Models that contain the Neuron : Neocortex M1 L2/6 pyramidal intratelencephalic GLU cell

Re-display model names without descriptions
    Models   Description
1.  Composite spiking network/neural field model of Parkinsons (Kerr et al 2013)
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.
2.  Cortical model with reinforcement learning drives realistic virtual arm (Dura-Bernal et al 2015)
We developed a 3-layer sensorimotor cortical network of consisting of 704 spiking model-neurons, including excitatory, fast-spiking and low-threshold spiking interneurons. Neurons were interconnected with AMPA/NMDA, and GABAA synapses. We trained our model using spike-timing-dependent reinforcement learning to control a virtual musculoskeletal human arm, with realistic anatomical and biomechanical properties, to reach a target. Virtual arm position was used to simultaneously control a robot arm via a network interface.
3.  Cortical network model of posttraumatic epileptogenesis (Bush et al 1999)
This simulation from Bush, Prince, and Miller 1999 shows the epileptiform response (Fig. 6C) to a brief single stimulation in a 500 cell network of multicompartment models, some of which have active dendrites. The results which I obtained under Redhat Linux is shown in result.gif. Original 1997 code from Paul Bush modified slightly by Bill Lytton to make it work with current version of NEURON (5.7.139). Thanks to Paul Bush and Ken Miller for making the code available.
4.  Motor cortex microcircuit simulation based on brain activity mapping (Chadderdon et al. 2014)
"... We developed a computational model based primarily on a unified set of brain activity mapping studies of mouse M1. The simulation consisted of 775 spiking neurons of 10 cell types with detailed population-to-population connectivity. Static analysis of connectivity with graph-theoretic tools revealed that the corticostriatal population showed strong centrality, suggesting that would provide a network hub. ... By demonstrating the effectiveness of combined static and dynamic analysis, our results show how static brain maps can be related to the results of brain activity mapping."

Re-display model names without descriptions