Embedding a neuron model in a network model¶

ISF defines network connectivity between presynaptic cells and a postsynaptic neuron model in terms of two files:

The .syn file defines the location of synapses onto a postsynaptic morphology, including the type of the corresponding presynaptic cell
The .con file defines for each synapse the corresponding ID, and the presynaptic cell ID. Each presynaptic cell may have one or more synapses with the postsynaptic neuron.

ISF provides a way to generate this connectivity data based on an empirically quantified dense anatomical model, as shown in this section

Info

Some model systems may not have the necessary data available for this pipeline, or infer connectivity from anatomical models in different ways. You can still use ISF for those model systems. In these cases, we highly recommend to generate synapse locations on the postsynaptic morphology in whichever form is most suitable to your model system, and convert the connectivity specification to the ISF file formats: .syn and .con.

Warning

If you decide to use your own methods for inferring synapse locations onto the neuron model morphology, it may be necessary to use methods based on empirically verified dense anatomical models. Otherwise, simulation results may not be empirically consistent, or predictions based on these results may not successfully generalize to in vivo conditions.

The .syn file ¶

.syn files define the location of synapses onto a postsynaptic morphology. As such, they are only valid for one specific morphology.

Let’s have a look at what exactly a .syn file looks like.

[1]:

from getting_started import example_data_dir
from pathlib import Path
example_embedding_dir = Path(example_data_dir) / "anatomical_constraints" / "example_embedding_86_C2_center"

[2]:

example_syn_fn = [e for e in example_embedding_dir.glob("*.syn")][0]
with open(example_syn_fn) as input_file:
    head = [next(input_file) for _ in range(10)]
print("".join(head))

# Synapse distribution file
# corresponding to cell: 86_C2_center
# Type - section - section.x

VPM_E1  112     0.138046479525
VPM_E1  130     0.305058053119
VPM_E1  130     0.190509288017
VPM_E1  9       0.368760777084
VPM_E1  110     0.0
VPM_E1  11      0.120662910562

The .syn file consists of a three-line header, a blank line, and then the connectivity data.

The header of the .syn file defines what the file is, to which morphology file this .syn file belongs, and the names of the three columns:

Type: the type of the presynaptic cell that connects with this synapse
section: the section number of the postsynaptic morphology where this synapse connects
section.x: the relative coordinate on this section where this synapse connects

Each line corresponds to a single synapse. Each synapse has an ID that equals the index of this line, starting at 0.

A single .syn file uniquely defines, for one morphology, where each synapse is located on the dendritic tree, and what the corresponding presynaptic cell type is for each synapse.

The .con file ¶

A .con file defines to which presynaptic cell each synapse belongs.

[3]:

example_con_fn = [e for e in example_embedding_dir.glob("*.con")][0]
with open(example_con_fn) as input_file:
    head = [next(input_file) for _ in range(10)]
print("".join(head))

# Anatomical connectivity realization file; only valid with synapse realization:
# example_embedding_86_C2_center.syn
# Type - cell ID - synapse ID

L6cc_A3 0       0
L6cc_A3 1       1
L6cc_A3 2       2
L6cc_A3 3       3
L6cc_A3 4       4
L6cc_A3 4       5

We see that the .con file again describes a three-line header. This time, it backlinks to a .syn file and again defines three columns:

Type: the type of the presynaptic cell this synapse belongs to. It is added for redundancy, so that this file is interpretable in isolation.
cell ID: the ID of the presynaptic cell this synaps belongs to. This is not necessarily the same as the ID of the synapse. The same cell can make multiple synaptic connections with a postsynaptic neuron. These IDs are only the same if each synapse belongs to a unique presynaptic cell.
synapse ID: the ID of this synapse. This corresponds to the row index in a .syn file. This also explains why the corresponding .syn file is mentioned in the header.

.con files provide some redundant information when combined with .syn files. This is simply to keep these files interpretable and informative when viewed in isolation.

