Permanent URL to this publication: http://dx.doi.org/10.5167/uzh-17647
Köndgen, H; Geisler, C; Fusi, S; Wang, X J; Lüscher, H R; Giugliano, M (2008). The dynamical response properties of neocortical neurons to temporally modulated noisy inputs in vitro. Cerebral Cortex, 18(9):2086-2097.
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Cortical neurons are often classified by current-frequency relationship. Such a static description is inadequate to interpret neuronal responses to time-varying stimuli. Theoretical studies suggested that single-cell dynamical response properties are necessary to interpret ensemble responses to fast input transients. Further, it was shown that input-noise linearizes and boosts the response bandwidth, and that the interplay between the barrage of noisy synaptic currents and the spike-initiation mechanisms determine the dynamical properties of the firing rate. To test these model predictions, we estimated the linear response properties of layer 5 pyramidal cells by injecting a superposition of a small-amplitude sinusoidal wave and a background noise. We characterized the evoked firing probability across many stimulation trials and a range of oscillation frequencies (1-1000 Hz), quantifying response amplitude and phase-shift while changing noise statistics. We found that neurons track unexpectedly fast transients, as their response amplitude has no attenuation up to 200 Hz. This cut-off frequency is higher than the limits set by passive membrane properties (approximately 50 Hz) and average firing rate (approximately 20 Hz) and is not affected by the rate of change of the input. Finally, above 200 Hz, the response amplitude decays as a power-law with an exponent that is independent of voltage fluctuations induced by the background noise.
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|Item Type:||Journal Article, refereed, original work|
|Communities & Collections:||07 Faculty of Science > Institute of Neuroinformatics|
|Dewey Decimal Classification:||570 Life sciences; biology|
|Deposited On:||08 Mar 2009 12:25|
|Last Modified:||05 Apr 2016 13:10|
|Publisher:||Oxford University Press|
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