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The design of echo state network (ESN) parameters relies on the selection of the maximum eigenvalue of the linearized system around zero (spectral radius). However, this procedure does not quantify in a systematic manner the performance of the ESN in terms of approximation error. This article presents a functional space approximation framework to better(More)
The present design of echo state network (ESN) parameters relies on the selection of the maximum eigenvalue of the linearized system around zero. However , this has been found far from optimal for function approximation. This letter presents a function approximation perspective to better understand the operation of ESNs and proposes an information-theoretic(More)
The use of echo state networks (ESN) to find patterns in time (dynamical pattern recognition) has been limited. This paper argues that ESNs are particularly well suited for dynamical pattern recognition and proposes a linear associative memory (LAM) as a novel readout for ESNs. From the class of LAMs, the minimum average correlation energy (MACE) filter is(More)
A type of recurrent neural network has been proposed by H. Jaeger. This model, called Echo State Network (ESN), possesses a highly interconnected and recurrent topology of nonlinear processing elements, which constitutes a "reservoir of rich dynamics" and contains information about the history of input or/and output patterns. The interesting property of ESN(More)
— Towards non-invasive neuroprosthetic systems for motor control, electrocorticogram (ECoG) recordings provide an intermediate step from microwire single neuron recordings to electroencephalograms. Preprocessing modalities that emphasize amplitude modulation and temporal power in the ECoG are employed to build human brain-machine interfaces, which have been(More)
– It has been shown that a framework composed of digital signal processing (DSP) elements can be used to simulate and study Freeman's model of the biologically realistic olfactory cortex. In this paper, based on impulse invariant transformation, a DSP environment has been developed corresponding to the original continuous-time dynamical system. The(More)
This paper describes how echo state networks (ESN) can be used in conjunction with minimum average correlation energy (MACE) filters in order to create a system that can identify spikes in neural recordings. Various experiments using real-world data were used to compare the performance of the ESN-MACE against threshold and matched filter detectors to(More)
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