A collection of modules to make developing Neuron and BMTK models easier.
Installation
pip install bmtool
For developers who will be pulling down additional updates to this repository regularly use the following instead.
git clone https://github.com/cyneuro/bmtool.git
cd bmtool
python setup.py develop
Then download updates (from this directory) with
git pull
> cd your_bmtk_model_directory
> bmtool
Usage: bmtool [OPTIONS] COMMAND [ARGS]...
Options:
--verbose Verbose printing
--help Show this message and exit.
Commands:
debug
plot
util
>
> bmtool plot
Usage: bmtool plot [OPTIONS] COMMAND [ARGS]...
Options:
--config PATH Configuration file to use, default: "simulation_config.json"
--no-display When set there will be no plot displayed, useful for saving
plots
--help Show this message and exit.
Commands:
connection Display information related to neuron connections
positions Plot cell positions for a given set of populations
raster Plot the spike raster for a given population
report Plot the specified report using BMTK's default report plotter
>
from bmtool.singlecell import Profiler
profiler = Profiler(template_dir='templates', mechanism_dir = 'mechanisms', dt=0.1)
from bmtool.singlecell import load_allen_database_cells
cell = load_allen_database_cells(path_to_SWC_file,path_to_json_file)
from bmtool.singlecell import Passive,run_and_plot
import matplotlib.pyplot as plt
sim = Passive('Cell_Cf', inj_amp=-100., inj_delay=1500., inj_dur=1000., tstop=2500., method='exp2')
title = 'Passive Cell Current Injection'
xlabel = 'Time (ms)'
ylabel = 'Membrane Potential (mV)'
X, Y = run_and_plot(sim, title, xlabel, ylabel, plot_injection_only=True)
plt.gca().plot(*sim.double_exponential_fit(), 'r:', label='double exponential fit')
plt.legend()
plt.show()
Injection location: Cell_Cf[0].soma[0](0.5)
Recording: Cell_Cf[0].soma[0](0.5)._ref_v
Running simulation for passive properties...
V Rest: -70.21 (mV)
Resistance: 128.67 (MOhms)
Membrane time constant: 55.29 (ms)
V_rest Calculation: Voltage taken at time 1500.0 (ms) is
-70.21 (mV)
R_in Calculation: dV/dI = (v_final-v_rest)/(i_final-i_start)
(-83.08 - (-70.21)) / (-0.1 - 0)
12.87 (mV) / 0.1 (nA) = 128.67 (MOhms)
Tau Calculation: Fit a double exponential curve to the membrane potential response
f(t) = a0 + a1*exp(-t/tau1) + a2*exp(-t/tau2)
Constained by initial value: f(0) = a0 + a1 + a2 = v_rest
Fit parameters: (a0, a1, a2, tau1, tau2) = (-83.06, -3306.48, 3319.33, 55.29, 55.15)
Membrane time constant is determined from the slowest exponential term: 55.29 (ms)
Sag potential: v_sag = v_peak - v_final = -0.66 (mV)
Normalized sag potential: v_sag / (v_peak - v_rest) = 0.049
from bmtool.singlecell import CurrentClamp
sim = CurrentClamp('Cell_Cf', inj_amp=350., inj_delay=1500., inj_dur=1000., tstop=3000., threshold=-15.)
X, Y = run_and_plot(sim, title='Current Injection', xlabel='Time (ms)',
ylabel='Membrane Potential (mV)', plot_injection_only=True)
plt.show()
Injection location: Cell_Cf[1].soma[0](0.5)
Recording: Cell_Cf[1].soma[0](0.5)._ref_v
Current clamp simulation running...
Number of spikes: 19
from bmtool.singlecell import FI
sim = FI('Cell_Cf', i_start=0., i_stop=1000., i_increment=50., tstart=1500.,threshold=-15.)
X, Y = run_and_plot(sim, title='FI Curve', xlabel='Injection (nA)', ylabel='# Spikes')
plt.show()
Injection location: Cell_Cf[21].soma[0](0.5)
Recording: Cell_Cf[21].soma[0](0.5)._ref_v
Running simulations for FI curve...
Results
Injection (nA): 0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95
Number of spikes: 0, 1, 10, 12, 15, 16, 17, 19, 20, 20, 21, 21, 22, 23, 23, 24, 25, 25, 26, 27
from bmtool.singlecell import ZAP
sim = ZAP('Cell_Cf')
X, Y = run_and_plot(sim)
plt.show()
Injection location: Cell_Cf[22].soma[0](0.5)
Recording: Cell_Cf[22].soma[0](0.5)._ref_v
ZAP current simulation running...
Chirp current injection with frequency changing from 0 to 15 Hz over 15 seconds
Impedance is calculated as the ratio of FFT amplitude of membrane voltage to FFT amplitude of chirp current
simulation_config.json file:bmtool util cell tune --builder
bmtool util cell --template TemplateFile.hoc --mod-folder ./ tune --builder
> bmtool util cell vhseg --help
Usage: bmtool util cell vhseg [OPTIONS]
Alturki et al. (2016) V1/2 Automated Segregation Interface, simplify
tuning by separating channel activation
Options:
--title TEXT
--tstop INTEGER
--outhoc TEXT Specify the file you want the modified cell template
written to
--outfolder TEXT Specify the directory you want the modified cell
template and mod files written to (default: _seg)
--outappend Append out instead of overwriting (default: False)
--debug Print all debug statements
--fminpa INTEGER Starting FI Curve amps (default: 0)
--fmaxpa INTEGER Ending FI Curve amps (default: 1000)
--fincrement INTEGER Increment the FI Curve amps by supplied pA (default:
100)
--infvars TEXT Specify the inf variables to plot, skips the wizard.
