Advancing Artificial Intelligence through Biological Process by Ana B. Porto Pazos, Alejandro Pazos Sierra, Washington Buno

By Ana B. Porto Pazos, Alejandro Pazos Sierra, Washington Buno Buceta

As technological know-how maintains to boost, researchers are consistently gaining new insights into the way in which residing beings behave and serve as, and into the composition of the smallest molecules. every one of these organic methods were imitated by way of many clinical disciplines with the aim of attempting to resolve diverse difficulties, certainly one of that's man made intelligence.

Advancing man made Intelligence via organic method Applications provides fresh advances within the examine of sure organic strategies regarding details processing which are utilized to man made intelligence. Describing the advantages of lately stumbled on and present concepts to adaptive man made intelligence and biology, this booklet can be a hugely valued addition to libraries within the neuroscience, molecular biology, and behavioral technology spheres.

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Thus, we use the wavelet analysis of spike trains to describe the neural behavior in both time and frequency domains. Wavelet Analysis The wavelet power spectrum described in the equation (6) allows us to study the spectral properties of a spike train and how these properties change in time. Figure 3 shows a representative example of the wavelet power spectrum of neural spike-train in spontaneous condition and during response to vibrissa deflection in control conditions and during ipsi- and contra-lateral distractions.

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Vaadia E. (1993). Spatiotemporal firing patterns in the frontal cortex of behaving monkeys. Journal Neurophysiology, 70, 1629-1638. , & Artieda, J. (2003). Movementrelated changes in cortical oscillatory activity in ballistic, sustained and negative movements. Experimental Brain Research, 148,17–25. Carvell, G. , & Simons, D. J. (1990). Biometric analyses of vibrissal tactile discrimination in the rat. Journal Neuroscience, 10, 2638-2648. Castellanos, N. P. & Makarov, V. A. (2006). Recovering EEG brain signals: artifact suppression with wavelet enhanced independent component analysis.

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