CA1 network with biophysical neurons

README file for Shuman et al, 2019 Breakdown of spatial coding and interneuron synchronization in epileptic mice. Nature Neuroscience

UPDATED: March 20, 2021: all scripts are running under python 3.

for more information, refer to the comments inside the scripts or contact me in: chavlis [DOT] spiros [AT] gmail [DOT] com

Scripts' authors: S. Chavlis, PhD, I. Pandi, M.Sc.

INPUT CREATION

First you have to create the Inputs, go in make_inputs_linear_track directory

cd make_inputs_linear_track

In a command line execute

python make_grid_like_inputs.py <run_number>
python sp_make_place_inputs.py <run_number>
python glim_shuf_new_noisy.py <run_number> <desynch_level> <jitter_source>

Arguments:

• <run_number> is a specific run from one edge of the track to the other. To replicate the figures one needs 10 runs
• <desynch_level>: ms of desynchronization
• <jitter_source>: which input to randomize. Valid options: EC or CA3. Use EC to replicate the paper figures.

e.g.,

python glim_v2_prelearning.py 1 20 EC

Then enter background_noise directory

cd ../background_noise

create the background noise by executing

python poisson_input.py <total_number_of_runs> <poisson_rate>  # e.g., python poisson_input.py 1 5 --> creates run1 poisson random noise with lambda 5 Hz

MAIN SIMULATIONS

return to main directory

cd ../

Compile all mechanisms (mod files)

nrnivmodl mechanisms/

Run the simulation

./x86_64/special -nogui -nopython -c n_runs=<run_number> -c n_trials=<virtual_mouse_id> -c desynch=<desynch_level_in_ms> -c n_neuron=<deletion_type> -c factor=<reduction_factor> Network_CA1.hoc

e.g.,

./x86_64/special -nogui -nopython -c nruns=1 -c ntrials=1 -c n_neuron=0 -c desynch=0 -c factor=1 Network_CA1.hoc

To replicate the results of the paper you need 10 runs/trial and 10 trials and all possible deletions (see below)

Valid deletions:

• Control: All connections and cells, default -- option: 0
• SOMred: SOMs are removed by a specific percentage -- option: 1
• PVred: PVs are removed by a specific percentage -- option: 2
• Desynch: Desynchronization of EC/CA3 inputs by a specific amount -- option: 3
• ALL: SOMred, PVred and Desynch simultaneously -- option: 4
• SOMdel: All PVs are removed -- option: 5
• PVdel: All PVs are removed -- option: 6

** Notice: it is highly recommended to run all inputs first, and then run the simulations. Output of the simulation is saved into Simulation_Results/

ANALYSIS OF LOCOMOTION DATA BEFORE PROCEEDING

First, one needs to extract the spiketimes for neurons in order to analyze them Navigate to AnalysisRawData directory

cd AnalysisRawData

Exctract spike times

python spiketimes_analysis.py <neuron_type> <deletion_type> <number_of_trial> <number_of_run>

Valid deletions_types:

• Control
• SOMred
• PVred
• Desynch
• ALL
• SOMdel
• PVdel

Valid <neuron_type> values:

• _pvsoma_ : Pyramidal cells
• _aacell_ : Axoaxonic cells
• _bcell_ : Basket cells
• _bscell_ : Bistratified cells
• _olmcell_ : OLM cells
• _vipcck_ : VIP/CCK cells
• _vipcr_ : VIP/CR PVM cells
• _vipcrnvm_ : VIP/CR NVM cells

After the analysis for all trials, runs and deletions execute:

python all_path_all_spiketimes.py <deletion_type> # e.g., python all_path_all_spiketimes.py Control

This will create the subfolder final_results/metrics_permutations, where the spiketimes and the path for all cases is stored (for better handling)

Permutations for all cells to find spatial information and stability null distributions

python permutations_analysis_peyman.py <virtual_mouse> <pyramidalID> <deletion_type>

Data save for using in GraphPad Prism and basic plotting

python analysis_path.py <virtual_mouse> <deletion_type>
python all_trials_paper_all.py
python all_trials_per_animal.py

For more information, refer to the comments inside the scripts or contact me in: chavlis [DOT] spiros [AT] gmail [DOT] com