(Comma separated, eg: inf_mech,minf_mech2,ninf_mech2)
--segvars TEXT Specify the segregation variables to globally set,
skips the wizard. (Comma separated, eg:
mseg_mech,nseg_mech2)
--eleak TEXT Specify the eleak var manually
--gleak TEXT Specify the gleak var manually
--othersec TEXT Specify other sections that a window should be
generated for (Comma separated, eg: dend[0],dend[1])
--help Show this message and exit.
> bmtool util cell vhseg
? Select a cell: CA3PyramidalCell
Using section dend[0]
? Show other sections? (default: No) Yes
? Select other sections (space bar to select): done (2 selections)
? Select inf variables to plot (space bar to select): done (5 selections)
? Select segregation variables [OR VARIABLES YOU WANT TO CHANGE ON ALL SEGMENTS at the same time] (space bar to select): done (2 selections)
bmtool util cell --template CA3PyramidalCell vhseg --othersec dend[0],dend[1] --infvars inf_im --segvars gbar_im --gleak gl_ichan2CA3 --eleak el_ichan2CA3
Example:
bmtool util cell --hoc cell_template.hoc vhsegbuild --build
bmtool util cell --hoc segmented_template.hoc vhsegbuild
ex: https://github.com/tjbanks/two-cell-hco
connectors.py for more notes on usage.from bmtk.builder import NetworkBuilder
net = NetworkBuilder('example_net')
net.add_nodes(N=100, pop_name='PopA',model_type = 'biophysical')
net.add_nodes(N=100, pop_name='PopB',model_type = 'biophysical')
background = NetworkBuilder('background')
background.add_nodes(N=300,pop_name='tON',potential='exc',model_type='virtual')
from bmtool.connectors import UnidirectionConnector
connector = UnidirectionConnector(p=0.15, n_syn=1)
connector.setup_nodes(source=net.nodes(pop_name = 'PopA'), target=net.nodes(pop_name = 'PopB'))
net.add_edges(**connector.edge_params())
from bmtool.connectors import ReciprocalConnector
connector = ReciprocalConnector(p0=0.15, pr=0.06767705087, n_syn0=1, n_syn1=1,estimate_rho=False)
connector.setup_nodes(source=net.nodes(pop_name = 'PopA'), target=net.nodes(pop_name = 'PopA'))
net.add_edges(**connector.edge_params())
from bmtool.connectors import ReciprocalConnector, CorrelatedGapJunction
connector = ReciprocalConnector(p0=0.15, pr=0.06, n_syn0=1, n_syn1=1, estimate_rho=False)
connector.setup_nodes(source=net.nodes(pop_name='PopA'), target=net.nodes(pop_name='PopA'))
net.add_edges(**connector.edge_params())
gap_junc = CorrelatedGapJunction(p_non=0.1228,p_uni=0.56,p_rec=1,connector=connector)
gap_junc.setup_nodes(source=net.nodes(pop_name='PopA'), target=net.nodes(pop_name='PopA'))
conn = net.add_edges(is_gap_junction=True, syn_weight=0.0000495, target_sections=None,afferent_section_id=0, afferent_section_pos=0.5,
**gap_junc.edge_params())
from bmtool.connectors import OneToOneSequentialConnector
connector = OneToOneSequentialConnector()
connector.setup_nodes(source=background.nodes(), target=net.nodes(pop_name = 'PopA'))
net.add_edges(**connector.edge_params())
connector.setup_nodes(target=net.nodes(pop_name = 'PopB'))
net.add_edges(**connector.edge_params())
bmplot.plot_network_graph(config='config.json',sources='LA',targets='LA',tids='pop_name',sids='pop_name',no_prepend_pop=True)
from bmtool import graphs
import networkx as nx
Graph = graphs.generate_graph(config='config.json',source='LA',target='LA')
print("Number of nodes:", Graph.number_of_nodes())
print("Number of edges:", Graph.number_of_edges())
print("Node labels:", set(nx.get_node_attributes(Graph, 'label').values()))
Number of nodes: 2000
Number of edges: 84235
Node labels: {'SOM', 'PNc', 'PNa', 'PV'}
graphs.plot_graph(Graph)
import pandas as pd
graphs.export_node_connections_to_csv(Graph, 'node_connections.csv')
df = pd.read_csv('node_connections.csv')
df.head()
| Unnamed: 0 | Node Label | PNc Connections | PV Connections | SOM Connections | PNa Connections | |
|---|---|---|---|---|---|---|
| 0 | 0 | PNa | 15 | 11 | 9 | 6 |
| 1 | 1 | PNa | 24 | 25 | 6 | 21 |
| 2 | 2 | PNa | 27 | 28 | 12 | 25 |
| 3 | 3 | PNa | 19 | 27 | 15 | 35 |
| 4 | 4 | PNa | 25 | 11 | 8 | 16 |
FAQs
BMTool
We found that bmtool demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
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