Abstract
Spectral methods allow the estimation of the firing frequency in the activity of a single neuron. However, transient periods, changes in the neuron firing frequency or even changes in the neuron activity regime (rest, tonic firing or spiking) due to different inputs or to the presence of neurotransmitters are not well detected by means of these methods due to the fact that frequency and time are not commutable operators. Some other methods have been developed to deal with local transients, for example the localized Fourier transform or the Wigner distribution. Unfortunately these localized methods need fine tuning to find an adequate working resolution and the resulting coefficients are hard to interpret. In this work we propose the use of the tomographic transforms to detect and characterize transient components in the behaviour of a single neuron.
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Aguirre, C., Pascual, P., Campos, D., Serrano, E. (2011). Single Neuron Transient Activity Detection by Means of Tomography. In: Cabestany, J., Rojas, I., Joya, G. (eds) Advances in Computational Intelligence. IWANN 2011. Lecture Notes in Computer Science, vol 6691. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21501-8_7
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DOI: https://doi.org/10.1007/978-3-642-21501-8_7
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