SAN 2011 abstract in Neuroscience Letters 2011, vol. 500(Suppl.), e32-33. Read the rest of this entry…

Background noise and spike overlap pose problems in contemporary spike-sorting strategies. In this paper, both issues are addressed by a hybrid scheme that combines the robust representation of spike waveforms to facilitate the reliable identification of contributing neurons with efficient data learning to enable the precise decomposition of coactivations.

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R.W. Lucky discusses the Gaussian profile of our world in the November’s issue of IEEE Spectrum.

Illustration: Richard Mia, IEEE Spectrum Nov 2010

Noise in neural systems usually carries a non-white complex correlated gaussian profile with higher power in low frequencies, due to synaptic coupling among neurons, superimposed field potentials etc.

Furthermore, as it has already been argued for more than a decade [1], in real case scenarios background noise cannot be considered as a stationary Gaussian process. On the other hand, most of communication theory, for example, is based on the “fiction” of additive white Gaussian noise.
In this article, the author challenges the “normality” of a world dominated by bell-shaped Gaussian curves.


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