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Models

From Spiking Neural Mark-up Language (SpineML)

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SpineCreator project examples

These examples are complete SpineCreator projects, including metadata for 2D and 3D layouts, as well as Population colours and Python connectivity generators.

Gurney, Prescott and Redgrave Basal Ganglia model

This is a rate coded model of the Basal Ganglia. Details can be found here:

A computational model of action selection in the basal ganglia. II. Analysis and simulation of behaviour K. Gurney, T. J. Prescott, P. Redgrave

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Gurney, Prescott and Redgrave Basal Ganglia model, spiking implementation

This is an integrate and fire neuron spiking version of the rate coded model of the Basal Ganglia found above.

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Striatal Microcircuit Model

This is a SpineML implementation of a Striatal microcircuit model. Details can be found here:

Dopamine-modulated dynamic cell assemblies generated by the GABAergic striatal microcircuit Mark D. Humphries, , Ric Wood, Kevin Gurney

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Angular Velocity Detector Unit model

This is a model of the angular velocity detection system in the insect. There are three experiments included, and there is some set up work to configure each. The model can be run on Linux or OSX.

First, download the zip file from here, and unzip it.

Second install Qt 5 and download the simulated environment (beeworld) from GitHub. You'll also need scipy.

Third, run QtCreator, load the .pro file and use the default build options, then build the beeworld. Copy the beeworld2 binary (if on Mac you need the one inside the .app package (right click and select 'show package contents' to get it). Then replace the beeworld2 file from the zip you downloaded (it is compiled for Mac, but almost certainly won't work on your computer).

Fourth, install SpineML_2_BRAHMS and BRAHMS as described in Brahms. Note the installation locations (on Mac the installation locations are inside the .app package,right click and select 'show package contents')

Fifth. The zip contains three directories beginning 'Paper' - these are the experiments. The cc_XXXX_model directories are the SpineML models. You now need to configure each experiment for your system - replace the SML_2_B_dir, SML_dir and Model_dir variables in run_FigX.py and analyse_FigX.py with the SpineML_2_BRAHMS, SystemML and model directories on your system, respectively.

Sixth, run

python run_FigX.py && python analyse_FigX.py

You will get a labelled graph of the model output when the batch run is complete.

SpineML toolchain examples

These example consist of pure SpineML with no SpineCreator project files or metadata. They can be imported into SpineCreator, but do not have 2D or 3D layout data.

Brette Benchmark Model

Components

Name Description XML PNG
LIF Leaky Integrate and Fire Neuron Body Xml icon.png Png icon.png
FixedWeight Fixed Weight Synaptic Update Xml icon.png Png icon.png
Curr_exp Exponentially Decaying Post-Synaptic Current Xml icon.png Png icon.png

Network

A network of two populations of Excitatory and Inhibitory neurons. Based upon the model described in; Romain Brette et al. "Simulation of networks of spiking neurons: A review of tools and strategies", 2007.

High Level Network Layer Model Xml icon.png

High Level Network Layer Model (split version with maximum population size 100) Xml icon.png (1.9MB)

Experiment

Experiment file which runs the Brett benchmark for a period of 1 second recording all spike and voltage values.

Experiment Layer Model Xml icon.png

Simulator Compatibility

This model can be executed in Brahms and DAMSON. DAMSON requires both the split and un-split version of the model to generate code. As DAMSON Alias creation is slow a pre transformed download is available [here].

Brette Benchmark Model (Using PyNN Neurons)

This is the same as above however the synapse model is integrated into the neuron body. The Post-Synapse component acts as a pass-through.

Components

Name Description XML PNG
IF_curr_exp Leaky Integrate and Fire Neuron Body with Exponentially Decaying Post-Synaptic Current Xml icon.png Png icon.png
PyNN_WeightUpdate A Fixed Weight Synaptic Update Xml icon.png Png icon.png
PyNN_PostSynapse A Pass-through Post-Synapse (Relays Impulses to the Post-Synaptic Neuron) Xml icon.png Png icon.png

Network

A network of two populations of Excitatory and Inhibitory neurons. Based upon the model described in; Romain Brette et al. "Simulation of networks of spiking neurons: A review of tools and strategies", 2007.. Replicates the standard Brett Benchmark model however the PyNN_PostSynpases redirects any impulse events to the post synaptic neuron body which models the dynamics. PyNN uses separate synaptic currents for excitatory and inhibitory synapses and as such negative synaptic weights are not required.

High Level Network Layer Model Xml icon.png

Experiment

Experiment file which runs the Brett benchmark for a period of 1 second recording all spike and voltage values.

Experiment Layer Model Xml icon.png

Simulator Compatibility

This model can be executed in Brahms and PyNN. DAMSON is currently not able to simulate this model due to the large number of impulse events used within post synapse models (a work around for this will be available shortly).

Striatal Microcircuit Model

This is a SpineML representation of the model described in (ref). It uses the Low Level Network Layer extension to implement gap junctions and is therefore only suitable for simulation with Brahms or DAMSON (no PyNN).

Components

Name Description XML PNG
regular_spike Regular source of spikes Xml icon.png Png icon.png
static_weight A non-plastic weight update Xml icon.png Png icon.png
Conductance_exp_synapse A conductance-based exponentially decaying synaptic current Xml icon.png Png icon.png
Conductance_exp_Mg_block_synapse_sat Mg influenced synaptic current Xml icon.png Png icon.png
Striatal_Fast-spiking_Interneuron Izhikevich based model of a striatal FSI Xml icon.png Png icon.png
Striatal_D1_Medium_Spiny Izhikevich based model of a striatal MSN Xml icon.png Png icon.png
Striatal_D2_Medium_Spiny Izhikevich based model of a striatal MSN Xml icon.png Png icon.png
Gap_junction_compartment model of a gap junction as a compartment Xml icon.png Png icon.png

Network

Microcircuit model of the striatum. WARNING - this is a large file, so make sure you right click and select save as - if left clicked it may crash your browser.

Low Level Network Layer Model Xml icon.png (24.7MB)

Low Level Network Layer Model (split version with maximum population size 100) Xml icon.png (38.9MB)

Low Level Network Layer no connections - This is the original low level network model with no explicit lists of connectivity to reduce the file size. This is for information purposes only and cannot be used for simulation! Xml icon.png (24KB)

Experiment

Experiment file which runs the Striatal model for a period of 10 seconds recording all spike from the MSNs and one voltage trace from the D1 MSNs.

Experiment Layer Model Xml icon.png

Simulator Compatibility

This model uses the Low Level Network Layer extension to implement gap junctions and is therefore only suitable for simulation with Brahms or DAMSON (no PyNN). DAMSON requires both the split and un-split version of the model to generate code. As DAMSON Alias creation is slow a pre transformed download is available here.