Before running any simulations, please compile NEURON modules by the command
nrnivmodl lmods modsLaunch the dLGNnetwork.py script with or without
additional arguments that alter parameters to run a simulation. The
complete list of command line arguments is given below.
To replicate the full simulations for Figure 2 on an HPC computer,
use SLURM script SLURM-scripts/converegence.sbatch
cp SLURM-scripts/converegence.sbatch .
sbatch -a 1-9 converegence.sbatchJust to plot results from the cumulative database, run
convergence-reader.py with the database. For NMDA+AMPA
mixture
python convergence-reader.py -l homeostasis-G-CONV-TFR0.50-SIGMAXX-NMDA.json For AMPA only
python convergence-reader.py -l homeostasis-G-CONV-TFR0.50-SIGMAXX-AMPA.json To replicate full simulation for Figure 3 on HPC computer use SLURM
script SLURM-scripts/trn-scan.sbatch
cp SLURM-scripts/trn-scan.sbatch .
sbatch -a 0-65 trn-scan.sbatchTo plot results from the cumulative database, run
trn-scan-reader.py with the database. For NMDA+AMPA
mixture
python trn-scan-reader.py -l homeostasis-G-CONV-TRN-TFR0.50-NMDA.json -M 2.3To replicate the full simulations for Figure 4 on an HPC computer, use SLURM script ’SLURM-scripts/dLGNnetwork-as-cortical-input.sbatch` and one of two setting files. Because overall simulation takes about 300 days on 16 nodes, 40 core in each node, we used Monte-Carlo sampling, which runs in two batches
for i in SLURM-scripts/dLGNnetwork-as-cortical-input.sbatch SLURM-scripts/homeostasis-G-CONV-TRN-CXT-TFR1.00-Nxx-Cxxx-20220816-100511.sbatch SLURM-scripts/homeostasis-G-CONV-TRN-CXT-TFR1.00-Nxx-Cxxx-20220816-100545.sbatch
do
cp $i .
done
sbatch -a 0-15 dLGNnetwork-as-cortical-input.sbatch homeostasis-G-CONV-TRN-CXT-TFR1.00-Nxx-Cxxx-20220816-100511.sbatch
sbatch -a 0-15 dLGNnetwork-as-cortical-input.sbatch homeostasis-G-CONV-TRN-CXT-TFR1.00-Nxx-Cxxx-20220816-100545.sbatchTo plot results from the cumulative database, run
cxt-trn-motecarlo-reader.py with the database.
python cxt-trn-motecarlo-reader.py -l homeostasis-G-CONV-TRN-CXT-TFR1.00-vX.json --cor-minmax 0.6 -I| File or directory | Description |
|---|---|
| dLGNnetwork.py | The main script to run simulations for Figure 2, 4, and 5 |
| dLGNanalysis.py | collection of tools to analyze simulation results |
| dLGNgraphs.py | graphical unities |
| simtoolkit | module for support manipulation with model parameters and storage for bulky results (should be on GitHub and PyPI soon) |
| mods | NEURON modules for TC neuron |
| lmods | NEURON modules for synapses and vector activation |
| SLURM-scripts | scripts to run simulations on HPC |
| convergence-reader.py | reader for results of converegence.sbatch script |
| homeostasis-G-CONV-TFR0.50-SIGMAXX-NMDA.json | database with cumulative results of NMDA+AMPA run |
| homeostasis-G-CONV-TFR0.50-SIGMAXX-AMPA.json | database with cumulative results of AMPA only run |
| trn-scan-reader.py | reader for results of trn-scan.sbatch script |
| homeostasis-G-CONV-TRN-TFR0.50-NMDA.json | database with cumulative results of TRN run |
| cxt-trn-motecarlo-reader.py | reader for results of dLGNnetwork-as-cortical-input.sbatch script |
| homeostasis-G-CONV-TRN-CXT-TFR1.00-vX.json | database with cumulative results of CTX-TRN run |
$python dLGNnetwork.py --help
dLGN network desynchronization at P7-P10
Ruben Tikidji-Hamburyan GWU 2020-2022
Usage: python dLGNnetwork.py [options] [parameters]
Any model parameter can be altered by /parameter_name=parameter_value in the command line
Options:
-o RD, --stkdb-record=RD
Record the simulation in database (default
dLGNnetwork.stkdb)
-m RM, --stkdb-message=RM
Supply database record with a massage. Useful when run
scripts (default None)
-c, --stkdb-cmd Add end command line to the data base (doesn't change
model hash sum)'
-L LG, --log=LG Output log to the file (default on the screen)
-l LL, --log-level=LL
Level of logging may be CRITICAL, ERROR, WARNING,
INFO, or DEBUG (default INFO)
-h, --help Print this help
Parameters:
/network/nprms/db = 'P07-selected-checked-gmin.json'
: file with neuron parameter database
/network/nprms/lst = 'models'
: variable with list of parameters
/network/nprms/gmin = 'gsynmin'
: variable with minimal conductance
/network/nprms/threshold = 0.