The network parameters ¶

Network parameters define all parameters that relate to a network; both connectivity (this tutorial) and activity (next tutorial). The next tutorial will build a fully functional network parameters that can be used for simulations of network-embedded neuron models.

For the sake of completeness, we already show here how to build one containing just connectivity info.

[4]:

from singlecell_input_mapper.network_param_builder import NetworkParamBuilder

netp = NetworkParamBuilder().add_network_embedding(
    syn_fn = example_syn_fn,
    con_fn = example_con_fn,
    join="right"  # right-join the incoming connectivity data to the network parameters (default is left)
)

netp.network_parameters.as_dict()

Warning: no DISPLAY environment variable.
--No graphics will be displayed.

[4]:

{'info': {'date': '2026-04-23', 'name': 'meulemeester'},
 'network': {'L1_B1': {'cellNr': 8,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'L1_B2': {'cellNr': 10,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'L1_B3': {'cellNr': 3,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'L1_Beta': {'cellNr': 2,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'L1_C1': {'cellNr': 15,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'L1_C2': {'cellNr': 43,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'L1_C3': {'cellNr': 15,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'L1_D1': {'cellNr': 6,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'L1_D2': {'cellNr': 26,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'L1_D3': {'cellNr': 8,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'L1_E2': {'cellNr': 3,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'L23Trans_B1': {'cellNr': 1,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'L23Trans_B2': {'cellNr': 7,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'L23Trans_B3': {'cellNr': 2,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'L23Trans_C1': {'cellNr': 6,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'L23Trans_C2': {'cellNr': 60,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'L23Trans_C3': {'cellNr': 2,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'L23Trans_D1': {'cellNr': 1,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'L23Trans_D2': {'cellNr': 9,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'L2_Alpha': {'cellNr': 19,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'L2_B1': {'cellNr': 69,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'L2_B2': {'cellNr': 279,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'L2_B3': {'cellNr': 153,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'L2_B4': {'cellNr': 27,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'L2_Beta': {'cellNr': 80,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'L2_C1': {'cellNr': 296,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'L2_C2': {'cellNr': 1184,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'L2_C3': {'cellNr': 299,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'L2_C4': {'cellNr': 18,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'L2_D1': {'cellNr': 97,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'L2_D2': {'cellNr': 315,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'L2_D3': {'cellNr': 57,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'L2_D4': {'cellNr': 67,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'L2_E1': {'cellNr': 21,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'L2_E2': {'cellNr': 1,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'L2_Gamma': {'cellNr': 52,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'L34_A1': {'cellNr': 29,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
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   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
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   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
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   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
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   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
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   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
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   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
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   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
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   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
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   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
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   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
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   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
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   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
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   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
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   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
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   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
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   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
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  'L6ccinv_E2': {'cellNr': 17,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
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  'L6ccinv_E3': {'cellNr': 28,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
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    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'SymLocal4_D3': {'cellNr': 3,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'SymLocal4_E1': {'cellNr': 1,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'SymLocal4_Gamma': {'cellNr': 2,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'SymLocal5_B3': {'cellNr': 1,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'SymLocal5_C2': {'cellNr': 4,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'SymLocal5_D1': {'cellNr': 1,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'SymLocal5_D2': {'cellNr': 1,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'SymLocal6_B2': {'cellNr': 1,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'VPM_A3': {'cellNr': 1,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'VPM_B1': {'cellNr': 4,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'VPM_B2': {'cellNr': 19,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'VPM_C1': {'cellNr': 25,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'VPM_C2': {'cellNr': 176,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'VPM_C3': {'cellNr': 3,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'VPM_D1': {'cellNr': 10,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'VPM_D2': {'cellNr': 5,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'VPM_E1': {'cellNr': 5,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}},
  'VPM_Gamma': {'cellNr': 3,
   'synapses': {'distributionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.syn'),
    'connectionFile': PosixPath('/gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/getting_started/example_data/anatomical_constraints/example_embedding_86_C2_center/example_embedding_86_C2_center.con')}}},
 'NMODL_mechanisms': {}}

We can see how the Network parameters simply contain references to these .syn and .con files. This information is defined for each cell type separately, allowing the user to specify additional cell types post-hoc, with additional and separate .syn and .con files. However, for many use cases, it is sufficient to define all connectivity in a single .syn and .con file.

