Causal spike-signal impulse response functions for finite-sized neuronal network models
Project description
LFPykernels
The LFPykernels
package incorporates forward-model based calculations of causal spike-signal
impulse response functions for finite-sized neuronal network models.
Build Status
Citation
If you use this software, please cite it as (change version accordingly):
Hagen, Espen. (2021). LFPykernels (version/git-SHA/git-tag). Zenodo. https://doi.org/10.5281/zenodo.5720619
BibTex format:
@software{hagen_espen_2021_5720619,
author = {Hagen, Espen},
title = {LFPykernels},
month = nov,
year = 2021,
note = {If you use this software, please cite it as below.},
publisher = {Zenodo},
version = {version/git-SHA/git-tag},
doi = {10.5281/zenodo.5720619},
url = {https://doi.org/10.5281/zenodo.5720619}
}
These codes correspond to results shown in the preprint manuscript:
Brain signal predictions from multi-scale networks using a linearized framework
Espen Hagen, Steinn H. Magnusson, Torbjørn V. Ness, Geir Halnes, Pooja N. Babu, Charl Linssen, Abigail Morrison, Gaute T. Einevoll
bioRxiv 2022.02.28.482256; doi: https://doi.org/10.1101/2022.02.28.482256
Bibtex format:
@article {Hagen2022.02.28.482256,
author = {Hagen, Espen and Magnusson, Steinn H. and Ness, Torbjørn V. and Halnes, Geir and Babu, Pooja N. and Linssen, Charl and Morrison, Abigail and Einevoll, Gaute T.},
title = {Brain signal predictions from multi-scale networks using a linearized framework},
elocation-id = {2022.02.28.482256},
year = {2022},
doi = {10.1101/2022.02.28.482256},
publisher = {Cold Spring Harbor Laboratory},
URL = {https://www.biorxiv.org/content/early/2022/03/02/2022.02.28.482256},
eprint = {https://www.biorxiv.org/content/early/2022/03/02/2022.02.28.482256.full.pdf},
journal = {bioRxiv}
}
If you use or refer to this work, please cite it as above.
Adaptations or modifications of this work should comply with the provided LICENSE
file provided with this repository.
Features
The LFPykernels
package incorporates forward-model based calculations of causal spike-signal
impulse response functions for finite-sized neuronal network models.
The signals considered are low-frequency extracellular potentials ("local field potential" - LFP)
or current dipole moments (and by extension EEG and MEG like signals) that are
thought to mainly stem from synaptic currents and associated return currents.
The basic idea is that the effect of any spike event in each presynaptic
population on each signal type can be captured by single linearised multicompartment neuron
models representative of each population and simultaneously accounting for known distributions of
cells and synapses in space, distributions of delays, synaptic currents and associated return currents.
A scientific publication describing the present methodology in detail is planned.
The intended use for filter kernels predicted using LFPykernels
is forward-model based signal predictions
from neuronal network simulation frameworks using simplified neuron representations like leaky integrate-and-fire
point neurons or rate-based neurons.
Let nu_X(t)
describe presynaptic population spike rates in units of spikes/dt
and H_YX(r, tau)
predicted spike-signal kernels for the connections between presynaptic populations X
and
postsynaptic populations Y
the full signal may then be computed via the sum over linear convolutions
V(r, t) = sum_X sum_Y conv(nu_X, H_YX)(r, t)
A more elaborate example combining kernel predictions with a spiking point-neuron network simulation is provided in the example notebook https://github.com/LFPy/LFPykernels/blob/main/examples/LIF_net_forward_model_predictions.ipynb
For questions, please raise an issue at https://github.com/LFPy/LFPykernels/issues.