: spike threshold
/network/geom/shape = 'H'
: H-hexagonal or S-square
/network/geom/x = 7
: number of columns
/network/geom/y = 16
: number of rows
/network/annoying_hetero = True
: If set True, it guarantees that all neurons in the population are different. For this
: option, one needs a database bigger than network size.
/network/gj/geom/p = 0.3
: gap junction probability
/network/gj/geom/unit = 'n'
: if 'n' - /network/gj/geom/p probability of connection per neuron. if 'c' -
: /network/gj/geom/p is probability of connection per pear of neurons within maxd
: distance if 'o' - /network/gj/geom/p is average number of GJ per neuron.
/network/gj/geom/maxd = 1.5
: gap junction max distance
/network/gj/geom/mind = None
: gap junction min distance
/network/gj/geom/dir = None
: gap junction direction (random if None)
/network/gj/r = 600.
: gap junction resistance. 2022/02/04: For current neuron database this resistance
: produces spikelets with dV = 1.7 +- 0.22 mV and absolute range for all neurons
: [0.95,2.8] mV, Coupling coefficients mean = 0.4+-0.077 range=[0.087,0.58] aren't
: Compatible with VBN coupling Lee et al. 2010, but spikelet amplitude is similar to
: spikelets in cat LGN Hughes et al. 2002
/network/syn/gsyn = None
: synaptic conductance: if positive - set for everyone, if negative - set the
: portion of minimal synaptic conductance for a neuron, if None set to 1/3 of the
: minimal synaptic conductance for each neuron.
/network/syn/rand = False
: If ture - gsyn randomly init between 0 and /syn/gsyn value
/network/syn/prenorm = True
: If true the total synaptic conductance will be set to /syn/gsyn value for each
: neuron in the **beginning**! It is active only in model building. For dynamical
: normalization see /network/btdp
/network/syn/geom/unit = 'N'
: Pattern of synaptic connectivity:
: 'o' - one-to-one creates only one connection for each rGC to a closest LGN cell.
: 'a' - all-to-all connections: each rGC connects all dLGN cells. This is the best configuration for btdp.
: You can use /network/syn/sig2gsyn to create a Gaussian distributions of weights
: 'e' - exponential distribution uses /network/syn/geom/pmax for maximum probability and
: /network/syn/geom/sig for sigma.
: 'E' - same as e but distance computed from the closets rGC
: 'g' - Gaussian distribution. it uses /network/syn/geom/pmax for maximum probability and
: /network/syn/geom/sig for sigma.
: 'G' - same as g but distance computed from the closets rGC
: 'r' - random distribution
: 'n' - random number between /network/syn/geom/nmin and /network/syn/geom/nmax for each TC is generated.
: rGC are picked randomly
: 'c' - connect to closets (but not one) rGC
: 'N' - same as n butcloset rGC are picked
: ADD HERE MORE IF NEEDED!
/network/syn/geom/pmax = None
: peak of synaptic probability for exponential or Gaussian distributions.
/network/syn/geom/sig = None
: sigma for exponential or Gaussian distributions.
/network/syn/geom/nmin = 7
: minimum of rGC connection per TC
/network/syn/geom/nmax = 20
: maximum of rGC connection per TC
/network/syn/sig2gsyn = None
: sigma for create Gaussian distributions of weights with distance
/network/syn/AMPA/p = 1
: portion of AMPA conductance
/network/syn/AMPA/tau1 = 1.
