Computational analysis of NN activity and spatial reach of sharp wave-ripples (Canakci et al 2017)

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Network oscillations of different frequencies, durations and amplitudes are hypothesized to coordinate information processing and transfer across brain areas. Among these oscillations, hippocampal sharp wave-ripple complexes (SPW-Rs) are one of the most prominent. SPW-Rs occurring in the hippocampus are suggested to play essential roles in memory consolidation as well as information transfer to the neocortex. To-date, most of the knowledge about SPW-Rs comes from experimental studies averaging responses from neuronal populations monitored by conventional microelectrodes. In this work, we investigate spatiotemporal characteristics of SPW-Rs and how microelectrode size and distance influence SPW-R recordings using a biophysical model of hippocampus. We also explore contributions from neuronal spikes and synaptic potentials to SPW-Rs based on two different types of network activity. Our study suggests that neuronal spikes from pyramidal cells contribute significantly to ripples while high amplitude sharp waves mainly arise from synaptic activity. Our simulations on spatial reach of SPW-Rs show that the amplitudes of sharp waves and ripples exhibit a steep decrease with distance from the network and this effect is more prominent for smaller area electrodes. Furthermore, the amplitude of the signal decreases strongly with increasing electrode surface area as a result of averaging. The relative decrease is more pronounced when the recording electrode is closer to the source of the activity. Through simulations of field potentials across a high-density microelectrode array, we demonstrate the importance of finding the ideal spatial resolution for capturing SPW-Rs with great sensitivity. Our work provides insights on contributions from spikes and synaptic potentials to SPW-Rs and describes the effect of measurement configuration on LFPs to guide experimental studies towards improved SPW-R recordings.
1 . Canakci S, Toy MF, Inci AF, Liu X, Kuzum D (2017) Computational analysis of network activity and spatial reach of sharp wave-ripples. PLoS One 12:e0184542 [PubMed]
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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): Hippocampus CA1 pyramidal GLU cell; Hippocampus CA1 basket cell;
Channel(s): I Na,t; I A; I K; I h;
Gap Junctions: Gap junctions;
Receptor(s): NMDA; GabaA; Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Oscillations; Spatio-temporal Activity Patterns;
Implementer(s): Canakci, Sadullah [scanakci at]; Inci, Ahmet F [afinci at sabanciuniv,edu]; Toy, Faruk [faruk.toy at]; Liu, Xin [xil432 at]; Kuzum, Duygu [dkuzum at];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell; GabaA; NMDA; Glutamate; I Na,t; I A; I K; I h;
"Computational Analysis of Network Activity and Spatial Reach of Sharp
Wave-Ripples" README file


This model creates the hippocampal model in:

Canakci S, Inci AF, Toy MF, Liu X, and Kuzum D. "Computational
Analysis of Network Activity and Spatial Reach of Sharp
Wave-Ripples". PLOS ONE 2017.

ModelDB Accession Number 230861


This model is derived from earlier model published in:

Fink CG, Gliske S, Catoni N, and Stacey WC. "Network mechanisms
generating abnormal and normal hippocampal high-frequency
oscillations: A computational analysis." eNeuro 2015,

ModelDB Accession Number 182134

To run the simulation

1. Should be run in the normal (serial) installation of NEURON 7.3.
Most of the code is actually written for parallel implementation, but
several functions were added that are not able to run in parallel.

2. Unzip the zip file, keeping the file structure intact (see below).

3. To compile the mod files, (in Windows) cd to the mod folder, and
execute the command mknrndll. Move the resulting file (nrnmech.dll),
to the root directory. (in Linux) execute "nrnivmod mod" at top level
folder, then "nrngui simulation.hoc".

4. Run simulation.hoc.

5. The model can generate simulated sharp waves (as in Figs. 2 and 3)
by running the simulation for a single point electrode. The model
simulates micro electrode arrays and larger electrode sizes (as in
Figs. 5 and 6) by uncommenting the for loops at the end of
simulation.hoc file. Electrode sizes in x axis and y axis ans also
spatial difference between point electrode are determined inside the
for loops.

Data files are written in data folder. extraactive file includes LFP
from active pyramidal cells and extraantenna file includes LFP from
inactive pyramidal cells. extra file includes LFP from all
neurons. When simulation is run for multiple electrodes, LFPs of
different electrodes are written in concatenated form(one followed by
another) in the files ending with _SUM.

We set pyrspiketau_vec=0.1, pyr_nospike_tau=1.0, and
baskspike_tau=bask_nospike_tau=6.0 in all of the simulations excepts
results in Fig. 2 d,e and f. In Fig. 2 d,e and f, pyrspiketau_vec=
pyr_nospike_tau=1.0, and baskspike_tau=0.60, and
bask_nospike_tau=6.0. We set the number of antenna cells (in Fig. 3)
in manycells.par file inside the parameters folders. We set the
distance of electrode to the network in the first argument of setelec
function inside simulation.hoc file. We set baskconnvector=100 which
means basket cells make synapses with 100% of pyramidal cells.
For other simulation details, please see the documentation
accompanying the earlier model (ModelDB accession numbers 182134).