By Hugh Aldersey-Williams
El cuerpo humano, nuestro tabú más incómodo. El único elemento común para todos los seres humanos. Una suma de huesos, piel, grasa y fluidos a los angeles que, mientras todo carbura, prestamos escasa atención. Y sin embargo, desde el arte antiguo a los angeles cirugía plástica, desde los primeros anatomistas a los artistas conceptuales, de los profanadores de tumbas a los atletas biónicos, nuestra relación con el cuerpo humano cube más de nosotros mismo que cualquier otro asunto en los angeles historia de los angeles humanidad.
Mezclando historia, arte, literatura y vida cotidiana, Hugh Aldersey-Williams se adentra en l. a. más misteriosa y maravillosa de las creaciones. El resultado es una obra fascinante repleta de hechos asombrosos, historias inverosímiles e información deslumbrante que va desde l. a. primera huella dactilar hasta los angeles psicología de los ángeles, desde l. a. máscara fúnebre de Isaac Newton hasta el cerebro embalsamado de Einstein.
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Extra info for Anatomías: El cuerpo humano, sus partes y las historias que cuentan
Use the model that leads to Eq. 19) to generate samples of the peak current, and then use these samples to estimate the probability distribution. 2, and n¯ = 1. Notice that because the current is continuous, you have to make bins along the current axis; smaller bins reveal more structure but also generate noisier results, because the number of counts in each bin is smaller. As you experiment with different bin sizes and different numbers of samples, try to develop some feeling for whether the agreement between theory and experiment in Fig.
In many systems we will also ﬁnd, along the path from input to output, an ampliﬁer that makes all signals larger. But the ampliﬁer does not “know” which of its inputs are signal and which are noise, so everything is ampliﬁed. Thus, a small noise near the input can become a large noise near the output, but the size of this noise at the output does not, by itself, tell us how hard it will be to detect signals at the input. What we can do is to imagine that the whole system is noiseless and that any noise we see at the output really was injected at the input, and thus it followed exactly the same path as the signals we are trying to detect.
The dark noise in rods) value rdark except in a 100 msec window where the rate is higher, say, r = rdark + rsignal . 01 s−1 if they can integrate over 1000 rods. Try to simulate events in this parameter range and actually look at examples, perhaps plotted with x marks to show you where the events occur in a single trial. 01 s−1 and one in which rsignal = 0? Does it matter whether you know when to expect the extra events? In 8. This problem has its origins in teaching at the Methods in Computational Neuroscience course at the Marine Biological Laboratory and was developed together with Rob de Ruyter van Steveninck.