Advertisement

Neural Systems Engineering

  • Steve Furber
  • Steve Temple
Part of the Studies in Computational Intelligence book series (SCI, volume 115)

Biological brains and engineered electronic computers fall into different categories. Both are examples of complex information processing systems, but beyond this point their differences outweigh their similarities. Brains are flexible, imprecise, error-prone and slow; computers are inflexible, precise, deterministic and fast. The sets of functions at which each excels are largely non-intersecting. They simply seem to be different types of system. Yet throughout the (admittedly still rather short) history of computing, scientists and engineers have made attempts to cross-fertilize ideas from neurobiology into computing in order to build machines that operate in a manner more akin to the brain. Why is this?

Part of the answer is that brains display very high levels of concurrency and fault-tolerance in their operation, both of which are properties that we struggle to deliver in engineered systems. Understanding how the brain achieves these properties may help us discover ways to transfer them to our machines. In addition, despite their impressive ability to process numbers at ever-increasing rates, computers continue to be depressingly dumb, hard to use and totally lacking in empathy for their hapless users. If we could make interacting with a computer just a bit more like interacting with another person, life would be so much easier for so many people.

Keywords

Neural System Logic Gate Spike Neural Network Leaky Integrator Spike Event 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Adrian ED 1964 Basis of sensation. Haffner, London, UK.Google Scholar
  2. 2.
    Austin J, Kennedy J, Lees K (1995) The Advanced Uncertain Reasoning Archi-tecture, AURA. In: Proc. Weightless Neural Network Workshop (WNNW’95), 26-27 September, University of Kent, UK.Google Scholar
  3. 3.
    Bainbridge WJ, Furber SB(2002) CHAIN: a delay-insensitive chip area interconnect. IEEE Micro, 22: 16-23.CrossRefGoogle Scholar
  4. 4.
    Binzegger T, Douglas RJ, Martin KAC 2004 A quantitative map of the circuit of cat primary visual cortex. J. Neuroscience, 24(39): 8441-8453.CrossRefGoogle Scholar
  5. 5.
    Boahen KA 2000 Point-to-point connectivity between neuromorphic chips using address events. IEEE Trans. Circuits and Systems, 47(5): 416-434.zbMATHCrossRefGoogle Scholar
  6. 6.
    Bower JM, Beeman D 1995 The Book of GENESIS: Exploring Realistic Neural Models with the GEneral NEural SImulation System. Springer-Verlag, New York, NY.zbMATHGoogle Scholar
  7. 7.
    Eliasmith C, Anderson CH 2003 Neural Engineering. MIT Press, Cambridge, MA.Google Scholar
  8. 8.
    Fatt P, Katz B 1952 Spontaneous subthreshold activity at motor nerve endings. J. Physiology, 117: 109-128.Google Scholar
  9. 9.
    Furber SB, Bainbridge WJ, Cumpstey JM, Temple S 2004 A sparse distributed memory based upon N-of-M codes. Neural Networks, 17(10): 1437-1451.zbMATHCrossRefGoogle Scholar
  10. 10.
    Furber SB, Temple S, Brown AD (2006) On-chip and inter-chip networks for modeling large-scale neural systems. In: Proc. Intl. Symp. Circuits and Systems (ISCAS’06), 21-24 May, Kos, Greece. IEEE Press, Piscataway, NJ: 1945-1948.Google Scholar
  11. 11.
    Furber SB, Temple S, Brown AD 2006 High-performance computing for sys-tems of spiking neurons. In: Kovacs T, Marshall JAR (eds.) Proc. Adaptation in Artificial and Biological Systems Workshop (AISB’06) - GC5: Architecture of Brain and Mind 2, 3-6 April, Bristol, UK. Society for Aartificial Intelligence and the Simulaiton of behavior: 29-36.Google Scholar
