INPUT SUMMATION BY CULTURED PYRAMIDAL NEURONS IS LINEAR AND POSITION-INDEPENDENT

Authors
Citation
S. Cash et R. Yuste, INPUT SUMMATION BY CULTURED PYRAMIDAL NEURONS IS LINEAR AND POSITION-INDEPENDENT, The Journal of neuroscience, 18(1), 1998, pp. 10-15
Citations number
36
Categorie Soggetti
Neurosciences
Journal title
ISSN journal
02706474
Volume
18
Issue
1
Year of publication
1998
Pages
10 - 15
Database
ISI
SICI code
0270-6474(1998)18:1<10:ISBCPN>2.0.ZU;2-F
Abstract
The role of dendritic morphology in integration and processing of neur onal inputs is still unknown. Models based on passive cable theory sug gest that dendrites serve to isolate synapses from one another. Becaus e of decreases in driving force or resistance, two inputs onto the sam e dendrite would diminish their joint effect, resulting in sublinear s ummation. When on different dendrites, however, inputs would not inter act and therefore would sum linearly. These predictions have not been rigorously tested experimentally. In addition, recent results indicate that dendrites have voltage-sensitive conductances and are not passiv e cables. To investigate input integration, we characterized the effec ts of dendritic morphology on the summation of subthreshold excitatory inputs on cultured hippocampal neurons with pyramidal morphologies. W e used microiontophoresis of glutamate to systematically position inpu ts throughout the dendritic tree and tested the summation of two input s by measuring their individual and joint effects. We find that summat ion was surprisingly linear regardless of input position. For small in puts, this linearity arose because no significant shunts or changes in driving force occurred and no voltage-dependent channels were opened. Larger inputs also added linearly, but this linearity was caused by b alanced action of NMDA and I-A potassium conductances. Therefore, acti ve conductances can maintain, paradoxically, a linear input arithmetic . Furthermore, dendritic morphology does not interfere with this linea rity, which may be essential for particular neuronal computations.