|Analysis of manifold structure in head direction data (SPUD)|
|Code to characterize manifolds and perform unsupervised decoding in the head direction system, as described in the study of Chaudhuri et al. (2019). Further details are in the readme.txt file. Code may be used freely. Please cite the associated paper if you use this code.|
R. Chaudhuri, B. Gercek*, B. Pandey*, A. Peyrache, I.R. Fiete. The intrinsic population dynamics of a canonical cognitive circuit. Nature Neurosci. (2019).
SPUD code | Readme
Python 2.7 code and associated readme file in a single zipped folder; Readme is a duplicate of what you will find in the code folder
|Grid cell continuous attractor models|
|Simulation code for grid cell activity based on continuous attractor dynamics in neural networks, with periodic or aperiodic boundary conditions. The code can be run with random or recorded trajectory data from rats (included in download). View the associated ReadMe files for more information.|
Y. Burak and I. R. Fiete. Accurate path integration in continuous attractor network models of grid cells. PLoS Comp. Biol. 5(2) (2009).
Matlab GC Dynamics | Matlab Readme
Matlab simulation files for 1-dim and 2-dim networks
c GC Dynamics | c code Readme
c simulation files
|Formation of neural sequences via STDP and heterosynaptic |
|Simulations showing how spike time dependent plasticity and heterosynaptic competition spontaneously organize networks to produce long, sparse neural activity sequences, whether the training inputs are sequential or not. This simulation aligns with Figure 2 in the associated paper (summed weight bound). After weights are learned, random bursts are provided to initiate activity and playback is plotted.|
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).
Binary neuron sequence formation | Readme
Matlab simulation file