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Nervous systems tune themselves to the statistical structure of the stimuli they encounter. This sensitivity to statistics appears in phenomena ranging over many timescales, from the adaptation of vision to a rapid change in light level to the loss of ability to distinguish the sounds of non-native languages. While multiple neural mechanisms contribute to this on-line learning of stimulus distributions, we show that the intrinsic nonlinearities of single neurons provide them with the ability to represent time-varying stimuli optimally. While such sensitivity to stimulus statistics does not require learning, slower timescales of adaptation are consistent with optimal inference of statistical parameters of the changing stimulus ensemble.
Adrienne Fairhall has a training in statistical physics from the Australian National University and the Weizmann Institute of Science in Israel. In her postdoctoral work, she moved into the area of computational neuroscience, working first in the fly visual system with Bill Bialek at NEC and then in the retina, with Michael Berry at Princeton. She has been a faculty member of the Department of Physiology and Biophysics at the University of Washington in Seattle since 2004.
Nervous sytem, Neural networks (Neurobiology), Neural circuitry -- Statistical aspects, Neurons -- Physiology
Neurology | Neurosciences
Fairhall, Adrienne, "Optimality in Neural Adaptation" (2011). Systems Science Friday Noon Seminar Series. 58.