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.
CITATION STYLE
Fernando, C., & Sojakka, S. (2003). Pattern recognition in a bucket. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 2801, pp. 588–597). Springer Verlag. https://doi.org/10.1007/978-3-540-39432-7_63
Mendeley helps you to discover research relevant for your work.