehler, and S. L. Schreiber, “Printing small molecules as microarrays and detecting protein-ligand interactions en masse,” J. Am. Chem. Soc. 121, pp. 7967–7968, 1999.
[16] Jitesh Dundas and David Chik, “ Implementing Human-like Intuition Mechanism in Artificial Intelligence” Edencore Technologies Ltd. Row House 6, Opp Ambo Vihar, Tirupati Nagar-II, Virar (w), Thane-401303, India.
[17] Kahneman D. (2003) A Perspective on Judgment and Choice. American Psychologist, 58(9), 697-720.
[18] John McCarthy. Mathematical Logic in Artificial Intelligence. Daedalus. Vol. 117, No. 1. Artificial Intelligence (Winter, 1988), pp. 297-311.MIT Press on behalf of American Academy of Arts & Sciences. Online: - http://www.jstor.org/stable/20025149.
[19] Aaron Sloman. INTERACTIONS BETWEEN PHILOSOPHY AND ARTIFICIAL INTELLIGENCE: The Role of Intuition and Non-Logical Reasoning in Intelligence. Artificial Intelligence 2 (1971), 209-225.
[20] Jitesh Dundas and David Chik. IBSEAD: - A Self-Evolving Self-Obsessed Learning Algorithm for Machine Learning. IJCSET (URL: - http://ijcset.excelingtech.co.uk/). Volume 1. Issue 4. No 48. December, 2010.
[21] Jitesh Dundas. Law of Connectivity in Machine Learning. International Journal of Simulation- Systems, Science and Technology - IJSSST (URL: - http://www.ijssst.info/). Vol. 11, No. 5. Dec 2010. (ISSN: 1473-804 x Online) and (ISSN: 1473-8031 Print). UK.
[22] Christopher M. Bishop, “Patten Recognition and Machine Learning,” Published by Springer Publishing Company, 2006.
[23] http://www.bbc.com/news/technology-30290540.
[24] http://searchcio.techtarget.com/news/4500260142/Despite-progress-the-future-of-AI-will-require-human-assistance.
[25] http://www.bbc.com/news/technology-30290540.
[26] http://searchcio.techtarget.com/news/4500260142/Despite-progress-the-future-of-AI-will-require-human-assistance.
[27] http://www.247-inc.com/company/blog/strike-balance-chatbots-vs-humans-telecom-landscape.
[28] http://venturebeat.com/2016/07/23/chatbots-will-take-over-customer-service-not-so-fast/.

Chapter 6

[1] Eeckman, F. H. and Bower, J. M. (eds.) (1993). Computation and Neural Systems, Kluwer Academic Publishers, Boston.
[2] Bower, J. M. (1992), Modeling the nervous system, Trends Neurosci. 15: 411–412.
[3] Bahman Zohuri and Masoud Moghaddam, Business Resilience System (BRS): Driven Through Boolean, Fuzzy Logics and Cloud Computation: Real and Near Real Time Analysis and Decision Making System 1st ed. 2017 Edition.
[4] Robert Hecht-Nielsen, Neurocomputing, Addison-Wesley Publishing Company, 1989.
[5] Rosenblatt, F., “Principle of Neurodynamics,” Spartan Book, Washington DE, 1961.
[6] Rosenblatt, F., “The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain,” Psychol. Rev.,

65, 386-408, 1958.
[7] Steinbuch, K., “Automat und Mensch,” Second Edition, Springer-Verlag, Heidelberg, 1963.
[8] Bahman Zohuri, Dimensional Analysis and Self-Similarity Methods for Engineers and Scientists 2015th Edition, Springer Publishing Company.
[9] Stamatios V. Kartalopoulos, “Understanding Neural Networks and Fuzzy Logic, Basic Concepts and Applications,” IEE Press, 1996.
[10] Rall, W. (1959), Branching dendritic trees and motoneuron membrane resistivity, Exp. Neurol.

