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Statistical Signal Processing for Neuroscience and Neurotechnology ReviewCapturing the signals from neurons is a very complex undertaking, as they must be gleaned from a great deal of extraneous data. There is the low-level random background noise generated by the natural transient changes in concentrations of ions and there are the pseudorandom oscillations of other physiological activity such as blood flow and the resultant movement of nutrients. The alterations of electrical potentials based on the actions of neurons must be extracted from all of this before any attempt can be made to study the phenomena.The algorithms used to obtain this information are based on statistical models and are presented in detail in this book. Understanding those models requires a great deal of knowledge of mathematics, with an emphasis on statistics. The statistical models are based on calculus, so exposure to advanced statistical techniques is necessary. A large number of graphs illustrating the behavior of the models as well as some depicting actual collected data are included. Natural consequences of the work, such as the development of artificial limbs that respond to neuron activity are also covered.
One of the major tools now used in the advancement of knowledge in many areas of science is the ability to mine the significant data from an overall stream of data, especially when that data is of secondary strength. The statistical techniques described here are used to extract the neural signals from everything else and are part of what is sure to be a revolution in prosthetics. This book is an excellent work in this area and some parts contain valuable information useful for computer scientists interested in machine cognitive models.
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