Embedding a neuron model into a network model¶

This section covers the ISF pipeline to calculate synapse positions on the neuron morphology from empirical data This pipeline has been described in detail in Udvary et al. (2022).

The data requirements for this pipeline are:

3D density distribution of synapses across the neuropil
Density of postsynaptic targets on the postsynaptic cell type, in units of length and area
Amount of cells per cell type across the neuropil
Locations of boutons in the neuropil, per cell type

We provide a dense model that contains this data for the rat barrel cortex, which you can download from Harvard Dataverse:

pixi run download_bc_model

We explore the input data provided by this model and its formats in this auxiliary tutorial. For an interactive exploration of this model, please visit cortexinsilico.mpinb.mpg

Info

This is one of many methods in which you can infer synapse locations onto a postsynaptic morphology. If you do not have the required input data for this method, or you have your own methods, you can still use ISF! Familiarize yourself with the network connectivity file formats used in ISF: .syn and .con

[5]:

# Check if the model is downloaded
from os.path import exists as path_exists, join as path_join, expandvars
assert path_exists(expandvars(path_join("$ISF_HOME", "barrel_cortex"))), "Please download the barrel cortex model first by running pixi r download_bc_model"

To get started, adapt the desired output directory below:

[6]:

from pathlib import Path
tutorial_output_dir = f"{Path.home()}/isf_tutorial_output"  # <-- Change this to your desired output directory

[7]:

%matplotlib inline
import Interface as I
from getting_started import getting_started_dir

example_data_dir = I.os.path.join(getting_started_dir, 'example_data')
db = I.DataBase(tutorial_output_dir).create_sub_db("network_modeling")

[INFO] ISF: Current version: heads/morph+0.gc42d144b8.dirty
[INFO] ISF: Current pid: 3075680
[INFO] mechanisms: Loaded mechanisms in NEURON namespace.
[ATTENTION] ISF: The source folder has uncommited changes!