1: 491–527.
[11] De Schutter, E. and Bower, J. M. (1994a), An active membrane model of the cerebellar Purkinje cell

I. Simulation of current clamps in slice, J. Neurophysiol.

71: 375–400.
[12] De Schutter, E. and Bower, J. M. (1994b), An active membrane model of the cerebellar Purkinje cell

II. Simulation of synaptic responses, J. Neurophysiol.

71: 401–419.
[13] Rapp, M., Yarom, Y. and Segev, I. (1992). The impact of parallel fiber background activity on the cable properties of cerebellar Purkinje cells, Neural Computation 4: 518–533.
[14] Segev, I., Rinzel, J. and Shepherd, G. H. (eds) (1995). The Theoretical Foundation of Dendritic Function: Selected Papers by Wilfrid Rall with Commentaries, MIT Press, Cambridge, MA.
[15] Principles of Neural Science 4th (fourth) Edition by Kandel, Eric, Schwartz, James, Jessell.

Chapter 7

[1] Rapp, M., Yarom, Y. and Segev, I. (1992), The impact of parallel fiber background activity on the cable properties of cerebellar Purkinje cells, Neural Computation 4: 518–533.
[2] Burke R. Spinal motoneuron Neuroscience in the 21st Century: From Basic to Clinical. 1027-1062.
[3] Wilson, C. J. (1992). Dendritic morphology, inward rectification and the functional properties of Neostriatal neurons, in T. McKenna, J. Davis and S. Zornetzer (eds), Single Neuron Computation, Academic Press, San Diego, pp. 141–172.
[4] Rall, W. (1959), Branching dendritic trees and motoneuron membrane resistivity, Exp. Neurol. 1: 491–527.
[5] Rall, W. (1964), Theoretical significance of dendritic trees for neuronal input-output relations, in R. Reiss (ed.), Neuronal Theory and Modeling, Stanford University Press, Stanford, CA, pp. 73–97.
[6] Segev, I., Rinzel, J. and Shepherd, G. H. (Eds) (1995), The Theoretical Foundation of Dendritic Function: Selected Papers by Wilfrid Rall with Commentaries, MIT Press, Cambridge, MA.
[7] White, E. L. (1989). Cortical circuits: Synaptic organization of the cerebral cortex — Structure, function and theory, Birkhȧuser, Boston.
[8] Shepherd, G. M. (1990), The Synaptic Organization of the Brain, third edn, Oxford University Press, New York.
[9] Segev, I. (1995). Denritic processing, in M. A. Arbib (ed.), The Handbook of Brain Theory and Neural Networks, MIT Press, Cambridge, MA.
[10] Segev, I. and Rall, W. (1988), Computational study of an excitable dendritic spine, J. Neurophsiol. 60: 499–523.
[11] Koch, C. and Zador, T. (1993), The function of dendritic spines: Devices subserving biochemical rather than electrical compartmentalization, J. Neuroscience 13: 413–422.
[12] Koch, C. and Zador, T. (1993), The function of dendritic spines: Devices subserving biochemical rather than electrical compartmentalization, J. Neuroscience 13: 413–422.
[13] Stuart, G. J. and Sakmann, B. (1994), Active propagation of somatic action potentials into neocortical pyramidal cell dendrites, Nature 367: 69–72.
[14] Shepherd, G. M. (1990), The Synaptic Organization of the Brain, third edn, Oxford University Press, New York.
[15] Bower, J. and Beeman, D. The Book of GENESIS, Exploring Realistic Neural Models with the General Neural Simulation System, 2nd edition, 1997, Springer Publishing Company.
[16] Zohuri, B., Dimensional Analysis and Self-Similarity Methods for Engineers and Scientists Apr 16, 2015.
[17] Rall, W. (1989), Cable theory for dendritic neurons, in C. Koch and I. Segev (eds), Methods in Neuronal Modeling, MIT Press, Cambridge, MA, chapter 2, pp. 9–62.
[18] Jack, J. J. B., Noble, D. and Tsien, R. W. (1975), Electric Current Flow in Excitable cells, Calderon Press, Oxford.
[19] Rall, W. (1989), Cable theory for dendritic neurons, in C. Koch and I. Segev (eds), Methods in Neuronal Modeling, MIT Press, Cambridge, MA, chapter 2, pp. 9–62.
[20] Jackson, J. D. Classical Electrodynamics. Wiley Published by John, 1962.
[21] Abbott, L.F. (1991), Realistic synaptic inputs for model neural networks. Network, 2:245-258.
[22] Rall, W. (1969) Time constant and electrotonic length of membrane cylinders and neurons, Biophys. J. 9: 1483–1508.
[23] Rall, W (1977), Cable theory for neurons, in E. R. Kandel, J. M. Brookhardt and V. B. Mountcastle (eds), Handbook of Physiology: The Nervous System, Vol. 1, Williams and Wilkins, Baltimore, chapter 3, pp. 39–98.
