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Noise-driven adaptation:



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Neurocomputing 52 54 2003 877 883 www elsevier com locate neucom Noise driven adaptation in vitro and mathematical analysis Liam Paninski 1 Brian Lau Alex Reyes Center for Neural Science New York University 4 Washington Place New York NY 10003 USA Abstract Variance adaptation processes have recently been examined in cells of the y visual system and various vertebrate preparations To better understand the contributions of somatic mechanisms to this kind of adaptation we recorded intracellularly in vitro from neurons of rat sensorimotor cortex The cells were stimulated with a noise current whose standard deviation was varied parametrically We observed systematic variance dependent adaptation de2ned as a scaling of a nonlinear transfer function similar in many respects to the e3ects observed in vivo The fact that similar adaptive phenomena are seen in such di3erent preparations led us to investigate a simple model of stochastic stimulus driven neural activity The simplest such model the leaky integrate and 2re LIF cell driven by noise current permits us to analytically compute many quantities relevant to our observations on adaptation We show that the LIF model displays adaptive behavior which is quite similar to the e3ects observed in vivo and in vitro c 2003 Elsevier Science B V All rights reserved Keywords Adaptation Noise Integrate and 2re Fokker Planck It is widely understood that sensory neurons adapt to the prevailing statistics of their inputs 10 Fairhall et al 5 recently reported one such adaptation process in the y visual system they described a motion sensitive neuron that appears to scale its input output function to adapt its 2ring rate to the variance of the observed motion signal However the mechanisms underlying this type of contrast dependent adaptation are unknown speci2cally it is unclear whether the observed phenomena arise from network This work was supported by NSF Grant IBN 0079619 LP and BL are supported by HHMI and NDSEG predoctoral fellowships



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