[INFO] ISF: Loaded modules with __version__ attribute are:
IPython: 8.18.1, Interface: heads/morph+0.gc42d144b8.dirty, PIL: 11.3.0, _brotli: 1.1.0, _csv: 1.0, _ctypes: 1.1.0, _curses: b'2.2', _decimal: 1.70, argparse: 1.1, blosc: 1.11.3, bluepyopt: 1.9.126, brotli: 1.1.0, certifi: 2025.08.03, charset_normalizer: 3.4.3, click: 7.1.2, cloudpickle: 3.1.1, colorama: 0.4.6, comm: 0.2.3, csv: 1.0, ctypes: 1.1.0, cycler: 0.12.1, cytoolz: 1.0.1, dash: 2.18.2, dask: 2.30.0, dateutil: 2.9.0.post0, deap: 1.4, debugpy: 1.8.16, decimal: 1.70, decorator: 5.2.1, defusedxml: 0.7.1, distributed: 2.30.0, distutils: 3.9.23, entrypoints: 0.4, exceptiongroup: 1.3.0, executing: 2.2.0, fasteners: 0.17.3, flask: 1.1.4, fsspec: 2025.7.0, future: 1.0.0, greenlet: 3.2.4, idna: 3.10, ipaddress: 1.0, ipykernel: 6.29.5, ipywidgets: 8.1.7, isf_pandas_msgpack: 0.4.0, itsdangerous: 1.1.0, jedi: 0.19.2, jinja2: 2.11.3, joblib: 1.5.1, json: 2.0.9, jupyter_client: 7.3.4, jupyter_core: 5.8.1, kiwisolver: 1.4.7, logging: 0.5.1.2, markupsafe: 2.0.1, matplotlib: 3.3.2, matplotlib_inline: 0.1.7, msgpack: 1.1.1, neuron: 7.8.2+, numcodecs: 0.12.1, numexpr: 2.9.0, numpy: 1.19.2, packaging: 25.0, pandas: 1.1.3, parso: 0.8.4, past: 1.0.0, pexpect: 4.9.0, pickleshare: 0.7.5, platform: 1.0.8, platformdirs: 4.3.8, plotly: 5.24.1, prompt_toolkit: 3.0.51, psutil: 7.0.0, ptyprocess: 0.7.0, pure_eval: 0.2.3, pydevd: 3.2.3, pygments: 2.19.2, pyparsing: 3.2.3, pytz: 2025.2, re: 2.2.1, requests: 2.32.5, scipy: 1.5.2, seaborn: 0.12.2, six: 1.17.0, sklearn: 0.23.2, socketserver: 0.4, socks: 1.7.1, sortedcontainers: 2.4.0, stack_data: 0.6.3, statsmodels: 0.12.1, tables: 3.8.0, tblib: 3.1.0, tlz: 1.0.1, toolz: 1.0.0, tqdm: 4.67.1, traitlets: 5.14.3, urllib3: 2.5.0, wcwidth: 0.2.13, werkzeug: 1.0.1, yaml: 6.0.2, zarr: 2.15.0, zlib: 1.0, zmq: 27.0.2, zstandard: 0.23.0

The first step to embedding a neuron model into a network model, is to align the reference frames of both.

From external resources (e.g. the NeuroMorph pipeline), you need a morphology .hoc-file. The coordinates in the hoc morphology file need to be anchored at the desired location.

As an example, we provide such file of a Layer-5 Pyramidal Tract Neuron (L5PT), whose coordinates are anchored in the C2 column of a rat barrel cortex (BC)

[8]:

anatomy_dir = I.os.path.join(example_data_dir, 'anatomical_constraints')
I.os.listdir(anatomy_dir)

[8]:

['86_C2_center_scaled_diameters.swc',
 'presynaptic_somata_soma_locations.csv',
 '86_C2_center.hoc',
 '89_C2.swc',
 '89_C2.hoc',
 '86_C2_center.swc',
 '86_C2_center_scaled_diameters.hoc',
 'example_embedding_86_C2_center']

[ ]:

path_to_morph = I.os.path.join(
    anatomy_dir,
    '86_C2_center.swc')
assert I.os.path.exists(path_to_morph)

We copy these morphology files to our DataBase.

[ ]:

if not 'anatomical_constraints' in db.keys():
    db.create_managed_folder('anatomical_constraints')
I.shutil.copy(path_to_morph, db['anatomical_constraints'])

'/gpfs/soma_fs/home/meulemeester/isf_tutorial_output/network_modeling/db/anatomical_constraints/86_C2_center_scaled_diameters.hoc'

We use singlecell_input_mapper to create an anatomical embedding. The primary use case is to pass multiple samples to this workflow. Under the hood, multiple anatomical embeddings will be then calculated, all constrained with the same input data. From this distribution of anatomical realizations, the one that is closest to the average is chosen, which can be refered to as a “representative realization”. The result is saved in the same folder as the hoc morphology. This can take quite long to compute (\(\sim 10\) min setup + \(\sim 1.5\) min/sample), but you can continue with a precomputed result, or generate only a single sample. A single sample does not capture biological variability, but it suffices to showcase the API and workflow.

For a given morphology file, a network embedding is fully defined by a .syn and .con file. The former to specify the locations fo the synapses onto the morphology, and the latter to specify their presynaptic origin.