[24] Rall, W (1967). Distinguishing theoretical synaptic potentials computed for different somadendritic distribution of synaptic inputs, J. Neurophysiol. 30: 1138–1168.
[25] Ranck, J. B. (1973), Studies on single neurons in dorsal hippocampal formation and septum in unrestrained rats. I. behavioral correlates and firing repertoires, Exp. Neurol. 41: 462–531.
[26] Bloomfield, S. A., Hamos, J. E. and Sherman, S. M. (1987). Passive cable properties and morphological correlates of neurones in the lateral geniculate nucleus of the cat, J. Physiol. (London) 383: 653–692.
[27] Rall, W. and Rinzel, J. (1973). Branch input resistance and steady state attenuation for input to one branch of a dendritic neuron model, Biophys. J. 13: 648–688.
[28] Rinzel, J. and Rall, W. (1974), Transient response in a dendritic neuron model for current injected at one branch, Biophys. J. 14: 759–790.
[29] Segev, I., Fleshman, J. W. and Burke, R. E. (1989), Compartmental models of complex neurons, in C. Koch and I. Segev (eds), Methods in Neuronal Modeling, MIT Press, Cambridge, MA, chapter 3, pp. 63–96.
[30] Yamada, W. M., Koch, C., and Adams, P. R. (1989), Multiple channels and calcium dynamics. In Koch, C. and Segev, I., editors, Methods in neuronal modeling: From synapses to networks, chapter 4. MIT Press, Cambridge, MA.
[31] Ekeberg, O., Wallen, P., Lansner, A., Traven, H., Brodin, L., and Grillner, S. (1991). A computer based model for realistic simulations of neural networks. Biol. Cybern., 65:81-90.
[32] Mel, B. W. (1994), Information processing in dendritic trees. Neural Comput., 6(1031-1085).
[33] Gabbiani, F., Midtgaard, J., and Knoepfl, T. (1994), Synaptic integration in a model of cerebellar granule cells. J. Neurophysiol., 72:999-1009. Corrigenda have been published in J. Neurophysiol. (1996) 75(6), without covering, however, all typing errors.
[34] Ito, M. (1984). The Cerebellum and Neural Control. Raven Press, New York.
[35] Otmakhov, N., Shirke, A. M. and Malinow, R. (1993), Measuring the impact of probabilistic transmission on neuronal output, Neuron 10: 1101–1111.
[36] Barbour, B. (1993). Synaptic currents evoked in Purkinje cells by stimulating individual granule cells, Neuron 11: 759–769.
[37] Koch, C., Poggio, T. and Torre, V. (1982), Retinal ganglion cells: a functional interpretation of dendritic morphology, Phil. Trans. R. Soc. Lond. (Biol.) 298: 227–264.
[38] Rinzel, J. and Rall, W. (1974). Transient response in a dendritic neuron model for current injected at one branch, Biophys. J. 14: 759–790.
[39] Agmon-Snir, H. and Segev, I. (1993), Signal delay and input synchronization in passive dendritic structures, J. Neurophysiol. 70: 2066–2085.
[40] Bernander, O., Douglas, R. D., Martin, K. A. and Koch, C. (1991). Synaptic background activity influences spatiotemporal integration in single pyramidal cells, Proc. Natl. Acad. Sci. (USA) 88: 11569–11573.
[41] Rapp, M., Yarom, Y. and Segev, I. (1992), The impact of parallel fiber background activity on the cable properties of cerebellar Purkinje cells, Neural Computation 4: 518–533.
[42] Stuart, G. J. and Sakmann, B. (1994), Active propagation of somatic action potentials into neocortical pyramidal cell dendrites, Nature 367: 69–72.
[43] Laurent, G. (1993). A dendritic gain control mechanism in axonless neurons of the locust, Schistocerca americana, J. Physiol. (London) 470: 45–54.
[44] McKenna, T., Davis, J. and Zornetzer, S. F. (eds) (1992). Single Neuron Computation, Academic Press, San Diego.
[45] Wilson, C. J. (1992). Dendritic morphology, inward rectification and the functional properties of neostriatal neurons, in T. McKenna, J. Davis and S. Zornetzer (eds), Single Neuron Computation, Academic Press, San Diego, pp. 141–172 Mel, W. B. (1993), Synaptic integration in an excitable dendritic trees, J. Neurophys. 70: 1086–1101.
[46] Laurent, G. (1993). A dendritic gain control mechanism in axonless neurons of the locust, Schistocerca americana, J. Physiol. (London) 470: 45–54.
[47] Rall, W. and Segev, I. (1987), Functional possibilities for synapses on dendrites and on dendritic spines, in G. M. Edelman, E. E. Gall and W. M. Cowan (eds), Synaptic Function, Wiley, New York, pp. 605–636.
[48] Pinski, P. F. and Rinzel, J. (1994). Intrinsic and network rhythmogenesis in a reduced Traub model for CA3 neurons, J. Comput. Neurosci. 1: 39–60.

