Generalized LIF Models

The Allen Cell Types Database contains Generalized Leaky Integrate and Fire (GLIF) models that simulate the firing behavior of neurons at five levels of complexity. Review the GLIF technical white paper for details on these models and how their parameters were optimized.

The Allen SDK GLIF simulation module is an explicit time-stepping simulator that evolves a neuron’s simulated voltage over the course of an input current stimulus. The module also tracks the neuron’s simulated spike threshold and registers action potentials whenever voltage surpasses threshold. Action potentials initiate reset rules that update voltage, threshold, and (optionally) trigger afterspike currents.

The GLIF simulator in this package has a modular architecture that enables users to choose from a number of dynamics and reset rules that update the simulation’s voltage, spike threshold, and afterspike currents during the simulation. The GLIF package contains a built-in set of rules, however developers can plug in custom rule implementations provided they follow a simple argument specification scheme.

The Allen SDK GLIF simulator was developed and tested with Python 2.7.9, installed as part of Anaconda Python distribution version 2.1.0.

The rest of this page provides examples demonstrating how to download models, examples of simulating these models, and general GLIF model documentation.

Note

the GLIF simulator module is still under heavy development and may change significantly in the future.

Downloading GLIF Models

There are two ways to download files necessary to run a GLIF model. The first way is to visit http://celltypes.brain-map.org and find cells that have GLIF models available for download. The electrophysiology details page for a cell has a neuronal model download link. Specifically:

  1. Click ‘More Options +’ and filter for GLIF models.
  2. Click the electrophysiology thumbnail for a cell on the right hand panel.
  3. Choose a GLIF model from the ‘Show model responses’ dropdown.
  4. Scroll down to the model response click ‘Download model’.

One such link (for a simple LIF neuronal model, ID 566302806), would look like this:

http://api.brain-map.org/neuronal_model/download/566302806

This link returns .zip archive containing the neuron configuration file and sweep metadata required to simulate the model with stimuli applied to the cell. Specifically, the .zip archive will contain:

  • 472423251_neuron_config.json: JSON config file for the GLIF model
  • ephys_sweeps.json: JSON with metadata for sweeps presented to the cell
  • neuronal_model.json: JSON with general metadata for the cell

If you would like to reproduce the model traces seen in the Cell Types Database, you can download an NWB file containing both the stimulus and cell response traces via a ‘Download data’ link on the cell’s electrophysiology page. See the NWB description section for more details on the NWB file format.

You can also download all of these files, including the cell’s NWB file, using the GlifApi class:

from allensdk.api.queries.glif_api import GlifApi
from allensdk.core.cell_types_cache import CellTypesCache
import allensdk.core.json_utilities as json_utilities

neuronal_model_id = 566302806

# download model metadata
glif_api = GlifApi()
nm = glif_api.get_neuronal_models_by_id([neuronal_model_id])[0]

# download the model configuration file
nc = glif_api.get_neuron_configs([neuronal_model_id])[neuronal_model_id]
neuron_config = glif_api.get_neuron_configs([neuronal_model_id])
json_utilities.write('neuron_config.json', neuron_config)

# download information about the cell
ctc = CellTypesCache()
ctc.get_ephys_data(nm['specimen_id'], file_name='stimulus.nwb')
ctc.get_ephys_sweeps(nm['specimen_id'], file_name='ephys_sweeps.json')


Running a GLIF Simulation

To run a GLIF simulation, the most important file you you need is the neuron_config JSON file. You can use this file to instantiate a simulator and feed in your own stimulus:

import allensdk.core.json_utilities as json_utilities
from allensdk.model.glif.glif_neuron import GlifNeuron

# initialize the neuron
neuron_config = json_utilities.read('neuron_config.json')
neuron = GlifNeuron.from_dict(neuron_config)

# make a short square pulse. stimulus units should be in Amps.
stimulus = [ 0.0 ] * 100 + [ 10e-9 ] * 100 + [ 0.0 ] * 100

# important! set the neuron's dt value for your stimulus in seconds
neuron.dt = 5e-6

# simulate the neuron
output = neuron.run(stimulus)

voltage = output['voltage']
threshold = output['threshold']
spike_times = output['interpolated_spike_times']

Note

The GLIF simulator does not simulate during action potentials. Instead it inserts NaN values for a fixed number of time steps when voltage surpasses threshold. The simulator skips neuron.spike_cut_length time steps after voltage surpasses threshold.

