By Noam Shabtai

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25). Theoretically, there is no limit to the computing power of this paradigm if the reservoir dynamics are stable and fulfill the requirements of short term memory and state separation. If the reservoir dynamics are carefully analysed then a simple gradient based algorithm can successfully classify a complex problem such as speech recognition. In this experiment, standard supervised algorithms were investigated to quantify their classification accuracies based on the input features sampled through the reservoir.

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20 Advances in Speech Recognition In the paradigm of reservoir computing, readout neurons have an exclusive access to the liquid or reservoir states x(t). For stable reservoir dynamics, it is required that two different inputs u(s) and v(s) should produce two significantly different states xu(t) and xv(t) which will hold information about preceding inputs. , 2002). In order to analyse the separation property of reservoirs, various reservoir architectures were simulated and their responses were observed in terms of membrane voltages and spike times.

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