Codes for running the network model with spatial connectivity (in 2D or 1D) to study how network structure affects spatiotemporal correlations.
For model details and its neuroscietific implications check:
Zeraati, R., Shi, Y., Steinmetz, N. A., Gieselmann, M. A., Thiele, A., Moore, T., Levina, A. & Engel, T. A. (2021). Intrinsic timescales in the visual cortex change with selective attention and reflect spatial connectivity. bioRxiv 2021.05.17.444537. https://www.biorxiv.org/content/10.1101/2021.05.17.444537v2.
Analytical derivations of spatiotemporal correlations in this model are provided in: Shi, Y.L., Zeraati, R., Levina, A. and Engel, T.A. (2023). Spatial and temporal correlations in neural networks with structured connectivity. Physical Review Research, 5(1), p.013005. https://doi.org/10.1103/PhysRevResearch.5.013005.
Please cite the above two references when you use these codes for a scientific publication.
Functions/scripts descriptions:
act_gen: the function that simulates the network (import from activity_generator.py)
run_ac.py: the script for simulating the network and computing autocorrelations.
run_cc.py: the script for simulating the network and computing cross-correlations.
Available connectivity structures for the network (conn_type variable):
'local': 2D network with local connectivity (defined within the radius R in Chebyshev distances, R=1 is the Moore neighborhood)
'local_1D': 1D network with local connectivity (defined within the radius R in Chebyshev distances)
'random': 2D network with random connectivity
'random_spR' (e.g., 'random_sp2'): 2D network with 8 random connections within the radius R = 2,3,5,7
Additional network models:
Network with random connectivity (conn_type = 'random_hetro') and two different cell types: act_gen defined in activity_generator_hetro.py
Network with synaptic timescales: act_gen defined in activity_generator_wSynFilter.py