Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons
Section snippets
Detailed biophysical models
Computational models were based on morphologically reconstructed neocortical pyramidal cells from cat parietal cortex (one from layer II–III, two from layer V and one from layer VI), which were obtained from two previous studies (Douglas et al., 1991, Contreras et al., 1997). The cell primarily used here is depicted in Fig. 1 (top). The passive properties were adjusted by matching the model to intracellular recordings obtained in the absence of synaptic activity (Destexhe and Paré, 1999).
Results
We first analyze the properties of background activity in a detailed biophysical model of a neocortical pyramidal cell and decompose this activity into excitatory and inhibitory conductances. We then design a simple model that captures the spectral and amplitude characteristics of these conductances. This model is tested using a single-compartment representation and in real cortical neurons maintained in vitro. In both cases, we show to what extent this point-conductance representation captures
Discussion
We have shown here that it is possible to draw a relatively simple point-conductance representation of synaptic background activity with only two variables and that this model is sufficient to account for several typical properties of neocortical neurons in vivo. We discuss here possible applications and further developments of this model, as well as how it could be tuned using experimental data.
Acknowledgements
Research supported by the Centre National de la Recherche Scientifique (CNRS), the Howard Hughes Medical Institute and the National Institutes of Health.
References (42)
- et al.
The pyramidal neuron of the cerebral cortex: morphological and chemical characteristics of the synaptic inputs
Prog. Neurobiol.
(1992) - et al.
Effects of uniform and non-uniform synaptic ‘activation-distributions’ on the cable properties of modeled cortical pyramidal neurons
Brain Res.
(1989) Neurotransmitter actions in the thalamus and cerebral cortex and their role in neuromodulation of thalamocortical activity
Prog. Neurobiol.
(1992)- et al.
The dynamic clamp: artificial conductances in biological neurons
Trends Neurosci.
(1993) A note on neuronal firing and input variability
J. Theor. Biol.
(1992)Some models of neuronal variability
Biophys. J.
(1967)- et al.
Influence of temporal correlation of synaptic input on the rate and variability of firing in neurons
Biophys. J.
(2000) Motoneuron dendrites: role in synaptic integration
Fed. Proc.
(1975)- et al.
Synaptic background activity influences spatiotemporal integration in single pyramidal cells
Proc. Natl. Acad. Sci. USA
(1991) - et al.
Visual input evokes transient and strong shunting inhibition in visual cortical neurons
Nature
(1998)
Intracellular and computational characterization of the intracortical inhibitory control of synchronized thalamic inputs in vivo
J. Neurophysiol.
The density of synapses and neurones in the motor and visual areas of the cerebral cortex
J. Anat.
Impact of network activity on the integrative properties of neocortical pyramidal neurons in vivo
J. Neurophysiol.
Simplified models of correlated synaptic background activity in neocortical pyramidal neurons
Soc. Neurosci. Abstr.
An intracellular analysis of the visual responses of neurones in cat visual cortex
J. Physiol.
Temporal patterns of discharge of pyramidal tract neurons during sleep and waking in the monkey
J. Neurophysiol.
Dynamic clamp of cortical neurons in vitro simulates in vivo activity patterns
Soc. Neurosci. Abstr.
The frequency dependence of spike timing reliability in cortical pyramidal cells and interneurons
J. Neurophysiol.,
Impact of correlated inputs on the output of the integrate-and-fire model
Neural Comput.
Time structure of the activity in neural network models
Phys. Rev. E
Cited by (504)
In-silico testing of new pharmacology for restoring inhibition and human cortical function in depression
2024, Communications BiologyCortical cell assemblies and their underlying connectivity: An in silico study
2024, PLoS Computational BiologyShared input and recurrency in neural networks for metabolically efficient information transmission
2024, PLoS Computational Biology