  12. 12.
    Furber SB, Brown G, Bose J, Cumpstey MJ, Marshall P, Shapiro JL (2007) Sparse distributed memory using rank-order neural codes. IEEE Trans. Neural Networks, 18(3): 648-659.CrossRefGoogle Scholar
  13. 13.
    Gerstner W 1995 Time structure of the activity in neural network models. Physics Reviews E, 51: 738-758.CrossRefGoogle Scholar
  14. 14.
    Hebb DO 1949 The Organization of Behavior: A Neuropsychological Theory. Wiley, New York, NY.Google Scholar
  15. 15.
    Hellmich HH, Geike M, Griep P, Mahr P, Rafanelli M, Klar H (2005) Emulation engine for spiking neurons and adaptive synaptic weights. In: Proc. Intl. Joint Conf. Neural Networks (IJCNN’05), 31 July - 4 August, Montreal, Canada. 5: 3261-3266.Google Scholar
  16. 16.
    Hodgkin A, Huxley AF 1952 A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiology, 117: 500-544.Google Scholar
  17. 17.
    Izhikevich EM 2004 Which model to use for cortical spiking neurons? IEEE Trans. Neural Networks, 15: 1063-1070.CrossRefGoogle Scholar
  18. 18.
    Izhikevich EM (2005) Simulation of large-scale brain models. (available online at:http://vesicle.nsi.edu/users/izhikevich/human brain simulation/Blue Brain. htm#Simulation of Large-Scale Brain Models - last accessed October 2007)
  19. 19.
    Izhikevich EM 2006 Polychronization: computation with spikes. Neural Computation, 18: 245-282.zbMATHCrossRefMathSciNetGoogle Scholar
  20. 20.
    Jahnke A, Roth U, Klar H 1996 A SIMD/dataflow architecture for a neu-rocomputer for spike-processing neural networks (NESPINN). MicroNeuro, 96: 232-237.Google Scholar
  21. 21.
    Kanerva P 1988 Sparse Distributed Memory. MIT Press, Cambridge, MA.zbMATHGoogle Scholar
  22. 22.
    Kohonen T 1972 Correlation matrix memories. IEEE Trans. Computers C, 21: 353-359.zbMATHCrossRefGoogle Scholar
  23. 23.
    Laughlin SB, Sejnowski TJ(2003) Communication in neuronal networks. Science, 301: 1870-1874.CrossRefGoogle Scholar
  24. 24.
    Lichtsteiner P, Posch C, Delbruck T (2006) A 128 × 128 120 dB 30 mW asyn-chronous vision sensor that responds to relative intensity change. In: Proc. Intl. Solid State Circuits Conf. (ISSCC’06), 4-9 February, San Francisco, CA: 508-509.Google Scholar
  25. 25.
    Lømo T 2003 The discovery of long-term potentiation. Philosophical Trans. Royal Soc. B, 358: 617-620.CrossRefGoogle Scholar
  26. 26.
    Mahowald M 1992 VLSI analogs of neuronal visual processing: a synthesis of form and function. PhD Dissertation, California Institute of Technology, Pasadena, CA.Google Scholar
  27. 27.
    Markram H 2006 The blue brain project. Nature Reviews Neuroscience, 7: 153-160.CrossRefGoogle Scholar
  28. 28.
    McCulloch WS, Pitts W 1943 A logical calculus of the ideas immanent in nervous activity. Bulletin Mathematical Biophysiology, 5: 115-133.zbMATHCrossRefMathSciNetGoogle Scholar
  29. 29.
    Mead CA 1989 Analog VLSI and Neural Systems. Addison Wesley, Reading, MA.zbMATHGoogle Scholar
  30. 30.
    Mead CA 1990 Neuromorphic electronic systems. Proc. IEEE, 78(10): 1629-1636.CrossRefGoogle Scholar
  31. 31.
    Mehrtash N, Jung D, Hellmich HH, Schoenauer T, Lu VT, Klar H 2003 Synaptic plasticity in spiking neural networks (SP2 INN): a system approach. IEEE Trans. Neural Networks, 14(5): 980-992.CrossRefGoogle Scholar
  32. 32.
