Simulation of a Continuous Attractor Network of Grid Cells
The associated code models periodic and aperiodic continuous attractor networks of Grid Cells with random or recorded trajectory data. Neuron spiking is simulated by a Poisson Process. For the 2d matlab model a single neuron response for the simulation is recorded and returned; during the simulation, the network population response and single neuron spiking with respect to the trajectory are displayed in figures. For the c code, population and spiking data are saved. View the assoicated ReadMe files for more information.
Code:
GC_Dynamics, Matlab Read Me - 1d and 2d Matlab simulation files

c_GC_Dynamics, c code Read Me - c simulation files
Related Publication:
Y. Burak and I. R. Fiete. Accurate path integration in continuous attractor network models of grid cells. PLoS Comp. Biol. 5(2) (2009).
Please cite this paper if you use this code.
Simulation of Long Chain Network formation via STDP and hLTD
Simulation of long chain network formation through spike time dependent plasticity and heterosynaptic long term depression. This simulation aligns with Figure 2 in the associated paper, imposing binary neuron network dynamics and a summed weight limit. After weights are learned, random bursts are provided to initiate activity and playback is plotted.
Code:
Long Chain Simulation, Read Me - Figure 2 simulation file
Related Publication:
I. R. Fiete, W. Senn, C. Wang, R. H. R. Hahnloser. Spike time-dependent plasticity and heterosynaptic competition organize networks to produce long scale-free sequences of neural activity. Neuron 65(4): 563-576 (2010).
Please cite this paper if you use this code.