: AMPA rising time constant
/network/syn/AMPA/tau2 = 2.2
: AMPA falling time contant From Chen & Regehr 2000 Table 1 P10-P14 Inconsistent with
: Hauser et al. 2014 AMPA $\tau_d \approx 5.47$ ms
/network/syn/NMDA/p = 2.25
: portion of NMDA conductance /AMPA/p and /NMDA/p are computed from Shah & Crair 2008
: as followed: For _P6–P7_ : $i_{AMPA}/i_{NMDA} = \beta_{P7} =0.78 \pm 0.09$ (peak-to-
: peak) comparable with $\beta_{P10} = 0.5 \pm 0.1$ from Chen & Regehr 2000 for
: voltage clamp $i_{vl}=g_{vl}*(E-V_{vl})$ where $E=0$ reversal potential for NMDA and
: AMPA, and $v_{VL}$ potential for voltage clamp. $g_{vl}=i_{vl}/v_{vl}$ conductance
: $\frac{g_{AMPA}}{g_{NMDA}}=\frac{i_{AMPA}}{i_{NMDA}}
: \frac{\Delat_{NMDA}}{\Delat_{AMPA}}$, where $\Delat_{NMDA}$ and $\Delat_{AMPA}$ are
: differences between holding potentials and the reversal potential for NMDA and AMPA
: currents $\frac{\Delat_{NMDA}}{\Delat_{AMPA}}$ = 40 mV / 70 mV = 0.57
: $\frac{g_{NMDA}}{g_{AMPA}} = \frac{1}{0.57 \beta_{P7}} \approx 2.25$ Note that Dilger
: et al. 2015 estimated $\beta_{P10} \approx 1$ which give /NMDA/p=1/0.57$\approx
: 1.75$. The value from from Chen & Regehr 2000 $\beta_{P10} = 0.5 \pm 0.1$ gives
: /NMDA/p=1/(0.57*0.5)$\approx 3.5$.
/network/syn/NMDA/tau1 = 1.
: NMDA rising time constant
/network/syn/NMDA/tau2 = 150.
: NMDA falling time contant From Chen & Regehr 2000 Table 1 P10-P14 Consistent with
: Dilger et al 2015
/network/syn/ppr_u0 = 0.3
: sets presynaptic single spike depression it's adjusted to obtain paired-pulse ratio
: 0.73 From Chen & Regehr 2000 Table 1 P10-P14
/network/syn/toneExc = None
: Conductance for tonic excitation (None to block)
/network/syn/toneInh = None
: Conductance for GABA_B receptors (None to block)
/network/file = 'dLGN-network.stkdata'
: save/read network configuration to/from this file
/network/load = True
: read network from the file (if True) or from another file (if filename given)
/network/save = True
: save network to the file (if True) or to another file (if filename given)
/network/reload/nprms = False
: sets specific record within the file from there neuron parameters will be reread or
: the first one if True
/network/reload/gjcon = False
: sets specific record within the file from there gap-junction connections list will
: be reread or the first one if True
/network/reload/syncon = False
: sets specific record within the file from there list of synaptic connections will
: be reread or the first one if True
/network/reload/gsyncond = False
: sets specific record within the file from there list of synaptic conductance will be
: reread or the first one if True
/network/reset/nprms = False
: reset neuron parameters
/network/reset/gjcon = False
: resets gap junction connections
/network/reset/syncon = False
: resets list of synaptic connections
/network/reset/gsyncond = False
: resets list of synaptic conductances
/network/annoying_reload/nprms = True
: If True and neuron parameters hash doesn't match it stops
/network/annoying_reload/gjcon = False
: If True and gap junction connection list hash doesn't match it stops
/network/annoying_reload/syncon = True
: If True and synaptic connection hash doesn't match it stops
/network/annoying_reload/gsyncond = False
: If True and synaptic conductance list hash doesn't match it stops
/network/annoying_reload/btdp = False
: If True and /btdp/continue and /record/file does not have gsyn with correct hash - it
: stops
/network/btdp/enable = False
: Set it to true for activation btdp
/network/btdp/update = 5000.
: update synaptic weights every # milliseconds. if btdp is enable it resets
: /sim/record/interval
/network/btdp/normgsyn = None
: Target level to which ....
/network/btdp/normn = 4
: strongest normn synapses are normalized, suppressing weaker synapses.