Computing it yourself¶

Here, we only use \(1\) sample. This is not enough to get a sufficiently large distribution to draw a representative sample from. In our experience, \(50\) samples is about enough to get a convergent distribution of synapse embeddings.

A sample size of \(1\) is however sufficient for the sake of this tutorial.

[13]:

celltype = 'L5tt'  # Layer 5 thick-tufted
morphology_fn = db['anatomical_constraints'].join('86_C2_center.swc')

I.logger.setLevel("INFO") # verbose output
I.map_singlecell_inputs(
    cellName=morphology_fn,
    cellTypeName=celltype,
    nrOfSamples=1  # <--- Increase this number to get more samples. ~50 is sufficient for convergence
)
I.logger.setLevel("WARNING")

[INFO] udvary2022: No numberOfCellsSpreadsheetName passed. Falling back to user-configured one.
[INFO] udvary2022: No connectionsSpreadsheetName passed. Falling back to user-configured one.
[INFO] udvary2022: No ExPSTDensityName passed. Falling back to user-configured one.
[INFO] udvary2022: No InhPSTDensityName passed. Falling back to user-configured one.
[INFO] udvary2022: No boutonDensityFolderName passed. Falling back to user-configured one.
[INFO] udvary2022: Loading cell morphology
[INFO] udvary2022: Loading spreadsheets and bouton/PST densities...
[INFO] udvary2022:     Loading numberOfCells spreadsheet /gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/barrel_cortex/nrCells.csv
[INFO] udvary2022:     Loading bouton densities from folder /gpfs/soma_fs/scratch/meulemeester/project_src/in_silico_framework/barrel_cortex/singleaxon_boutons_ascii (may take a while)
[INFO] udvary2022:     Loaded bouton densities in 766.89 s
[INFO] udvary2022: Creating network embedding for  /gpfs/soma_fs/home/meulemeester/isf_tutorial_output/network_modeling/db/anatomical_constraints/86_C2_center.swc
[INFO] network_embedding: ---------------------------
[INFO] network_embedding: Created 536162 presynaptic cells in total
[INFO] network_embedding: ---------------------------
[INFO] network_embedding: Computing synapse densities
[INFO] synapse_mapper: ---------------------------
[INFO] network_embedding: Generating network embedding sample 1 of 1
[INFO] network_embedding: ---------------------------
[INFO] network_embedding: Creating anatomical connectivity map for output...
[INFO] network_embedding: ---------------------------
[INFO] network_embedding: ---------------------------
[INFO] network_embedding: Calculating results summary
[INFO] network_embedding:     Computing path length to soma for all synapses...
[INFO] network_embedding: ---------------------------
[INFO] network_embedding: Computing parameter distribution for 1 samples in population...
[INFO] network_embedding: ---------------------------
[INFO] network_embedding: Found representative sample with all parameters within +-2 SD
[INFO] network_embedding: Closest sample within +-2 SD (ID 0) has minimum distance 0.0 ...
[INFO] network_embedding: ---------------------------
[INFO] network_embedding: Writing output files for representative sample (id=0)
[INFO] network_embedding: ---------------------------
[INFO] network_embedding: Writing output files...

Directory Name is  /gpfs/soma_fs/home/meulemeester/isf_tutorial_output/network_modeling/db/anatomical_constraints/86_C2_center_synapses_20260423-1501_3075680/
CSV file name is  /gpfs/soma_fs/home/meulemeester/isf_tutorial_output/network_modeling/db/anatomical_constraints/86_C2_center_synapses_20260423-1501_3075680/86_C2_center_summary_20260423-1501_3075680

---------------------------
[INFO] network_embedding: Done generating network embedding!
[INFO] network_embedding: ---------------------------
[INFO] udvary2022: Runtime: 15.2 minutes

[14]:

# Adapt the path if you have generated a new anatomical model
path_to_anatomical_model = sorted(I.glob.glob(db['anatomical_constraints'].join('*_center_synapses*')))[-1]
print("Saved embedding results to: ", path_to_anatomical_model)

