This code implements a composite model of Parkinson's disease (PD). The composite model consists of a leaky integrate-and-fire spiking neuronal network model being driven by output from a neural field model (instead of the more usual white noise drive). Three different sets of parameters were used for the field model: one with basal ganglia parameters based on data from healthy individuals, one based on data from individuals with PD, and one purely thalamocortical model. The aim of this model is to explore how the different dynamical patterns in each each of these field models affects the activity in the network model.
Model Type: Realistic Network; Neural mass
Region(s) or Organism(s): Neocortex; Thalamus
Cell Type(s): Neocortex M1 L2/6 pyramidal intratelencephalic GLU cell; Neocortex M1 L5B pyramidal pyramidal tract GLU cell; Neocortex fast spiking (FS) interneuron; Neocortex spiking low threshold (LTS) neuron
Currents: I Chloride; I Sodium; I Potassium
Receptors: GabaA; GabaB; AMPA; NMDA; Gaba
Model Concept(s): Oscillations; Methods; Parkinson's; Information transfer
Simulation Environment: NEURON (web link to model); MATLAB; Python
Implementer(s): Kerr, Cliff [cliffk at neurosim.downstate.edu]
References:
Kerr CC et al. (2013). Cortical information flow in Parkinson's disease: a composite network/field model. Frontiers in computational neuroscience. 7 [PubMed]