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Dendritic Integration (was: high resolution intracellular recordings?)

yan king yin y.k.y at lycos.com
Mon Nov 24 10:31:01 EST 2003


The following is relevant to this, I don't have a copy of
the paper yet..

==================

Nature Reviews Neuroscience 4, 739 -751 (2003); doi:10.1038/nrn1198

THE HIGH-CONDUCTANCE STATE OF NEOCORTICAL NEURONS IN VIVO  

Alain Destexhe, Michael Rudolph & Denis Paré

Preface
Intracellular recordings in vivo have shown that neocortical neurons
are subjected to an intense synaptic bombardment in intact networks
and are in a 'high-conductance' state. In vitro studies have shed
light on the complex interplay between the active properties of
dendrites and how they convey discrete synaptic inputs to the soma.
Computational models have attempted to tie these results together and
predicted that high-conductance states profoundly alter the
integrative properties of cortical neurons, providing them with a
number of computational advantages. Here, we summarize results from
these different approaches, with the aim of understanding the
integrative properties of neocortical neurons in the intact brain.

Summary

Neocortical neurons in vivo are subjected to intense synaptic
bombardment, leading to a 'high-conductance state' that differs
markedly from the conditions measured in cortical slices in vitro.
During barbiturate anaesthesia, as well as in slices, neuronal
activity is greatly reduced compared with states of activated or
desynchronized electroencephalogram (EEG) activity, such as in awake
animals. During EEG-activated states, intracellular recordings show a
depolarized and fluctuating membrane potential, a low input resistance
and high levels of spontaneous firing. In slices, cells have a high
input resistance, are hyperpolarized and show little spontaneous
activity.
Active dendritic properties such as the ability to generate and
propagate action potentials have important implications for the
integration of synaptic inputs. Computational models have been used to
investigate these implications for in vivo processing.
These models predict the following 'computational principles' for
high-conductance states: enhanced responsiveness and gain modulation;
equalization of synaptic efficacies; increased temporal resolution;
and probabilistic and irregular behaviour. By virtue of these
principles, cortical neurons would be tuned to optimally track fine
temporal variations in their synaptic inputs despite their stochastic
nature.
In dynamic-clamp experiments, in vitro electrophysiology is combined
with computational modelling to 'recreate' the characteristics of
high-conductance states in cortical slices, allowing the effects of
the high-conductance state on neuronal responsiveness to be measured
directly.
Such experiments confirm that synaptic noise enhances neuronal
responsiveness and modulates the gain of neurons. They could also be
used to test the predictions that it equalizes synaptic efficacies,
increases temporal resolution and induces probabilistic behaviour.



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