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Long time windows from theta modulated inhib. in entorhinal–hippo. loop (Cutsuridis & Poirazi 2015)
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<html><pre> A COMPUTATIONAL STUDY ON HOW THETA MODULATED INHIBITION CAN ACCOUNT FOR THE LONG TEMPORAL WINDOWS IN THE ENTORHINAL-HIPPOCAMPAL LOOP --------------------------------------------------------------------- The NEURON code implements a DG-CA3-CA1 network model (100 DG GCs, 2 DG MCs, 2 DG BCs, 1 HC, 100 CA3 PCs, 2 CA3 BCs, 1 CA3 AAC, 1 CA3 OLM cell, 100 CA1 PCs, 1 CA1 AAC, 2 CA1 BCs, and 1 CA1 BSC) on how theta modulated inhibition accounts for the long temporal windows in the entorhinal-hippocampal loop. Reference: Cutsuridis V*, Poirazi P. (2015). A computational study on how theta modulated inhibition can account for the long temporal delays in the entorhinal-hippocampal loop. Neurobiology of Learning and Memory, 120: 69-83. Abstract: A recent experimental study (Mizuseki, Sirota, Pastalkova, & Buzsaki, 2009) has shown that the temporal delays between population activities in successive entorhinal and hippocampal anatomical stages are longer (about 70-80 ms) than expected from axon conduction velocities and passive synaptic integration of feed-forward excitatory inputs. We investigate via computer simulations the mechanisms that give rise to such long temporal delays in the hippocampus structures. A model of the dentate gyrus (DG), CA3 and CA1 microcircuits is presented that uses biophysical representations of the major cell types including granule cells, CA3 and CA1 pyramidal cells (PCs) and six types of interneurons: basket cells (BCs), axo-axonic cells (AACs), bistratified cells (BSCs), oriens lacunosum-moleculare cells (OLMs), mossy cells (MCs) and hilar perforant path associated cells (HC). Inputs to the network came from the entorhinal cortex (EC) (layers 2 and 3) and the medial septum (MS). The model simulates accurately the timing of firing of different hippocampal cells with respect to the theta rhythm. The model shows that the experimentally reported long temporal delays in the DG, CA3 and CA1 hippocampal regions are due to theta modulated somatic and axonic inhibition. The model further predicts that the phase at which the CA1 PCs fire with respect to the theta rhythm is determined primarily by their increased dendritic excitability caused by the decrease of the axial resistance and the A-type K+ conductance along their dendritic trunk. The model predicted latencies by which the DG, CA3 and CA1 principal cells fire are inline with the experimental evidence. Finally, the model proposes functional roles for the different inhibitory interneurons in the retrieval of the memory pattern by the DG, CA3 and CA1 networks. The model makes a number of predictions, which can be tested experimentally, thus leading to a better understanding of the biophysical computations in the hippocampus. Main file: mosinit.hoc This file is configured to produce the results presented in figures 5-9 of the paper, showing the temporal relationships of voltage traces of the network's cells when the network is stimulated by EC-L2, EC-L3 and medial septum (MS) inputs. Example results are in the Results directory, in which there are also Matlab files for plotting figure 11, an example pattern recall when the EC-L2 input to DG-GCs is cueing the pattern, EC-L2 and EC-L3 inputs are present to drive the inhibitory interneurons, but they are disconnected from the CA3-PCs and CA1-PCs, respectively, so that recall is purely due to the EC-L2 input cue. Usage: First compile the mod files (nrnivmodl on unix/linux, mknrndll on mswin or mac os x). Then start with the command "nrngui mosinit.hoc" (unix/linux), or by double clicking the mosinit.hoc (mswin), or dragging and dropping mosinit.hoc onto the nrngui icon (mac os x). After the simulation starts it takes about a minute to setup the network, then you can run the default simulation (takes just under 20 minutes) or explore the model with the nrngui. When running the model, hide the graphs for faster run times. An example graph generated: <img src="./screenshot.png" alt="screenshot" width="550"> is similar to Fig7D. Changelog: 20150524 Update from Ted Carnevale: Changed integration method from euler to derivimplicit which is appropriate for simple ion accumulation mechanisms. See Integration methods for SOLVE statements http://www.neuron.yale.edu/phpBB/viewtopic.php?f=28&t=592 20150525 Update from Ted Carnevale: Fixed ca initialization by inserting cai = ca into INITIAL block in cad.mod 20220523 Updated MOD files to contain valid C++ and be compatible with the upcoming versions 8.2 and 9.0 of NEURON. </pre></html>
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