Saved embedding results to:  /gpfs/soma_fs/home/meulemeester/isf_tutorial_output/network_modeling/db/anatomical_constraints/86_C2_center_synapses_20260423-1501_3075680

Copying a pre-computed result instead¶

[ ]:

# Pre-computed result: --------------------
from distutils.dir_util import copy_tree
path_to_anatomical_model = I.os.path.join(anatomy_dir, "example_embedding_86_C2_center")
copy_tree(path_to_anatomical_model, db['anatomical_constraints'])
path_to_anatomical_model = db['anatomical_constraints']

Parallelizing the generation of anatomical models¶

We can use a dask Client to create our .syn and .con files for one morphology per process. This takes as long as creating an anatomical realization for a single cell, but thanks to parallellization, not much longer than that. At least, until the amount of cells exceed the amount of available threads.

[ ]:

morphology_paths = [db['anatomical_constraints'].join(f) for f in db['anatomical_constraints'].listdir() if f.endswith('.hoc')]
morphology_paths

client = I.get_client(timeout=10)
client

Client

Scheduler: tcp://10.100.2.217:38786
Dashboard: http://10.100.2.217:38787/status

Cluster

Workers: 8
Cores: 8
Memory: 128.85 GB

[ ]:

delayed_map_singlecell_inputs = I.dask.delayed(I.map_singlecell_inputs)  # make the single cell mapper a delayed function
delayeds = [delayed_map_singlecell_inputs(p, 'L5tt') for p in morphology_paths] # call it with the morphologies
delayeds = I.dask.delayed(delayeds) # bundle everything in one delayed object
futures = client.compute(delayeds)  # compute the result

You can restart the client to interrupt this computation if don’t want to compute this right now

[ ]:

client.restart()

Client

Scheduler: tcp://10.100.2.217:38786
Dashboard: http://10.100.2.217:38787/status

Cluster

Workers: 8
Cores: 8
Memory: 128.85 GB

Let’s move the results of our synapse embedding to the database for convenience during this tutorial

[ ]:

# Let's first unpack the re
I.shutil.copytree(path_to_anatomical_model, I.os.path.dirname(path_to_anatomical_model), dirs_exist_ok=True)
I.shutil.rmtree(path_to_anatomical_model)

Inspecting the output¶

In the directory generated by the singlecell_input_mapper, there are the following files:

[15]:

db['anatomical_constraints'].listdir()

[15]:

['Loader.json',
 'example_embedding_86_C2_center.con',
 '86_C2_center_synapses_20260423-1501_3075680',
 'NumberOfConnectedCells.csv',
 '86_C2_center.hoc',
 '86_C2_center_synapses_20260414-2153_1819524',
 'example_embedding_86_C2_center.syn',
 'example_embedding',
 '86_C2_center.swc',
 'metadata.json',
 '86_C2_center_scaled_diameters.hoc']

The most important files for making single-cell simulations are .syn and .con files. The others are useful for analysis, but are not required for further use in ISF.

[16]:

con_file = db['anatomical_constraints'].get_file('.con')
con_file_path = db['anatomical_constraints'].join(con_file)
with open(con_file_path) as f:
    print(f.read()[:300])

# Anatomical connectivity realization file; only valid with synapse realization:
# example_embedding_86_C2_center.syn
# Type - cell ID - synapse ID

L6cc_A3 0       0
L6cc_A3 1       1
L6cc_A3 2       2
L6cc_A3 3       3
L6cc_A3 4       4
L6cc_A3 4       5
L6cc_A3 5       6
L6cc_A3 6       7
L6cc_A3 7       8
L6cc_A3 7       9
L6cc_A3 8       10
L6cc_A3 9       11
L6cc_

The .syn file specifies the exact position of each synapse on the hoc morphology:

