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J Neurophysiol (September 19, 2007). doi:10.1152/jn.00414.2007
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Submitted on April 11, 2007
Accepted on September 17, 2007

The dendrite-to-soma input/output function of continuous time-varying signals in hippocampal CA1 pyramidal neurons

Erik P Cook1*, Jennifer A. Guest2, Yong Liang2, Nicolas Y Masse3, and Costa M Colbert2

1 Physiology, McGill University, Montreal, Canada; , Canada
2 Biology and Biochemistry, University of Houston, Houston, Texas, United States
3 Physiology, McGill University, Montreal, Canada

* To whom correspondence should be addressed. E-mail: erik.cook{at}mcgill.ca.

We examined how hippocamal CA1 neurons process complex time-varying inputs that dendrites are likely to receive in vivo. We propose a functional model of the dendrite-to-soma input/output relationship that combines temporal integration and static-gain control mechanisms. Using simultaneous dual whole-cell recordings, we injected fifty seconds of subthreshold and suprathreshold zero-mean white-noise current into the primary dendritic trunk along the proximal two-thirds of stratum radiatum and measured the membrane potential at the soma. Applying a nonlinear system-identification analysis we found that a cascade of a linear filter followed by an adapting static-gain term fully accounted for the non-spiking input/output relationship between the dendrite and soma. The estimated filters contained a prominent bandpass region in the 1 to 10 Hz frequency range that remained constant as a function of stimulus variance. The gain of the dendrite-to-soma input/output relationship, in contrast, varied as a function of stimulus variance. When the contribution of the voltage-dependent current Ih was eliminated, the estimated filters lost their bandpass properties and the gain regulation was substantially altered. Our findings suggest that the dendrite-to-soma input/output relationship for proximal apical inputs to CA1 pyramidal neurons is well described as a bandpass filter in the theta frequency range followed by a gain-control nonlinearity that dynamically adapts to the statistics of the input signal.




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