Usage
Example prediction of kernel function H
mapping spike events of a
presynaptic inhibitory population X=='I'
to extracellular potential contributions by a
postsynaptic excitatory population Y=='E'
(see https://github.com/LFPy/LFPykernels/blob/main/examples/README_example.ipynb):
import matplotlib.pyplot as plt
import scipy.stats as st
import numpy as np
from lfpykernels import GaussCylinderPotential, KernelApprox
import neuron
# recompile mod files if needed
mech_loaded = neuron.load_mechanisms('mod')
if not mech_loaded:
os.system('cd mod && nrnivmodl && cd -')
mech_loaded = neuron.load_mechanisms('mod')
print(f'mechanisms loaded: {mech_loaded}')
# misc parameters
dt = 2**-4 # time resolution (ms)
t_X = 500 # time of synaptic activations (ms)
tau = 50 # duration of impulse response function after onset (ms)
Vrest = -65 # assumed average postsynaptic potential (mV)
X=['E', 'I'] # presynaptic population names
N_X = np.array([8192, 1024]) # presynpatic population sizes
Y = 'E' # postsynaptic population
N_Y = 8192 # postsynaptic population size
C_YX = np.array([0.05, 0.05]) # pairwise connection probability between populations X and Y
nu_X = {'E': 2.5, 'I': 5.0} # assumed spike rates of each population (spikes/s)
g_eff = True # account for changes in passive leak due to persistent synaptic activations
def set_passive(cell, Vrest):
"""Insert passive leak channel across all sections
Parameters
----------
cell: object
LFPy.NetworkCell like object
Vrest: float
Steady state potential
"""
for sec in cell.template.all:
sec.insert('pas')
sec.g_pas = 0.0003 # (S/cm2)
sec.e_pas = Vrest # (mV)
# parameters for LFPy.NetworkCell representative of postsynaptic population
cellParameters={
'templatefile': 'BallAndSticksTemplate.hoc',
'templatename': 'BallAndSticksTemplate',
'custom_fun': [set_passive],
'custom_fun_args': [{'Vrest': Vrest}],
'templateargs': None,
'delete_sections': False,
'morphology': 'BallAndSticks_E.hoc'}
populationParameters={
'radius': 150.0, # population radius (µm)
'loc': 0.0, # average depth of cell bodies (µm)
'scale': 75.0} # standard deviation (µm)
# Predictor for extracellular potentials across depth assuming planar disk source
# elements convolved with Gaussian along z-axis.
# See https://lfpykernels.readthedocs.io/en/latest/#class-gausscylinderpotential for details
probe = GaussCylinderPotential(
cell=None,
z=np.linspace(1000., -200., 13), # depth of contacts (µm)
sigma=0.3, # tissue conductivity (S/m)
R=populationParameters['radius'], #
sigma_z=populationParameters['scale'],
)
# Create KernelApprox object. See https://lfpykernels.readthedocs.io/en/latest/#class-kernelapprox for details
kernel = KernelApprox(
X=X,
Y=Y,
N_X=N_X,
N_Y=N_Y,
C_YX=C_YX,
cellParameters=cellParameters,
populationParameters=populationParameters,
# function and parameters used to estimate average multapse count:
multapseFunction=st.truncnorm,
multapseParameters=[
{'a': (1 - 2.) / .6, 'b': (10 - 2.) / .6, 'loc': 2.0, 'scale': 0.6},
{'a': (1 - 5.) / 1.1, 'b': (10 - 5.) / 1.1, 'loc': 5.0, 'scale': 1.1}],
# function and parameters for delay distribution from connections between a
# population in X onto population Y:
delayFunction=st.truncnorm,
delayParameters=[{'a': -2.2, 'b': np.inf, 'loc': 1.3, 'scale': 0.5},
{'a': -1.5, 'b': np.inf, 'loc': 1.2, 'scale': 0.6}],
# parameters for synapses from connections by populations X onto Y
synapseParameters=[
{'weight': 0.00012, 'syntype': 'Exp2Syn', 'tau1': 0.2, 'tau2': 1.8, 'e': 0.0},
{'weight': 0.002, 'syntype': 'Exp2Syn', 'tau1': 0.1, 'tau2': 9.0, 'e': -80.0}],
# parameters for spatial synaptic connectivity by populations X onto Y
synapsePositionArguments=[
{'section': ['apic', 'dend'],
'fun': [st.norm],
'funargs': [{'loc': 50.0, 'scale': 100.0}],
'funweights': [1.0]},
{'section': ['soma', 'apic', 'dend'],
'fun': [st.norm],
'funargs': [{'loc': -100.0, 'scale': 100.0}],
'funweights': [1.0]}],
# parameters for extrinsic synaptic input
extSynapseParameters={'syntype': 'Exp2Syn', 'weight': 0.0002, 'tau1': 0.2, 'tau2': 1.8, 'e': 0.0},
nu_ext=40., # external activation rate (spikes/s)
n_ext=450, # number of extrinsic synapses
nu_X=nu_X,
)
# make kernel predictions for connection from populations X='I' onto Y='E'
H = kernel.get_kernel(
probes=[probe],
Vrest=Vrest, dt=dt, X='I', t_X=t_X, tau=tau,
g_eff=g_eff)
Physical units
Notes on physical units used in LFPykernels
:
-
There are no explicit checks for physical units
-
Transmembrane currents are assumed to be in units of (nA)
-
Spatial information is assumed to be in units of (µm)
-
Voltages are assumed to be in units of (mV)
-
Extracellular conductivities are assumed to be in units of (S/m)
-
current dipole moments are assumed to be in units of (nA µm)
-
Magnetic fields are assumed to be in units of (nA/µm)
-
Simulation times are assumed to be in units of (ms) with step size ∆t
-
Spike rates are assumed to be in units of (# spikes / ∆t)
Documentation
The online Documentation of LFPykernels
can be found here:
https://lfpykernels.readthedocs.io/en/latest
Dependencies
LFPykernels
is implemented in Python and is written (and continuously tested) for Python >= 3.7
(older versions may or may not work).
