Gamma oscillations in hippocampal interneuron networks (Bartos et al 2002)

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Accession:21329
To examine whether an interneuron network with fast inhibitory synapses can act as a gamma frequency oscillator, we developed an interneuron network model based on experimentally determined properties. In comparison to previous interneuron network models, our model was able to generate oscillatory activity with higher coherence over a broad range of frequencies (20-110 Hz). In this model, high coherence and flexibility in frequency control emerge from the combination of synaptic properties, network structure, and electrical coupling.
Reference:
1 . Bartos M, Vida I, Frotscher M, Meyer A, Monyer H, Geiger JR, Jonas P (2002) Fast synaptic inhibition promotes synchronized gamma oscillations in hippocampal interneuron networks. Proc Natl Acad Sci U S A 99:13222-7 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Hippocampus;
Cell Type(s): Abstract Wang-Buzsaki neuron;
Channel(s): I Na,t; I K;
Gap Junctions: Gap junctions;
Receptor(s): GabaA;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Activity Patterns; Oscillations; Synchronization; Synaptic Integration;
Implementer(s): Jonas, Peter [Peter.Jonas at ist.ac.at];
Search NeuronDB for information about:  GabaA; I Na,t; I K;
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inhibnet
README
gap.mod
nethhwbm.mod
mosinit.hoc
netring.hoc
                            
This model is associated with the paper:

Bartos M, Vida I, Frotscher M, Meyer A, Monyer H, Geiger JR, Jonas P (2002) 
Fast synaptic inhibition promotes synchronized gamma oscillations in 
hippocampal interneuron networks. 
Proc Natl Acad Sci U S A 99:13222-7

Abstract:
Networks of GABAergic interneurons are of critical importance for
the generation of gamma frequency oscillations in the brain. To
examine the underlying synaptic mechanisms, we made paired
recordings from basket cell (BCs) in different subfields of
hippocampal slices, using transgenic mice that express enhanced
green fluorescent protein (EGFP) under the control of the parvalbumin
promoter. Unitary inhibitory postsynaptic currents (IPSCs)
showed large amplitude and fast time course with mean amplitude weighted
decay time constants of 2.5, 1.2, and 1.8 ms in the dentate
gyrus, and the cornu ammonis area 3 (CA3) and 1 (CA1), respectively
(33-34°C). The decay of unitary IPSCs at BC-BC synapses was
significantly faster than that at BC-principal cell synapses, indicating
target cell-specific differences in IPSC kinetics. In addition,
electrical coupling was found in a subset of BC-BC pairs. To
examine whether an interneuron network with fast inhibitory
synapses can act as a gamma frequency oscillator, we developed an
interneuron network model based on experimentally determined
properties. In comparison to previous interneuron network models,
our model was able to generate oscillatory activity with higher
coherence over a broad range of frequencies (20-110 Hz). In this
model, high coherence and flexibility in frequency control emerge
from the combination of synaptic properties, network structure,
and electrical coupling.

Excerpt from the paper describing the network architecture:
...
A structured interneuron network was assembled from 200
neurons arranged on the circumference of a ring with 50 um
spacing, a simple representation that avoids edge effects. Each
neuron was randomly connected to its 100 nearest neighbors by
chemical synapses; the connection probability was given by a
Gaussian function with a standard deviation of 24 cell-cell
distances and an average connection probability of 0.57. This
connectivity was consistent with published anatomical data (ref.
29; see also ref. 12). The conduction time was calculated from the
distance between pre- and postsynaptic cells along the circumference.
... Furthermore, each neuron was randomly connected to its eight 
nearest neighbors by electrical synapses with a connection 
probability of 0.5.
...

see paper for more!

The model files reproduce images similar to those in fig 3 (takes about 
30 seconds on an 800 MHz IBM compatible). To generate fig 3A(B) set 
Imu=1(5) in netring.hoc and run nrngui mosinit.hoc again.
Note: to generate runs as similar to the paper as possible look at tstop 
comment to change tstop and also change dt to 0.01 (as shown in netring.hoc).
(will take longer to run).  The model uses a random number generator so that 
the figures you generate will always be slightly different than the paper.

To run the model auto-launch from ModelDB or download the zip file and extract
and setup in the usual way for your Mac, PC, or Unix system.  This involves
compiling the mechanisms with mknrndll (PC or MAC), or nrnivmodl (unix), and then
run nrngui mosinit.hoc.

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