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)
Stability is an essential constraint in the design of linear dynamical systems. Similar stability restrictions on nonlinear dynamical systems, such as echo state network, have been enforced in order to use them for reliable computation. In this paper we will introduce a novel computational mode for nonlinear systems with sigmoidal nonlinearity, which does… (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)
Recently we have proposed a recursive estimator for Renyi's quadratic entropy. This estimator can converge to accurate results for stationary signals or track the changing entropy of nonstationary signals. In this paper, we demonstrate the application of the recursive entropy estimator to supervised and unsupervised training of linear and nonlinear adaptive… (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)
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