The main LFPykernels
module depends on LFPy
(https://github.com/LFPy/LFPy, https://LFPy.readthedocs.io).
Running all unit tests and example files may in addition require py.test
, matplotlib
,
LFPy
.
Installation
From development sources (https://github.com/LFPy/LFPykernels)
Install the current development version on https://GitHub.com using git
(https://git-scm.com):
$ git clone https://github.com/LFPy/LFPykernels.git
$ cd LFPykernels
$ python setup.py install # --user optional
or using pip
:
$ pip install . # --user optional
For active development, link the repository location
$ pip install -e . # --user optional
Installation of stable releases on PyPI.org (https://www.pypi.org)
Installing stable releases from the Python Package Index (https://www.pypi.org/project/lfpykernels):
$ pip install lfpykernels # --user optional
To upgrade the installation using pip:
$ pip install --upgrade --no-deps lfpykernels
Installation of stable releases on conda-forge (https://conda-forge.org)
Installing lfpykernels
from the conda-forge
channel can be achieved by adding conda-forge
to your channels with:
$ conda config --add channels conda-forge
Once the conda-forge
channel has been enabled, lfpykernels
can be installed with:
$ conda install lfpykernels
It is possible to list all of the versions of lfpykernels
available on your platform with:
$ conda search lfpykernels --channel conda-forge
Docker
We provide a Docker (https://www.docker.com) container recipe file with LFPykernels etc. To get started, install Docker and issue either:
# build Dockerfile from GitHub
$ docker build -t lfpykernels https://raw.githubusercontent.com/LFPy/LFPykernels/main/Dockerfile
$ docker run -it -p 5000:5000 lfpykernels
or
# build local Dockerfile (obtained by cloning repo, checkout branch etc.)
$ docker build -t lfpykernels - < Dockerfile
$ docker run -it -p 5000:5000 lfpykernels
If the docker file should fail for some reason it is possible to store the build log and avoid build caches by issuing
$ docker build --no-cache --progress=plain -t lfpykernels - < Dockerfile 2>&1 | tee lfpykernels.log
For successful builds, the --mount
option can be used to mount a folder on the host to a target folder as:
$ docker run --mount type=bind,source="$(pwd)",target=/opt/data -it -p 5000:5000 lfpykernels
which mounts the present working dirctory ($(pwd)
) to the /opt/data
directory of the container.
Try mounting the LFPykernels
source directory for example (by setting source="<path-to-LFPykernels>"
).
Various example files can then be found in the folder /opt/data/examples/
when the container is running.
Jupyter notebook servers running from within the container can be accessed after invoking them by issuing:
$ cd /opt/data/examples/
$ jupyter-notebook --ip 0.0.0.0 --port=5000 --no-browser --allow-root
and opening the resulting URL in a browser on the host computer, similar to: http://127.0.0.1:5000/?token=dcf8f859f859740fc858c568bdd5b015e0cf15bfc2c5b0c1
Acknowledgements
This work was supported by the European Union Horizon 2020 Research and Innovation Programme under Grant Agreement No. 785907 and No. 945539 Human Brain Project (HBP) SGA2 and SGA3.
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