Skip to main content

Pattern Recognition in a Bucket

  • Conference paper
Advances in Artificial Life (ECAL 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2801))

Included in the following conference series:


This paper demonstrates that the waves produced on the surface of water can be used as the medium for a “Liquid State Machine” that pre-processes inputs so allowing a simple perceptron to solve the XOR problem and undertake speech recognition. Interference between waves allows non-linear parallel computation upon simultaneous sensory inputs. Temporal patterns of stimulation are converted to spatial patterns of water waves upon which a linear discrimination can be made. Whereas Wolfgang Maass’ Liquid State Machine requires fine tuning of the spiking neural network parameters, water has inherent self-organising properties such as strong local interactions, time-dependent spread of activation to distant areas, inherent stability to a wide variety of inputs, and high complexity. Water achieves this “for free”, and does so without the time-consuming computation required by realistic neural models. An analogy is made between water molecules and neurons in a recurrent neural network.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others


  1. Maass, W., Natschlager, T., et al.: Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations. Neural Computation 14, 2531–2560 (2002)

    Article  MATH  Google Scholar 

  2. Hopfield, J.J., Brody, C.D.: What is a moment? ”Cortical” sensory integration over a brief interval. PNAS 97(25), 13919–13924 (2000)

    Article  Google Scholar 

  3. Auer, P., Burgsteiner, H., et al.: The p-Delta Learning Rule for Parallel Perceptrons (2002) (submitted for publication),

  4. Adamatzky, A.: Computing in nonlinear media: make waves, study collisions. In: Kelemen, J., Sosík, P. (eds.) ECAL 2001. LNCS (LNAI), vol. 2159, pp. 1–11. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  5. Adamatzky, A., De Lacy Costello, B.P.J.: Experimental logical gates in a reaction-difusion medium: The XOR gate and beyond. Physical Review E 66, 46112 (2002)

    Article  Google Scholar 

  6. Goldenholz, D.: Liquid Computing: A Real Effect. BE707 Final Project. Boston University School of Medicine, Boston (2002),

    Google Scholar 

  7. MacLennan, B.: Field Computation in Natural and Artificial Intelligence. Knoxville, University of Tennessee. Report UT-CS-99-422. (1999),

  8. McCulloch, W.S., Pitts, W.: A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathamatical Biophysics 5, 115–133 (1943)

    Article  MATH  MathSciNet  Google Scholar 

  9. Freeman, W.: Mesoscopic Neurodynamics: From neuron to brain. Journal of Physiology (Paris) 94, 303–322 (2000)

    Article  Google Scholar 

  10. Walmsley, I.: Computing with interference: All-optical single-query 50- element database search. In: Conference on Lasers and Electro-Optics/Quantum Electronics and Laser Science, Baltimore, Maryland (2001)

    Google Scholar 

  11. Tononi, E., Sporns, O.: Complexity and Coherency: integrating information in the brain. Trends in Cognitive Sciences 2(12), 474–483 (1998)

    Article  Google Scholar 

  12. Muller, G., Newman, S.: Origination of Organismal Form: Beyond the Gene in Developmental and Evolutionary Biology. MIT Press, Cambridge (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations


Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Fernando, C., Sojakka, S. (2003). Pattern Recognition in a Bucket. In: Banzhaf, W., Ziegler, J., Christaller, T., Dittrich, P., Kim, J.T. (eds) Advances in Artificial Life. ECAL 2003. Lecture Notes in Computer Science(), vol 2801. Springer, Berlin, Heidelberg.

Download citation

  • DOI:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-20057-4

  • Online ISBN: 978-3-540-39432-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics