Model code is published on ModelDB. Some highlights:
Spiking neuron model of the basal ganglia
Humphries, Stewart & Gurney (2006) A physiologically plausible model of action selection and oscillatory activity in the basal ganglia. Journal of Neuroscience, 26: 12921-12942.
The purpose of this model was to test the “action selection” theory of basal ganglia operation: we set out to show that a basal ganglia model implementing action selection was consistent with a wide range of electrophysiological data on the dynamics of the basal ganglia nuclei.
In particular, this paper advanced the hypothesis that dopamine acts to de-couple the subthalamic nucleus (STN) – globus pallidus (GP) negative feedback loop in normal conditions, and that a reduction in tonic dopamine will re-couple the feedback loop. Consequently, we proposed that in parkinsonian states this loop will be permanently re-coupled, producing pathological rhythms that interfere with normal selection operation in the basal ganglia. Our hypothesis has been further tested by others (e.g. Holgado et al, 2010, J Neurosci; Hahn & McIntyre, 2010, J Comput Neurosci) who showed that such a re-coupling indeed produces pathological rhythmic dynamics.
Striatum 3D network
Humphries, Wood & Gurney (2010) PLoS Computational Biology
The main thrust of this paper was the development of the 3D anatomical network of the striatum's GABAergic microcircuit. We grew dendrite and axon models for the MSNs and FSIs and extracted probabilities for the presence of these neurites as a function of distance from the soma. From these, we found the probabilities of intersection between the neurites of two neurons given their inter-somatic distance, and used these to construct three-dimensional striatal networks. These networks were examined for their predictions for the distributions of the numbers and distances of connections for all the connections in the microcircuit.
We used this model to examine the impact of the anatomical network on the firing properties of the MSN and FSI populations, and to study the influence of all the inputs to one MSN within the network.
The code archive contains all of the MATLAB scripts, functions and MEX files that produced the dynamical model results from that paper. The source C++ code for the MEX files is also included so they can be recompiled for other systems. The code includes the full set of analysis routines for finding the firing rate distributions (Fig. 9 in the paper) and for assessing the impact of the inputs to a single MSN in the network (Fig. 10 in the paper). We also make available a complete connectivity matrix (129MB) describing all the connections in a 1mm3 model with 1% FSIs. This can be loaded by the StriatumNetworkParameters.m function - see that function for instructions.
Notes: an updated version of this model was published in Tomkins, A., Vasilaki, E., Beste, C., Gurney, K. & Humphries, M. (2014)
Transient and steady-state selection in the striatal microcircuit.
Front. Comput. Neurosci., 7, 192.
Control of behaviour by the brainstem reticular formation
Humphries, Prescott & Gurney (2007) Phil Trans Roy Soc B
A set of models to study the medial reticular formation (mRF) of the brainstem. We developed a collection of algorithms to derive the adult-state wiring of the model: one set a stochastic model; the other set mimicking the developmental process. We found that the anatomical models had small-world properties, irrespective of the choice of algorithm; and that the cluster-like organisation of the mRF may have arisen to minimise wiring costs. (The model code includes options to be run as dynamic models; papers examining these dynamics are included in the .zip file).
The anatomical models are detailed in Humphries, Prescott & Gurney (2006) Proc Roy Soc B.
A review of the functions of the mRF, and study of a simple population-level dynamic model are in Humphries, Prescott & Gurney (2007) Phil Trans Roy Soc B. An extended review and study of a simple network-level dynamic model are in Humphries, Prescott & Gurney (2011) The medial reticular formation: a brainstem substrate for simple action selection?. In “Modelling Natural Action Selection”. CUP: Cambridge.