[17]:

syn_file_path = db['anatomical_constraints'].join(db['anatomical_constraints'].get_file('.syn'))
with open(syn_file_path) as f:
    print(f.read()[:300])

# Synapse distribution file
# corresponding to cell: 86_C2_center
# Type - section - section.x

VPM_E1  112     0.138046479525
VPM_E1  130     0.305058053119
VPM_E1  130     0.190509288017
VPM_E1  9       0.368760777084
VPM_E1  110     0.0
VPM_E1  11      0.120662910562
SymLocal6_B2    96      0.592704752952
SymLocal3_C1    83      0.754400311346

Here, section referes to the ID of the section in the cell object. x specifies, where along that section the synapse is placed. If x is 0, this is the beginning of the section, if x is one, this is the end of the section.

Analyzing and visualizing the network embedding¶

Let’s create a Cell object from our morphology, add the synapse locations we just created in the .syn format, and inspect the result.

[18]:

cp = I.scp.CellParser(morphology_fn)
cp.spatialgraph_to_cell()
cell = cp.get_cell()

To add synapse locations from a .syn file with a SynapseMapper:

[19]:

syn_locations = I.scp.io.connectivity.read_synapse_realization(syn_file_path)
synapse_mapper = I.scp.SynapseMapper(cell, syn_locations)
synapse_mapper.map_synapse_realization()

You can inspect the synapse location statistics with Dendrograms

[20]:

%matplotlib inline
I.plt.style.use("fivethirtyeight")
from visualize.dendrogram import DendrogramStatistics

dg = DendrogramStatistics(cell)
dg.plot(figsize=(8, 15));

../../_images/tutorials_2._network_models_2.1_Anatomical_embedding_55_0.png

You can also simply plot out the morphology and the synapse locations as a fully featured plot using CellMorphologyVisualizer

[21]:

from visualize.cell_morphology_visualizer import CellMorphologyVisualizer
from matplotlib.pyplot import cm

# only show synapses of this type
synapse_filter = "VPM"
show_synapses = [syntype for syntype in cell.synapses if synapse_filter in syntype]
synapse_colors = cm.Set3(I.np.linspace(0, 1, num=len(show_synapses)))

cmv = CellMorphologyVisualizer(cell)
cmv.population_to_color_dict = dict(zip(show_synapses, synapse_colors))
cmv.background_color="#f0f0f0"
fig, ax = cmv.plot(show_synapses=show_synapses, show_legend=True)

../../_images/tutorials_2._network_models_2.1_Anatomical_embedding_57_0.png

Compatibility with other dense connectome models.¶

While ISF currently only features one network embedding pipeline (Udvary et al., 2022), ISF is not limited to this particular way of generating synapse locations. As long as a .syn and .con file is present (no matter which pipeline created them), any dense connectome model is compatible with ISF. All subsequent workflows in ISF simply rely on these .syn and .con files, but not on the input data that was necessary to generate these. In other words, the barrel cortex model as downloaded in the beginning of this tutorial is simply one example of a network model. Many other dense connectome models may be used. If you have a different way of generating synapse locations, but you still want to use ISF, simply convert the information to .syn and .con. The file formats are purposefully kept as simple as possible.

Recap¶

This tutorial provided a full pipeline on how to generate synapse locations for a given neuron morphology (i.e. an “anatomical network embedding”), and how to parallellize these for multiple morphologies.

In order to assign activity to these synapse realizations, consult the the next tutorial.

Embedding a neuron model in a network model¶

The .syn file¶

The .con file¶

The network parameters¶

Embedding a neuron model into a network model¶

Computing it yourself¶

Copying a pre-computed result instead¶

Parallelizing the generation of anatomical models¶

Client

Cluster

Client

Cluster

Inspecting the output¶

Analyzing and visualizing the network embedding¶

Compatibility with other dense connectome models.¶

Recap¶

The .syn file ¶

The .con file ¶

The network parameters ¶