    Moore GE 1965 Cramming more components onto integrated circuits. Electronics, 38(8): 114-117.Google Scholar
  33. 33.
    Mountcastle V 1978 An organizing principle for cerebral function: the unit module and the distributed system. In: Edelman GM, Mountcastle VB (eds.) The Mindful Brain. MIT Press, Cambridge, MA: 7-50.Google Scholar
  34. 34.
    NEURON (available online at: http://www.neuron.yale.edu/neuron- last accessed October 2007).
  35. 35.
    O’Keefe J, Recce ML 1993 Phase relationship between hippocampal place units and the EEG theta rhythm. Hippocampus, 3(3): 317-330.CrossRefGoogle Scholar
  36. 36.
    OReilly RC 1996 Biologically plausible error-driven learning using local activation differences: the generalized recirculation algorithm. Neural Computation, 8(5): 895-938.CrossRefMathSciNetGoogle Scholar
  37. 37.
    Prange SJ, Klar H (1993) Cascadable digital emulator IC for 16 biological neurons. In: Proc. 40th Intl. Solid State Cicruits Conf. (ISSCC’93), 24-26 February, San Francisco, CA: 234-235, 0294.Google Scholar
  38. 38.
    Rall W 1959 Branching dendritic trees and motoneuron membrane resistivity. Experimental Neurology, 1: 491-527.CrossRefGoogle Scholar
  39. 39.
    Saudargiene A, Porr B, Wörgötter F 2004 How the shape of pre- and post-synaptic signals can influence STDP: a biophysical model. Neural Computation, 16: 595-625.zbMATHCrossRefGoogle Scholar
  40. 40.
    Schoenauer T, Mehrtash N, Jahnke A, Klar H 1998 MASPINN: novel concepts for a neuro-accelerator for spiking neural networks. In: Lindblad T, Padgett ML, Kinser JM (eds.) Proc. Workshop on Virtual Intelligence and Dynamic Neural Networks (VIDYNN’98), 26-28 June, Stockholm, Sweden: 87-97.Google Scholar
  41. 41.
    Schwartz J, Begley S 2003 The Mind and the Brain: Neuroplasticity and the Power of Mental Force. Regan Books, New York, NY.Google Scholar
  42. 42.
    Sivilotti M 1991 Wiring considerations in analog VLSI systems, with applica-tion to field-programmable networks. PhD Dissertation, California Institute of Techology, Pasadena, CA.Google Scholar
  43. 43.
    Sloman A (2004) GC5: The architecture of brain and mind. In: Hoare CAR, Milner R (eds.) UKCRC Grand Challenges in Computing - Research. British Computer Society, Edinburgh, UK: 21-24.Google Scholar
  44. 44.
    Van Rullen R, Thorpe S 2001 Rate coding versus temporal order coding: what the retinal ganglion cells tell the visual cortex. Neural Computation, 13(6): 1255-1283.zbMATHCrossRefGoogle Scholar
  45. 45.
    Werbos P 1994 The Roots of Backpropagation: From Ordered Derivatives to Neural Networks and Political Forecasting. Wiley, New York, NY.Google Scholar
  46. 46.
    Willshaw DJ, Buneman OP, Longuet-Higgins HC 1969 Non-holographic associative memory. Nature, 222: 960-962.CrossRefGoogle Scholar
  47. 47.
    Yang Z, Murray AF, Wörgötter F, Cameron KL, Boonsobhak V 2006 A neuro-morphic depth-from-motion vision model with STDP adaptation. IEEE Trans. Neural Networks, 17(2): 482-495.CrossRefGoogle Scholar
  48. 48.
    Zhu J, Sutton P 2003 FPGA Implementations of neural networks - a survey of a decade of progress. In: Cheung PYK, Constantinides GA, de Sousa JT (eds.) Proc. 13th Annual Conf. Field Programmable Logic and Applications (FPL’03), 1-3 September, Lisbon, Portugal. Springer-Verlag, Berlin: 1062-1066.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Steve Furber
    • 1
  • Steve Temple
    • 1
  1. 1.School of Computer ScienceUniversity of ManchesterManchesterUK

Personalised recommendations