11:00 am Thursday, March 10, 2016
Math-Neuro Seminar: Trial-to-trial variability, nonstationarity, and statistical interpretation in neurophysiology. by Asohan Amarasingham (CCNY) in SEAY 4.244
How do neurons encode information, and communicate with one another via synapses? Experimental approaches to these questions are challenging because the spike outputs of a neuronal population, as typically recorded in behaving animals, are influenced by a vast array of factors. Such factors span all levels of description, from the microscopic (e.g., ion fluctuations, states of presynaptic neurons) to the macroscopic (e.g., sensation, attention), but only a small fraction of these is measured, or even understood. As a consequence, it is not clear to what degree variations in unknown and uncontrolled variables alternately reveal or confound the underlying signals that observed spikes are presumed to encode. Another consequence, very much related, is that these uncertainties also disturb our intuitive comfort with common models of statistical repeatability in neurophysiological signal analysis. I will describe these issues in the context of large-scale electrophysiology recordings in behaving animals. I will use this context to motivate an approach to robust neurophysiological signal analysis, based on conditional modeling. Applications will be suggested to the problems of synaptic and network identification in behavioral conditions, timescale analysis for point processes, and classical spike-behavior associations in neural coding studies. Submitted by
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