EXTRACTING OSCILLATIONS - NEURONAL COINCIDENCE DETECTION WITH NOISY PERIODIC SPIKE INPUT

How does a neuron vary its mean output firing rate if the input change s from random to oscillatory coherent but noisy activity? What are the critical parameters of the neuronal dynamics and input statistics? To answer these questions, we investigate the coincidence-detection prop erties of an integrate-and-fire neuron. We derive an expression indica ting how coincidence detection depends on neuronal parameters. Specifi cally, we show how coincidence detection depends on the shape of the p ostsynaptic response function, the number of synapses, and the input s tatistics, and we demonstrate that there is an optimal threshold. Our considerations can be used to predict from neuronal parameters whether and to what extent a neuron can act as a coincidence detector and thu s can convert a temporal code into a rate code.