"""Network model functions."""
# Authors: Nick Tolley <nicholas_tolley@brown.edu>
import os.path as op
import hnn_core
from hnn_core import read_params
from .network import Network
from .params import _short_name
from .cells_default import pyramidal_ca
from .externals.mne import _validate_type
[docs]def jones_2009_model(params=None, add_drives_from_params=False,
legacy_mode=False, mesh_shape=(10, 10)):
"""Instantiate the network model described in
Jones et al. J. of Neurophys. 2009 [1]_
Parameters
----------
params : str | dict | None
The path to the parameter file for constructing the network.
If None, parameters loaded from default.json
Default: None
add_drives_from_params : bool
If True, add drives as defined in the params-dict. NB this is mainly
for backward-compatibility with HNN GUI, and will be deprecated in a
future release. Default: False
legacy_mode : bool
Set to False by default. Enables matching HNN GUI output when drives
are added suitably. Will be deprecated in a future release.
mesh_shape : tuple of int (default: (10, 10))
Defines the (n_x, n_y) shape of the grid of pyramidal cells.
Returns
-------
net : Instance of Network object
Network object used to store
Notes
-----
The network is composed of a square grid of pyramidal cells, arranged in
two layers (L5 and L2). The default in-plane separation of the grid points
is 1.0 um, and the layer separation 1307.4 um. These can be adjusted after
the net is created using the set_cell_positions-method. An all-to-all
connectivity pattern is applied between cells. Inhibitory basket cells are
present at a 1:3-ratio.
References
----------
.. [1] Jones, Stephanie R., et al. "Quantitative Analysis and
Biophysically Realistic Neural Modeling of the MEG Mu Rhythm:
Rhythmogenesis and Modulation of Sensory-Evoked Responses."
Journal of Neurophysiology 102, 3554–3572 (2009).
"""
hnn_core_root = op.dirname(hnn_core.__file__)
if params is None:
params = op.join(hnn_core_root, 'param', 'default.json')
if isinstance(params, str):
params = read_params(params)
net = Network(params, add_drives_from_params=add_drives_from_params,
legacy_mode=legacy_mode, mesh_shape=mesh_shape)
delay = net.delay
# source of synapse is always at soma
# layer2 Pyr -> layer2 Pyr
# layer5 Pyr -> layer5 Pyr
lamtha = 3.0
loc = 'proximal'
for target_cell in ['L2_pyramidal', 'L5_pyramidal']:
for receptor in ['nmda', 'ampa']:
key = f'gbar_{_short_name(target_cell)}_'\
f'{_short_name(target_cell)}_{receptor}'
weight = net._params[key]
net.add_connection(
target_cell, target_cell, loc, receptor, weight,
delay, lamtha, allow_autapses=False)
# layer2 Basket -> layer2 Pyr
src_cell = 'L2_basket'
target_cell = 'L2_pyramidal'
lamtha = 50.
loc = 'soma'
for receptor in ['gabaa', 'gabab']:
key = f'gbar_L2Basket_L2Pyr_{receptor}'
weight = net._params[key]
net.add_connection(
src_cell, target_cell, loc, receptor, weight, delay, lamtha)
# layer5 Basket -> layer5 Pyr
src_cell = 'L5_basket'
target_cell = 'L5_pyramidal'
lamtha = 70.
loc = 'soma'
for receptor in ['gabaa', 'gabab']:
key = f'gbar_L5Basket_{_short_name(target_cell)}_{receptor}'
weight = net._params[key]
net.add_connection(
src_cell, target_cell, loc, receptor, weight, delay, lamtha)
# layer2 Pyr -> layer5 Pyr
src_cell = 'L2_pyramidal'
lamtha = 3.
receptor = 'ampa'
for loc in ['proximal', 'distal']:
key = f'gbar_L2Pyr_{_short_name(target_cell)}'
weight = net._params[key]
net.add_connection(
src_cell, target_cell, loc, receptor, weight, delay, lamtha)
# layer2 Basket -> layer5 Pyr
src_cell = 'L2_basket'
lamtha = 50.
key = f'gbar_L2Basket_{_short_name(target_cell)}'
weight = net._params[key]
loc = 'distal'
receptor = 'gabaa'
net.add_connection(
src_cell, target_cell, loc, receptor, weight, delay, lamtha)
# xx -> layer2 Basket
src_cell = 'L2_pyramidal'
target_cell = 'L2_basket'
lamtha = 3.