Chapter 8

[1] Haberly, L. B. (1990). Olfactory cortex, in G. M. Shepherd (ed.), The Synaptic Organization of the Brain, Oxford University Press, New York, chapter 10, pp. 317–345.
[2] Wilson, M. and Bower, J. M. (1992) “Cortical oscillations and temporal interactions in a computer simulation of piriform cortex,” J. Neurophysiol, 67: 981–995.
[3] Hasselmo, M. E. and Bower, J. M. (1993). Acetylcholine and memory, Trends Neurosci. 16: 218–222.
[4] Bower, J. M. (1995). Reverse engineering the nervous system: An in vivo, in vitro, and in computer approach to understanding the mammalian olfactory system, in S. F. Zornetzer, J. L. Davis and C. Lau (eds), An Introduction to Neural and Electronic Networks, second eds, Academic Press, New York, NY, pp. 3–28.
[5] Haberly, L. B. (1985). Neuronal circuitry in olfactory cortex: anatomy and functional applications, Chemical Senses 10: 219–238.
[6] Haberly, L. B. and Bower, J. M. (1989). Olfactory cortex – model circuit for study of associative memory, Trends Neurosci. 12: 258–264.
[7] Wilson, M. A. and Bower, J. M. (1989). The simulation of large scale neural networks, in C. Koch and I. Segev (editors), Methods in Neuronal Modeling, MIT Press, Cambridge, MA, chapter 9, pp. 291–333.
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Chapter 9

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Appendix A

[1] Shinya Inoué and Kenneth R. Spring, Video Microscopy: The Fundamentals, Plenum Press, New York and London, 2 edition, 1997.
[2] S. Nie, D. T. Chiu, and R. N. Zare, “Probing individual molecules with confocal fluorescence microscopy,” Science, vol. 266, no. 5187, pp. 1018-21, 1994.
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[5] Axelrod, D. (1 April 1981). “Cell-substrate contacts illuminated by total internal reflection fluorescence.” The Journal of Cell Biology. 89 (1): 141–145. doi:10.1083/jcb.89.1.141. PMC 2111781Freely accessible. PMID 7014571.
[6] Yanagida, Toshio; Sako, Yasushi; Minoghchi, Shigeru (10 February 2000). “Single-molecule imaging of EGFR signaling on the surface of living cells.” Nature Cell Biology. 2 (3): 168–172. doi:10.1038/35004044. PMID 10707088.
[7] Ambrose, W; et al. (1999). “Single-molecule detection with total internal reflection excitation: comparing signal-to-background and total signals in different geometries.” Cytometry. 36(3): 224.

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