Cortical oscillations and the basal ganglia (Fountas & Shanahan 2017)

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"Although brain oscillations involving the basal ganglia (BG) have been the target of extensive research, the main focus lies disproportionally on oscillations generated within the BG circuit rather than other sources, such as cortical areas. We remedy this here by investigating the influence of various cortical frequency bands on the intrinsic effective connectivity of the BG, as well as the role of the latter in regulating cortical behaviour. To do this, we construct a detailed neural model of the complete BG circuit based on fine-tuned spiking neurons, with both electrical and chemical synapses as well as short-term plasticity between structures. As a measure of effective connectivity, we estimate information transfer between nuclei by means of transfer entropy. Our model successfully reproduces firing and oscillatory behaviour found in both the healthy and Parkinsonian BG. We found that, indeed, effective connectivity changes dramatically for different cortical frequency bands and phase offsets, which are able to modulate (or even block) information flow in the three major BG pathways. ..."
1 . Fountas Z, Shanahan M (2017) The role of cortical oscillations in a spiking neural network model of the basal ganglia. PLoS One 12:e0189109 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Subthalamic Nucleus; Basal ganglia;
Cell Type(s): Subthalamic nucleus principal GABA cell; Globus pallidus principal GABA cell; Substantia nigra pars reticulata principal DA cell; Subthalamus nucleus projection neuron; Globus pallidus neuron;
Gap Junctions:
Simulation Environment: Brian;
Model Concept(s): Short-term Synaptic Plasticity; Parkinson's; Information transfer; Pathophysiology; Synaptic Plasticity; Oscillations; Activity Patterns;
Implementer(s): Fountas, Zafeirios [zfountas at];
Search NeuronDB for information about:  Substantia nigra pars reticulata principal DA cell; Globus pallidus principal GABA cell; Subthalamic nucleus principal GABA cell;
# A large-scale spiking neural network model of the basal ganglia circuitry. 

This model integrates fine-tuned models of phenomenological (Izhikevich) spiking neurons that correspond to different sub-types of cells within the BG nuclei, electrical and conductance-based chemical synapses that include short-term plasticity and neuromodulation, as well as anatomically-derived striatal connectivity. 

In particular, this model comprises 10 neural populations that correspond to the four major nuclei of the biological basal ganglia and form their canonical circuit. These include the striatum (modelled with higher detail than the other groups) and the subthalamic nucleus (STN), the two inputs of the basal gnaglia, the external part of the globus pallidus (GPe), as well as the substantia nigra pars reticulata (SNr), one of the two output structures. Furthermore, the effect of the pars compacta part of the substantia nigra (SNc) is realized through the concentration of the neurotransmitter dopamine in the different parts of the network. The network is divided into three microscopic channels, which are mutually inhibited and used to represent different action requests. A full description of this model can be found in the first two published manuscripts that follow.

A list of citable manuscripts that used this model:

* Fountas, Zafeirios. "*Action selection in the rhythmic brain: The role of the basal ganglia and tremor.*" PhD Thesis, Imperial College London (2016).

* Fountas, Zafeirios, and Murray Shanahan. "*The role of cortical oscillations in a spiking neural model of the basal ganglia.*" *Under review*, PLOS ONE

* Fountas, Zafeirios, and Murray Shanahan. "*Assessing Selectivity in the Basal Ganglia: The 'Gearbox' Hypothesis.*" bioRxiv (2017): 197129.

The latest version of this project can be also found on github:

## Prerequisites

The project's prerequisites include the python2.7 libraries brian, numpy and matplotlib. To install these libraries on a linux machine please open a terminal and type:

(sudo) pip install -r requirements.txt

## Run simulations

To run a simulation please type:

./bgrun -argument1 -argument2 ...

where the available arguments are given as:

* --help: *shows the basic arguments*
* -fr {value}: *is the frequency of cortical oscillations *
* -ph {value}: *is the phase offset between two different cortical inputs*
* -dop {value}: *is the overall level of dopamine in the system*
* -file {data_file_name}: *is optional, and represents the name of the file where data will be stored*
* -print: *Ff defined, it activates a verbose mode*
* -plots: *If defined, it will generate plots of the simulation*
* -seed {value/random}: *specifies the random seed of the simulation*
* -duration {value}: *specifies the duration of the simulation*
* -fr1 {value}: *is the frequency of the first cortical input*
* -end {value}:

* -initial_period: *If defined, the model runs for a initial period of 500ms with only tonic (cortical) stimulation*
* -random_walk: *If defined, the intensity of cortical input follows a random walk*

* -rec_rasters: *If defined, records spike trains for all populations*
* -rec_GPe_types: *If defined, records different types of GPe neurons individually*
* -rec_bins: *If defined, records binned spikes for each neuron group*

* -tonic: *If active, the model will run with only tonic cortical stimulation*
* -one_channel: *If active, only one channel receives stimulation*
* -ramp: *If active, the model receives ramped stimulation*
* -GG_stn_gpe:
* -GG_gpe_stn:

* -T1base {value}: *Lowest value of the firing rate of the cortical input in channel 1*
* -T2base {value}: *Lowest value of the firing rate of the cortical input in channel 2*
* -T3base {value}: *Lowest value of the firing rate of the cortical input in channel 3*
* -T1max {value}: *Highest value of the firing rate of the cortical input in channel 1*
* -T2max {value}: *Highest value of the firing rate of the cortical input in channel 2*

### REST 
* -pd_off_state:
* -weight_Bahuguna:
* -P_striatum_weight {value}:
* -GPe_density {value1,2} {value3}: *Density of the 3 GPe neuron types*
* -gpe_type {A/B/C}: *Allows only one GPe neuron type in the simulation*
* -plasticity {True/False}: *If True, short-term synaptic plasticity activates*

## Authors

* **[Zafeirios Fountas](** - *Initial work and author of the PhD thesis*

## License

This project is licensed under the GLUv3 License - see the [LICENSE](LICENSE) file for details.

## Acknowledgments

* **Murray Shanahan** who was Zafeirios' PhD supervisior and co-author of published work
* **Mark Humphries** who provided valuable feedback and inspiration
* **Jeanette Hellgren Kotaleski** who was the first PhD examiner
* **Rob Leech** who was the second PhD examiner
* **EPSRC** and **Imperial College London** for providing funding

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