Source code for hnn_core.network_models

"""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)