Multi-scale spiking network model of macaque visual cortex

This code implements the spiking network model of macaque visual cortex developed at the Institute of Neuroscience and Medicine (INM-6), Research Center Jülich. The model has been documented in the following publications:

1. Schmidt M, Bakker R, Hilgetag CC, Diesmann M & van Albada SJ (2017) Multi-scale account of the network structure of macaque visual cortex Brain Structure and Function (2017) https://doi.org/10.1007/s00429-017-1554-4

2. Schuecker J, Schmidt M, van Albada SJ, Diesmann M & Helias M (2017) Fundamental Activity Constraints Lead to Specific Interpretations of the Connectome. PLOS Computational Biology, 13(2). https://doi.org/10.1371/journal.pcbi.1005179

3. Schmidt M, Bakker R, Shen K, Bezgin B, Diesmann M & van Albada SJ (2018) A multi-scale layer-resolved spiking network model of resting-state dynamics in macaque cortex. (under review)

The code in this repository is self-contained and allows one to reproduce the results of all three papers.

Python framework for the multi-area model

The entire framework is summarized in the figure below:

In principle, we strictly separate the structure of the network (defined by population sizes, synapse numbers/indegrees etc.) from its dynamics (neuron model, neuron parameters, strength of external input, etc.). The complete set of default parameters for all components of the framework is defined in default_params.py.

To start using the framework, the user has to define a few environment variables in a new file called config.py. The file config_template.py lists the required environment variables that need to specified by the user.

Furthermore, please add the path to the multiarea_model subfolder to your PYTHONPATH:

export PYTHONPATH=/path/to/repository/multiarea_model/:$PYTHONPATH.

MultiAreaModel

The central class that initializes the network and contains all information about population sizes and network connectivity. This enables reproducing all figures in [1]. Network parameters only refer to the structure of the network and ignore any information on its dynamical simulation or description via analytical theory.

Simulation

This class can be initialized by MultiAreaModel or as standalone and takes simulation parameters as input. These parameters include, e.g., neuron and synapses parameters, the simulated biological time and also technical parameters such as the number of parallel MPI processes and threads. The simulation uses the network simulator NEST (https://www.nest-simulator.org). For the simulations in [2, 3], we used NEST version 2.8.0. The code in this repository runs with a later release of NEST, version 2.14.0 .

Theory

This class can be initialized by MultiAreaModel or as standalone and takes simulation parameters as input. It provides two main features:

• predict the stable fixed point of the system using mean-field theory
• execute the stabilization method described in [2] on a network instance (will be provided soon)

Analysis

This class allows the user to load simulation data and perform some basic analysis and plotting.

Analysis and figure scripts for [1-3]

The figures folder contains a subfolder with all scripts necessary to produce the figures from [1]. The scripts for [2] and [3] will follow soon. If snakemake is installed, the figures can be produced by executing snakemake in the respective folder:

cd figures/Schmidt2017/
snakemake

Running a simulation

A simple simulation can be run in the following way:

1. Define custom parameters custom_params = ... custom_simulation_params = ...

2. Instantiate the model class together with a simulation class instance.

   M = MultiAreaModel(custom_params, simulation=True, sim_spec=custom_simulation_params)

3. Start the simulation.

   M.simulation.simulate()

Typically, a simulation of the model will be run in parallel on a compute cluster. The files start_jobs.py and run_simulation.py provide the necessary framework for doing this in an automated fashion. The procedure is similar to a simple simulation:

1. Define custom parameters custom_params = ... custom_simulation_params = ...

2. Instantiate the model class together with a simulation class instance.

   M = MultiAreaModel(custom_params, simulation=True, sim_spec=custom_simulation_params)

3. Start the simulation. Call start_job to create a job file using the jobscript_template from the configuration file and submit it to the queue with the user-defined submit_cmd.

The file run_example.py provides an example.

Be aware that, depending on the chosen parameters and initial conditions, the network can enter a high-activity state, which slows down the simulation drastically and can cost a significant amount of computing resources.

Simulation modes

The multi-area model can be run in different modes.

1. Full model

   Simulating the entire networks with all 32 areas and the connections between them is the default mode configure in default_params.py.

2. Down-scaled model

   Since simulating the entire network with approx. 4.13 million neurons and 24.2 billion synapses requires a large amount of resources, the user has the option to scale down the network in terms of neuron numbers and synaptic indegrees (number of synapses per receiving neuron). This can be achieved by setting the parameters N_scaling and K_scaling in network_params to values smaller than 1. In general, this will affect the dynamics of the network. To approximately preserve the population-averaged spike rates, one can specify a set of target rates that is used to scale synaptic weights and apply an additional external DC current.

3. Subset of the network

   You can choose to simulate a subset of the 32 areas specified by the areas_simulated parameter in the sim_params. If a subset of areas is simulated, one has different options for how to replace the rest of the network set by the replace_non_simulated_areas parameter:

   • hom_poisson_stat: all non-simulated areas are replaced by Poissonian spike trains with the same rate as the stationary background input (rate_ext in input_params).
   • het_poisson_stat: all non-simulated areas are replaced by Poissonian spike trains with population-specific stationary rate stored in an external file.
   • current_nonstat: all non-simulated areas are replaced by stepwise constant currents with population-specific, time-varying time series defined in an external file.

4. Cortico-cortical connections replaced

   In addition, it is possible to replace the cortico-cortical connections between simulated areas with the options het_poisson_stat or current_nonstat.

Testsuite

The tests/ folder holds a testsuite that tests different aspects of network model initalization and meanfield calculations. It can be conveniently run by executing pytest in the tests/ folder:

cd tests/
pytest

Requirements

h5py_wrapper, python_dicthash (https://github.com/INM-6/python-dicthash), correlation_toolbox (https://github.com/INM-6/correlation-toolbox), pandas, numpy, nested_dict, matplotlib (2.1.2), pyx, scipy, NEST 2.14.0

Optional: seaborn, Sumatra

To install the required packages in a conda environment, execute:

conda env create -f environment.yaml

Note that NEST needs to be installed separately, see http://www.nest-simulator.org/installation/.

In addition, reproducing the figures of [1] requires python-igraph and networkx. To install these additional packages, execute:

pip install -r figures/Schmidt2017/additional_requirements.txt

In addition, Figure 7 of [1] requires installing the infomap package to perform the map equation clustering. See http://www.mapequation.org/code.html for all necessary information.

The SLN fit in multiarea_model/data_multiarea/VisualCortex_Data.py and figures/Schmidt2017/Fig5_cc_laminar_pattern.py requires an installation of R and the R library aod (http://cran.r-project.org/package=aod). Without R installation, both scripts will directly use the resulting values of the fit (see Fig. 5 of [1]).

Contributors

All authors of the publications [1-3] made contributions to the scientific content. The code base was written by Maximilian Schmidt, Jannis Schuecker, and Sacha van Albada. Testing and review was supported by Alexander van Meegen.

Citation

If you use this code, we ask you to cite the appropriate papers in your publication. For the multi-area model itself, please cite [1] and [3]. If you use the mean-field theory or the stabilization method, please cite [2] in addition. We provide bibtex entries in CITATION.