Proust Was a Neuroscientist by Jonah Lehrer
By Jonah Lehrer
During this technology-driven age, it’s tempting to think that technological know-how can remedy each secret. in the end, technology has cured numerous illnesses or even despatched people into area. yet as Jonah Lehrer argues during this glowing debut, technology isn't the in simple terms route to wisdom. in truth, by way of knowing the mind, artwork acquired there first.
Taking a gaggle of artists — a painter, a poet, a chef, a composer, and a handful of novelists — Lehrer exhibits how each chanced on a vital fact concerning the brain that technological know-how is barely now rediscovering. We study, for instance, how Proust first printed the fallibility of reminiscence; how George Eliot chanced on the brain’s malleability; how the French chef Escoffier came across umami (the 5th taste); how Cézanne labored out the subtleties of imaginative and prescient; and the way Gertrude Stein uncovered the deep constitution of language — a whole half-century sooner than the paintings of Noam Chomsky and different linguists. It’s the last word story of artwork trumping science.
More widely, Lehrer exhibits that there is a fee to decreasing every thing to atoms and acronyms and genes. size isn't the related as figuring out, and artwork understands this greater than technological know-how does. An inventive mix of biography, feedback, and excellent technology writing, Proust was once a Neuroscientist urges technological know-how and paintings to hear extra heavily to one another, for keen minds can mix the easiest of either, to exceptional effect.
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N. (2006). Selective enhancement of associative learning by microstimulation of the anterior caudate. Nature Neuroscience, 9, 4, 562–568. N. A. Single neurons in the monkey hippocampus and learning of new associations. Science, 300, 5625, 1578–1581. N. A. (2009). Comparison of associative learning-related signals in the macaque perirhinal cortex and hippocampus. Cerebral Cortex, 19, 5, 1064–1078. 18 THE DYNAMIC BRAIN APPENDIX A Details of the Recursive Filter In this section, we provide details of the derivation of equations (8)-(12).
Analysis of population activity demonstrates signiﬁcantly larger trial-to-trial variability in the time from stimulus onset until activity changes signiﬁcantly from baseline in the “jumping” mode compared to “ramping” mode. Averaging activity across multiple trials produces similar average behavior across both modes, however. Thus, only analysis based on multiple spike trains observed simultaneously in each trial can identify the general mode of network operation. Comparison of the outputs of the network in the two modes of operation indicates that in the presence of typical internal noise, the “jumping” mode responds more reliably to a small bias in its inputs than does the “ramping” mode (Miller and Katz, 2010).
Hidden Markov Modeling (HMM) HMM is a statistical method most commonly used in speech recognition software in engineering and for analysis of DNA sequences in biology. , 2007). HMM assumes the presence of two Markov processes, each of which, by deﬁnition, depends only on the current state of the system and is independent of the history of prior states. When applied to neural spike trains, one of the Markov processes is the emission of spikes by a neuron at any point in time. The Markov assumption for spike emission assumes that spike trains follow a Poisson process at a ﬁxed mean rate, given a particular state of the system.