To reproduce the model’s traces displayed on the Allen Cell Types Database web page, the Allen SDK provides the allensdk.core.model.glif.simulate_neuron module for simulating all sweeps presented to a cell and storing them in the NWB format:

import allensdk.core.json_utilities as json_utilities

from allensdk.model.glif.glif_neuron import GlifNeuron
from allensdk.model.glif.simulate_neuron import simulate_neuron

neuron_config = json_utilities.read('neuron_config.json')
ephys_sweeps = json_utilities.read('ephys_sweeps.json')
ephys_file_name = 'stimulus.nwb'

neuron = GlifNeuron.from_dict(neuron_config)

sweep_numbers = [ s['sweep_number'] for s in ephys_sweeps 
                  if s['stimulus_units'] == 'Amps' ]
simulate_neuron(neuron, sweep_numbers, ephys_file_name, ephys_file_name, 0.05)

Warning

These stimuli are sampled at a very high resolution (200kHz), and a given cell can have many sweeps. This process can take over an hour.

The simulate_neuron function call simulates all sweeps in the NWB file. Because the same NWB file is being used for both input and output, the cell’s response traces will be overwritten as stimuli are simulated. simulate_neuron optionally accepts a value which will be used to overwrite these NaN values generated during action potentials (in this case 0.05 Volts).

If you would like to run a single sweep instead of all sweeps, try the following:

import allensdk.core.json_utilities as json_utilities
from allensdk.model.glif.glif_neuron import GlifNeuron
from allensdk.core.nwb_data_set import NwbDataSet

neuron_config = json_utilities.read('neuron_config.json')
ephys_sweeps = json_utilities.read('ephys_sweeps.json')
ephys_file_name = 'stimulus.nwb'

# pull out the stimulus for the current-clamp first sweep
ephys_sweep = next( s for s in ephys_sweeps 
                    if s['stimulus_units'] == 'Amps' )
ds = NwbDataSet(ephys_file_name)
data = ds.get_sweep(ephys_sweep['sweep_number']) 
stimulus = data['stimulus']

# initialize the neuron
# important! update the neuron's dt for your stimulus
neuron = GlifNeuron.from_dict(neuron_config)
neuron.dt = 1.0 / data['sampling_rate']

# simulate the neuron
output = neuron.run(stimulus)

voltage = output['voltage']
threshold = output['threshold']
spike_times = output['interpolated_spike_times']

Note

The dt value provided in the downloadable GLIF neuron configuration files does not correspond to the sampling rate of the original stimulus. Stimuli were subsampled and filtered for parameter optimization. Be sure to overwrite the neuron’s dt with the correct sampling rate.

If you would like to plot the outputs of this simulation using numpy and matplotlib, try:

import numpy as np
import matplotlib.pyplot as plt

voltage = output['voltage']
threshold = output['threshold']
interpolated_spike_times = output['interpolated_spike_times']
spike_times = output['interpolated_spike_times']
interpolated_spike_voltages = output['interpolated_spike_voltage']
interpolated_spike_thresholds = output['interpolated_spike_threshold']
grid_spike_indices = output['spike_time_steps']
grid_spike_times = output['grid_spike_times']
after_spike_currents = output['AScurrents']

# create a time array for plotting
time = np.arange(len(stimulus))*neuron.dt

plt.figure(figsize=(10, 10))

# plot stimulus
plt.subplot(3,1,1)
plt.plot(time, stimulus)
plt.xlabel('time (s)')
plt.ylabel('current (A)')
plt.title('Stimulus')

# plot model output
plt.subplot(3,1,2)
plt.plot(time,  voltage, label='voltage')
plt.plot(time,  threshold, label='threshold')

if grid_spike_indices is not None:
    plt.plot(interpolated_spike_times, interpolated_spike_voltages, 'x', 
             label='interpolated spike')

    plt.plot((grid_spike_indices-1)*neuron.dt, voltage[grid_spike_indices-1], '.', 
             label='last step before spike')

plt.xlabel('time (s)')
plt.ylabel('voltage (V)')
plt.legend(loc=3)
plt.title('Model Response')

# plot after spike currents
plt.subplot(3,1,3)
for ii in range(np.shape(after_spike_currents)[1]):
    plt.plot(time, after_spike_currents[:,ii])
plt.xlabel('time (s)')
plt.ylabel('current (A)')
plt.title('After Spike Currents')

plt.tight_layout()
plt.show()

Note

There both interpolated spike times and grid spike times. The grid spike is the first time step where the voltage is higher than the threshold. Note that if you try to plot the voltage at the grid spike indices the output will be NaN. The interpolated spike is the calculated intersection of the threshold and voltage between the time steps.