/network/btdp/synthr = -0.05
: the threshold below with synapse assumed to be silent same as for /syn/normg :
: positive sets the absolute level for the threshold, negative sets a relative to
: minimal synaptic conductance for each neuron threshold and None sets all synapses
: active
/network/btdp/minact = @/network/btdp/normn@
: threshold of minimal number of active connections if the number of active synapses is
: below this number - it creates new random synapses. **Note** if
: /network/btdp/synthr=None, this mechanism is disabled.
/network/btdp/frthr = 0.1
: if max firing rate at given interval /network/btdp/update for any pre or postsyn
: below this value, correlations term of leaning rule will not computed. **Note** if
: you set it in None, False, or below zero, correlation term will be compute always -
: and two silent neurons will have very high correlation and ramp up connections.
/network/btdp/frkernel = 25.
: kernel for smoothing firing-rate. Effectively, gaussian kernel low-pass frequencies 4
: time lower than kernel size. Butts et al 1999, computed minimal jitter to perturb
: spacial information around 100 ms, so ~25ms kernel should be OK.
/network/btdp/frsmwidth = @/network/btdp/frkernel@*10.
: one-side width of smoothing vector for firing-rate
/network/btdp/continue = True
: reads last update from record and reset weights
/network/home/enable = False
: Set it to true to activate homeostasis
/network/home/update = 120000.
: update synaptic weights every # milliseconds. if btdp is enable it will be set to
: /sim/btdp/update if btdp is not enable, /home/update resets /sim/record/interval
/network/home/tau = 60000.
: homeostasis time constant in ms ~10 sec
/network/home/target_fr = 0.5
: Target firing rate in spikes/sec from Murata & Colonnese 2018 Figure 5G1, it should
: be ~ 1 spike/sec, but with blocked cortex it decreases ~50% Murata & Colonnese 2016
: Fig 3.
/network/home/slope = 1.
: sloe slope of activation function is spikes/sec
/network/home/gmax = -2.
: maximal synaptic conductance (notation is the same as in /network/syn/gsyn)
/network/home/gmin = 0.0
: minimal synaptic conductance
/network/home/delta = -0.2/111
: increment/decrement of synaptic weights
/network/home/continue = True
: reads last update from the record and reset weights
/network/cx/enable = False
: Set it to true for enabling **cortical feedback**
/network/cx/delay = 15.
: delay for spike travel to cortex and back
/network/cx/gsyn = -0.005
: synaptic conductance. ATTENTION! If negative it set ratio to mean RGC input
/network/cx/AMPA/p = @/network/syn/AMPA/p@
: portion of AMPA conductance
/network/cx/AMPA/tau1 = @/network/syn/AMPA/tau1@
: AMPA rising time constant
/network/cx/AMPA/tau2 = @/network/syn/AMPA/tau2@
: AMPA falling time constant
/network/cx/NMDA/p = @/network/syn/NMDA/p@
: portion of NMDA conductance
/network/cx/NMDA/tau1 = @/network/syn/NMDA/tau1@
: NMDA rising time constant
/network/cx/NMDA/tau2 = @/network/syn/NMDA/tau2@
: NMDA falling time constant
/network/cx/ppr_u0 = 0.7
: sets presynaptic PP depression
/network/trn/enable = False
: Set it to true for enabling **TRN inhibitory feedback**
/network/trn/delay = 15.
: delay for spike travel to TRN and back
/network/trn/gsyn = -0.005
: synaptic conductance (same as /syn/gsyn)
/network/trn/tau1 = 5.
: GABA A rising time constant
/network/trn/tau2 = 50.
: GABA A falling time constant
/network/trn/e = -70.
: inhibitory reversal potential
/block/gjcon = False
: Block gap junction
/block/syncon = False
: Block synapses
/sim/temperature = None
: model temperature in celsius, if None it will try to find 'temperature' variable in
: the neuron database
/sim/Tmax = 600000.
: Simulation duration in ms. If negative (any negative number) it defines by
: stimulation recording
/sim/record/interval = 60000
: record and clean buffers every /sim/record/interval milliseconds
/sim/record/file = 'dLGN-record.stkdata'
: save everything into this file
/sim/record/save = True
: enable any recordings
/sim/record/spike = True
: record spikes
/sim/record/gsyn = False
: if a number records synaptic weights every n updates, if true records synaptic
: weights every update. Works only if btdp or homeostasis are enabled
/sim/record/cont/dt = 0.5
: time step for continues recordings
/sim/record/cont/cur = False
: record currents
/sim/record/cont/volt = True
: record voltages
/sim/record/cont/meancur = True
: record current averaged through population
/sim/parallel = True
: sent number of cores or True for auto-detection
/sim/Belly = False
: ring the bell when simulation finishes
/sim/timestamp = time.strftime("%Y%m%d-%H%M%S")
: simulation time-stamp
/stim/iapp = None
: inject constant current. Current is slowly ramping up first 1000 ms
/stim/rGC/file = "../experimentaldata/Maccione2014_P9_29March11_control_SpkTs_bursts_filtered.h5"
: reads positions and spikes of RG from this file
/stim/rGC/groups = [ 824,825,826,827,828,829,830,831, 849,850,851,852,853,854,855,856, 875,876,877,878,879,880,881,882, 905,906,907,908,909,910 ]
: recorded rGC IDs which will be used for network stimulation if more than one neuron
: on one electrode spike can be lumped together by putting IDs into tuple
: (121,122,123) for example
/stim/trecstart = 140000
: remove first milliseconds of the recording.
/stim/shuffle = False
: Shuffles spikes between electrodes
/stim/uniform = False
: uniform distribution of the same number of spikes over each electrode
/stim/stimfile = None
: used npy file with array (n,2) : [spike time,rGC] instead of spikes in the original
: hd5 file
/stim/poisson = None
: Generate Poisson firing rate with given rate
/FR/window = 100
: windows size for filtering firing rate
/FR/kernel = 50.
: Gaussian kernel to smooth FR
/FR/kernelwidth = @/FR/kernel@*5
: 1/2 width of smoothing kernel (it is half, because +- boundary). It is set to 5 sigma
: If /kernel and /kernelwidthare gaussian kernel is used to smooth 1ms bin histogram,
: otherwise histogram with /FR/window will be plotted
/FR/CorrDist/positive = 20.
: Size of positive Gaussian kernel to smooth firing rate for computing Correlation
: distribution
/FR/CorrDist/negative = @/FR/CorrDist/positive@*4
: size of negative Gaussian kernel
/FR/CorrDist/window = @/FR/CorrDist/negative@*5
: 1/2 of smoothing window size
/FR/CorrDist/bins = False
: set a number of bins in correlation distribution (must be int); default 201
/FR/CorrDist/left = False
: left boundary of histogram range; default -1.
/FR/CorrDist/right = False
: right boundary of histogram range; default 1.
/FR/Spectrum/filter-off = False
: set False to remove filtration
/FR/Spectrum/Fmax = 39.
: Maximal frequency (Hz)
/FR/Spectrum/dt = 1.
: Histogram bin-size in ms
/FR/Spectrum/kernel = 20.
: Gaussian kernel to smooth FR (different to from the above to have high frequency
: components)
/FR/Spectrum/width = @/FR/Spectrum/kernel@*5
: 5 sigma for 1/2 of the window (n /FR/Spectrum/dt time units)
/FR/Overall = True
: computes overall FR for a run. If string is given, it jsones the list of overall FR
: into a file with this name.
/Figures/enable = True
: if False will not generate figures and exit it will make a DB record only if -m
: message is provided and the record won't have figures
/Figures/X-term = False
: saves all figures into a file with this prefix. If False - shows on screen
/Figures/FigSize = (21,16)
: XxY size of figures
/Figures/FigLimit = None
: If any of /sim/record/cont/{cur,volt} is set True, will show every # neuron, and all
: if None
/Figures/STKDB-Record = True
: Add figures into record
/Figures/connectivity/flat = False
: if True makes 2D plot of connectivity instead of 3D
/Figures/disable/connectivity = False
: set True to disable connectivity plot
/Figures/disable/volt = False
: set True to disable voltage plot
/Figures/disable/cur = False
: set True to disable current plot
/Figures/disable/cordist = False
: set True to disable correlation distribution plot
/Figures/disable/spectrum = False
: set True to disable spectrum plot
/Figures/disable/2dspiking = True
: set False to activate interactive figure which can generates movies
/Figures/formats = 'jpg svg'.split()
: list of figures formats
/analysis/connectivity = True
: statistics of RGC to LGN connectivity
/analysis/gj = True
: statistics of number GJ per neuron