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

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

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/

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