bmtool 0.5.9.9

BMTool

bmtool

A collection of modules to make developing Neuron and BMTK models easier.

Table of Contents

• Getting Started
• CLI
• Single Cell
• Synapses
• Connectors
• Bmplot
• Graphs

Getting Started

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

CLI

Many of modules available can be accesed using the command line

> 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
>

Single Cell Module

• Passive properties
• Current injection
• FI curve
• ZAP
• Tuner
• VHalf Segregation

The single cell module can take any neuron HOC object and calculate passive properties, run a current clamp, calculate FI curve, or run a ZAP. The module is designed to work with HOC template files and can also turn Allen database SWC and json files into HOC objects and use those. The examples below uses "Cell_Cf" which is the name of a HOC templated loaded by the profiler.

First step is it initialize the profiler.

from bmtool.singlecell import Profiler
profiler = Profiler(template_dir='templates', mechanism_dir = 'mechanisms', dt=0.1)

Can provide any single cell module with either name of Hoc template or a HOC object. If you are wanted to use Allen database SWC and json files you can use the following function

from bmtool.singlecell import load_allen_database_cells
cell = load_allen_database_cells(path_to_SWC_file,path_to_json_file)

Passive properties

Calculates the passive properties(V-rest, Rin and tau) of a HOC object

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

Current clamp

Runs a current clamp on a HOC object

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

FI curve

Calculates the frequency vs current injection plot for a HOC object

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

ZAP

Runs a ZAP on a HOC object

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

Single Cell Tuning

From a BMTK Model directory containing a simulation_config.json file:

bmtool util cell tune --builder

For non-BMTK cell tuning:

bmtool util cell --template TemplateFile.hoc --mod-folder ./ tune --builder

VHalf Segregation Module

Based on the Alturki et al. (2016) paper.

Segregate your channel activation for an easier time tuning your cells.

> 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.

Examples

Wizard Mode (Interactive)

> 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)

Command Mode (Non-interactive)

bmtool util cell --template CA3PyramidalCell vhseg --othersec dend[0],dend[1] --infvars inf_im --segvars gbar_im --gleak gl_ichan2CA3 --eleak el_ichan2CA3

Example:

Simple models can utilize

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

Synapses Module

-SynapticTuner

SynapticTuner - Aids in the tuning of synapses by printing out synaptic properties and giving the user sliders in a Jupyter notebook to tune the synapse. For more info view the example here

Connectors Module

• UnidirectionConnector
• ReciprocalConnector
• CorrelatedGapJunction
• OneToOneSequentialConnector

This module contains helper functions and classes that work with BMTK's NetworkBuilder module in building networks. It facilitates building reciprocal connections, distance dependent connections, afferent connections, etc. See documentation inside the script connectors.py for more notes on usage.

All connector example below use the following network node structure

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')

Unidirectional connector - Object for building unidirectional connections in bmtk network model with given probability within a single population (or between two populations).

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())

Recipical connector - Object for building connections in bmtk network model with reciprocal probability within a single population (or between two populations)

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())

CorrelatedGapJunction - Object for building gap junction connections in bmtk network model with given probabilities within a single population which could be correlated with the recurrent chemical synapses in this population.

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())

OneToOneSequentialConnector - Object for building one to one correspondence connections in bmtk network model with between two populations. One of the population can consist of multiple sub-populations.

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 Module

for a demo please see the notebook here

• total_connection_matrix
• percent_connection_matrix
• connector_percent_matrix
• convergence_connection_matrix
• divergence_connection_matrix
• gap_junction_matrix
• connection_distance
• connection_histogram
• plot_3d_positions
• plot_3d_cell_rotation

total_connection_matrix

Generates a table of total number of connections each neuron population recieves

percent_connection_matrix

Generates a table of the percent connectivity of neuron populations.Method can change if you want the table to be total percent connectivity, only unidirectional connectivity or only bi directional connectvity

connector_percent_matrix

Generates a table of the percent connectivity using the output from bmtool.connector. By default will generate the percent connectivity of the possible connections meaning factoring in distance rules.

convergence_connection_matrix

Generates a table of the mean convergence of neuron populations. Method can be changed to show max, min, mean, or std for convergence a cell recieves

divergence_connection_matrix

Generates a table of the mean divergence of neuron populations. Method can be changed to show max, min, mean or std divergence a cell recieves.

gap_junction_matrix

While gap junctions can be include in the above plots, you can use this function to only view gap junctions. Method can be either 'convergence' or 'percent' connections to generate different plots

connection_distance

Generates a 3d plot with the source and target cells location along with a histogram showing connection distance

connection_histogram

Generates a histogram of the distribution of connections a population of cells give to individual cells of another population

plot_3d_positions

Generates a plot of cells positions in 3D space

plot_3d_cell_rotation

Generates a plot of cells location in 3D plot and also the cells rotation

Plot Connection Diagram

bmplot.plot_network_graph(config='config.json',sources='LA',targets='LA',tids='pop_name',sids='pop_name',no_prepend_pop=True)

Graphs Module

• Generate graph
• Plot Graph
• Connectioon table

Generate Graph

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'}

Plot Graph

Generates an interactive plot showing nodes, edges and # of connections

graphs.plot_graph(Graph)

Generate graph connection table

Generates a CSV of all cells and the number of connections each individual cell receives

import pandas as pd
graphs.export_node_connections_to_csv(Graph, 'node_connections.csv')
df = pd.read_csv('node_connections.csv')
df.head()

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	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