GLIF Configuration

Instances of the GlifNeuron class require many parameters for initialization. Fixed neuron parameters are stored directly as properties on the class instance:

Parameter Description Units Type
El resting potential Volts float
dt time duration of each simulation step seconds float
R_input input resistance Ohms float
C capacitance Farads float
asc_vector afterspike current coefficients Amps np.array
spike_cut_length spike duration time steps int
th_inf instantaneous threshold Volts float
th_adapt adapted threshold Volts float

Some of these fixed parameters were optimized to fit Allen Cell Types Database electrophysiology data. Optimized coefficients for these parameters are stored by name in the neuron.coeffs dictionary. For more details on which parameters were optimized, please see the technical white paper.

The GlifNeuron class has six methods that can be customized: three rules for updating voltage, threshold, and afterspike currents during the simulation; and three rules for updating those values when a spike is detected (voltage surpasses threshold).

Method Type Description
voltage_dynamics_method Update simulation voltage for the next time step.
threshold_dynamics_method Update simulation threshold for the next time step.
AScurrent_dynamics_method Update afterspike current coefficients for the next time step.
voltage_reset_method Reset simulation voltage after a spike occurs.
threshold_reset_method Reset simulation threshold after a spike occurs.
AScurrent_reset_method Reset afterspike current coefficients after a spike occurs.

The GLIF neuron configuration files available from the Allen Brain Atlas API use built-in methods, however you can supply your own custom method if you like:

# define your own custom voltage reset rule 
# this one linearly scales the input voltage
def custom_voltage_reset_rule(neuron, voltage_t0, custom_param_a, custom_param_b):
    return custom_param_a * voltage_t0 + custom_param_b

# initialize a neuron from a neuron config file
neuron_config = json_utilities.read('neuron_config.json')
neuron = GlifNeuron.from_dict(neuron_config)

# configure a new method and overwrite the neuron's old method
method = neuron.configure_method('custom', custom_voltage_reset_rule, 
                                 { 'custom_param_a': 0.1, 'custom_param_b': 0.0 })
neuron.voltage_reset_method = method

output = neuron.run(stimulus)

Notice that the function is allowed to take custom parameters (here custom_param_a and custom_param_b), which are configured on method initialization from a dictionary. For more details, see the documentation for the GlifNeuron and GlifNeuronMethod classes.

Built-in Dynamics Rules

The job of a dynamics rule is to describe how the simulator should update the voltage, spike threshold, and afterspike currents of the simulator at a given simulation time step.

Voltage Dynamics Rules

These methods update the output voltage of the simulation. They all expect a voltage, afterspike current vector, and current injection value to be passed in by the GlifNeuron. All other function parameters must be fixed using the GlifNeuronMethod class. They all return an updated voltage value.

Threshold Dynamics Rules

These methods update the spike threshold of the simulation. They all expect the current threshold and voltage values of the simulation to be passed in by the GlifNeuron. All other function parameters must be fixed using the GlifNeuronMethod class. They all return an updated threshold value.

Afterspike Current Dynamics Rules

These methods expect current afterspike current coefficients, current time step, and time steps of all previous spikes to be passed in by the GlifNeuron. All other function parameters must be fixed using the GlifNeuronMethod class. They all return an updated afterspike current array.

Built-in Reset Rules

The job of a reset rule is to describe how the simulator should update the voltage, spike threshold, and afterspike currents of the simulator after the simulator has detected that the simulated voltage has surpassed threshold.

Voltage Reset Rules

These methods update the output voltage of the simulation after voltage has surpassed threshold. They all expect a voltageto be passed in by the GlifNeuron. All other function parameters must be fixed using the GlifNeuronMethod class. They all return an updated voltage value.

Threshold Reset Rules

These methods update the spike threshold of the simulation after a spike has been detected. They all expect the current threshold and the reset voltage value of the simulation to be passed in by the GlifNeuron. All other function parameters must be fixed using the GlifNeuronMethod class. They all return an updated threshold value.

Afterspike Reset Reset Rules

These methods expect current afterspike current coefficients to be passed in by the GlifNeuron. All other function parameters must be fixed using the GlifNeuronMethod class. They all return an updated afterspike current array.