key = f'gbar_L2Pyr_{_short_name(target_cell)}'
weight = net._params[key]
loc = 'soma'
receptor = 'ampa'
net.add_connection(
src_cell, target_cell, loc, receptor, weight, delay, lamtha)
src_cell = 'L2_basket'
lamtha = 20.
key = f'gbar_L2Basket_{_short_name(target_cell)}'
weight = net._params[key]
loc = 'soma'
receptor = 'gabaa'
net.add_connection(
src_cell, target_cell, loc, receptor, weight, delay, lamtha)
# xx -> layer5 Basket
src_cell = 'L5_basket'
target_cell = 'L5_basket'
lamtha = 20.
loc = 'soma'
receptor = 'gabaa'
key = f'gbar_L5Basket_{_short_name(target_cell)}'
weight = net._params[key]
net.add_connection(
src_cell, target_cell, loc, receptor, weight, delay, lamtha,
allow_autapses=False)
src_cell = 'L5_pyramidal'
lamtha = 3.
key = f'gbar_L5Pyr_{_short_name(target_cell)}'
weight = net._params[key]
loc = 'soma'
receptor = 'ampa'
net.add_connection(
src_cell, target_cell, loc, receptor, weight, delay, lamtha)
src_cell = 'L2_pyramidal'
lamtha = 3.
key = f'gbar_L2Pyr_{_short_name(target_cell)}'
weight = net._params[key]
loc = 'soma'
receptor = 'ampa'
net.add_connection(
src_cell, target_cell, loc, receptor, weight, delay, lamtha)
return net
[docs]def law_2021_model(params=None, add_drives_from_params=False,
legacy_mode=False, mesh_shape=(10, 10)):
"""Instantiate the expansion of Jones 2009 model to study beta
modulated ERPs as described in
Law et al. Cereb. Cortex 2021 [1]_
Returns
-------
net : Instance of Network object
Network object used to store the model used in
Law et al. 2021.
See Also
--------
jones_2009_model
Notes
-----
Model reproduces results from Law et al. 2021
This model differs from the default network model in several
parameters including
1) Increased GABAb time constants on L2/L5 pyramidal cells
2) Decrease L5_pyramidal -> L5_pyramidal nmda weight
3) Modified L5_basket -> L5_pyramidal inhibition weights
4) Removal of L5 pyramidal somatic and basal dendrite calcium channels
5) Replace L2_basket -> L5_pyramidal GABAa connection with GABAb
6) Addition of L5_basket -> L5_pyramidal distal connection
References
----------
.. [1] Law, Robert G., et al. "Thalamocortical Mechanisms Regulating the
Relationship between Transient Beta Events and Human Tactile
Perception." Cerebral Cortex, 32, 668–688 (2022).
"""
net = jones_2009_model(params, add_drives_from_params, legacy_mode,
mesh_shape=mesh_shape)
# Update biophysics (increase gabab duration of inhibition)
net.cell_types['L2_pyramidal'].synapses['gabab']['tau1'] = 45.0
net.cell_types['L2_pyramidal'].synapses['gabab']['tau2'] = 200.0
net.cell_types['L5_pyramidal'].synapses['gabab']['tau1'] = 45.0
net.cell_types['L5_pyramidal'].synapses['gabab']['tau2'] = 200.0
# Decrease L5_pyramidal -> L5_pyramidal nmda weight
net.connectivity[2]['nc_dict']['A_weight'] = 0.0004
# Modify L5_basket -> L5_pyramidal inhibition
net.connectivity[6]['nc_dict']['A_weight'] = 0.02 # gabaa
net.connectivity[7]['nc_dict']['A_weight'] = 0.005 # gabab
# Remove L5 pyramidal somatic and basal dendrite calcium channels
for sec in ['soma', 'basal_1', 'basal_2', 'basal_3']:
del net.cell_types['L5_pyramidal'].sections[
sec].mechs['ca']
# Remove L2_basket -> L5_pyramidal gabaa connection
del net.connectivity[10] # Original paper simply sets gbar to 0.0
# Add L2_basket -> L5_pyramidal gabab connection
delay = net.delay
src_cell = 'L2_basket'
target_cell = 'L5_pyramidal'
lamtha = 50.
weight = 0.0002
loc = 'distal'
receptor = 'gabab'
net.add_connection(
src_cell, target_cell, loc, receptor, weight, delay, lamtha)
# Add L5_basket -> L5_pyramidal distal connection
# ("Martinotti-like recurrent tuft connection")
src_cell = 'L5_basket'
target_cell = 'L5_pyramidal'
lamtha = 70.
