Switching approaches can improve the performance of adaptive schemes, however a data driven criterion to accomplish the task is unclear. In this paper, we propose a new optimization criterion for switching which is estimated directly from data. We apply the method to the recently introduced MEE and MEE-SAS algorithms. Using this novel switching scheme, we develop a single algorithm which effectively combines the strengths of MEE and MEE-SAS without sacrificing the simplicity and stability properties of MEE. We explain these results analytically, and through simulations.

In this paper a clustering algorithm that learns the groups of synchronized spike trains directly from data is proposed. Clustering of spike trains based on the presence of synchronous neural activity is of high relevance in neurophys-iological studies. In this context such activity is thought to be associated with functional structures in the brain. In addition, clustering has the potential to analyze large volumes of data. The algorithm couples a distance between two spike trains recently proposed in the literature with spectral clustering. Finally, the algorithm is illustrated in sets of computer generated spike trains and analyzed for the dependence on its parameters and accuracy with respect to features of interest.

In this paper, we revisit information theoretic vector quantization (ITVQ) algorithm introduced in (T. Lehn-Schioler et al., 2005) and make it practical. We derive a fixed point update rule to minimize the Cauchy-Schwartz(CS) pdf divergence between the set of codewords and the actual data. In doing so, we overcome two severe deficiencies of the previous gradient based method namely, the number of parameters to be optimized and slow convergence rate, thus making this algorithm more efficient and useful as a compression algorithm.

In this paper, we present a weighted Linde-Buzo-Gray algorithm (WLBG) as a powerful and efficient technique for compressing neural spike data. We compare this technique with the recently proposed self-organizing map with dynamic learning (SOM-DL) and the traditional SOM. A significant achievement of WLBG over SOM-DL is a 15 dB increase in the SNR of the spike data apart from having a compression ratio of 150 : 1. Being simple and extremely fast, this algorithm allows real-time implementation on DSP chips opening new opportunities in BMI applications.

We propose in this paper the normalized Minimum Error Entropy (NMEE). Following the same rational that lead to the normalized LMS, the weight update adjustment for Minimum Error Entropy (MEE) is constrained by the principle of minimum disturbance. Unexpectedly, we obtained an algorithm that not only is insensitive to the power of the input, but is also faster than the MEE for the same misadjustment, and also that is less sensitive to the kernel size. We explain these results analytically, and through system identification simulations.

We propose a new method of clustering neural spike waveforms for spike sorting. After detecting the spikes using a threshold detector, we use principal component analysis (PCA) to get the first few PCA components of the data. Clustering on these PCA components is achieved by maximizing the Cauchy Schwartz PDF divergence measure which uses the Parzen window method to non parametrically estimate the pdf of the clusters. Comparison with other clustering techniques in spike sorting like k-means and Gaussian mixture elucidates the superiority of our method in terms of classification results and computational complexity.

Relational database systems do not effectively support complex queries containing quantifiers (quantified queries) that are increasingly becoming important in decision support applications. Generalized quantifiers provide an effective way of expressing such queries naturally. In this paper, we consider the problem of processing quantified queries within the generalized quantifier framework. We demonstrate that current relational systems are ill-equipped, both at the language and at the query processing level, to deal with such queries. We also provide insights into the intrinsic difficulties associated with processing such queries. We then describe the implementation of a quantified query processor, Q2P, that is based on multidimensional and boolean matrix structures. We provide results of performance experiments run on Q2P that demonstrate superior performance on quantified queries. Our results indicate that it is feasible to augment relational systems with query subsystems like Q2P for significant performance benefits for quantified queries in decision support applications.