loc = 'distal'
receptor = 'gabaa'
key = f'gbar_L5Basket_L5Pyr_{receptor}'
weight = net._params[key]
net.add_connection(
src_cell, target_cell, loc, receptor, weight, delay, lamtha)
return net
# Remove params argument after updating examples
# (only relevant for Jones 2009 model)
[docs]def calcium_model(params=None, add_drives_from_params=False,
legacy_mode=False, mesh_shape=(10, 10)):
"""Instantiate the Jones 2009 model with improved calcium dynamics in
L5 pyramidal neurons. For more details on changes to calcium dynamics
see Kohl et al. Brain Topragr 2022 [1]_
Returns
-------
net : Instance of Network object
Network object used to store the Jones 2009 model with an improved
calcium channel distribution.
See Also
--------
jones_2009_model
Notes
-----
This model builds on the Jones 2009 model by using a more biologically
accurate distribution of calcium channels on L5 pyramidal cells.
Specifically, this model introduces a distance dependent maximum
conductance (gbar) on calcium channels such that the gbar linearly
decreases along the dendrites in the direction of the soma.
References
----------
.. [1] Kohl, Carmen, et al. "Neural Mechanisms Underlying Human Auditory
Evoked Responses Revealed By Human Neocortical Neurosolver."
Brain Topography, 35, 19–35 (2022).
"""
hnn_core_root = op.dirname(hnn_core.__file__)
params_fname = op.join(hnn_core_root, 'param', 'default.json')
if params is None:
params = read_params(params_fname)
net = jones_2009_model(params, add_drives_from_params, legacy_mode,
mesh_shape=mesh_shape)
# Replace L5 pyramidal cell template with updated calcium
cell_name = 'L5_pyramidal'
pos = net.cell_types[cell_name].pos
net.cell_types[cell_name] = pyramidal_ca(
cell_name=_short_name(cell_name), pos=pos)
return net
def add_erp_drives_to_jones_model(net, tstart=0.0):
"""Add drives necessary for an event related potential (ERP)
Parameters
----------
net : Instance of Network object
Network object that will be updated with ERP drives.
Drives are updated in place.
tstart : float | int
Start time of sensory input in ms. (Default 0.0 ms)
Notes
-----
The first proximal input arrives at cortex ~20 ms after sensory
stimulus. The exact delay depends random number generator due to
random sampling of times from a gaussian.
"""
_validate_type(net, Network, 'net', 'Network')
_validate_type(tstart, (float, int), 'tstart', 'float or int')
# Add distal drive
weights_ampa_d1 = {'L2_basket': 0.006562, 'L2_pyramidal': 7e-6,
'L5_pyramidal': 0.142300}
weights_nmda_d1 = {'L2_basket': 0.019482, 'L2_pyramidal': 0.004317,
'L5_pyramidal': 0.080074}
synaptic_delays_d1 = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,
'L5_pyramidal': 0.1}
net.add_evoked_drive(
'evdist1', mu=63.53 + tstart, sigma=3.85, numspikes=1,
weights_ampa=weights_ampa_d1, weights_nmda=weights_nmda_d1,
location='distal', synaptic_delays=synaptic_delays_d1, event_seed=274)
# Add proximal drives
weights_ampa_p1 = {'L2_basket': 0.08831, 'L2_pyramidal': 0.01525,
'L5_basket': 0.19934, 'L5_pyramidal': 0.00865}
synaptic_delays_prox = {'L2_basket': 0.1, 'L2_pyramidal': 0.1,
'L5_basket': 1., 'L5_pyramidal': 1.}
net.add_evoked_drive(
'evprox1', mu=26.61 + tstart, sigma=2.47, numspikes=1,
weights_ampa=weights_ampa_p1, weights_nmda=None, location='proximal',
synaptic_delays=synaptic_delays_prox, event_seed=544)
weights_ampa_p2 = {'L2_basket': 0.000003, 'L2_pyramidal': 1.438840,
'L5_basket': 0.008958, 'L5_pyramidal': 0.684013}
net.add_evoked_drive(
'evprox2', mu=137.12 + tstart, sigma=8.33, numspikes=1,
weights_ampa=weights_ampa_p2, location='proximal',
synaptic_delays=synaptic_delays_prox, event_seed=814)