Spatial structure from diffusive synaptic plasticity (Sweeney and Clopath, 2016)

 Download zip file 
Help downloading and running models
Accession:190311
In this paper we propose a new form of Hebbian synaptic plasticity which is mediated by a diffusive neurotransmitter. The effects of this diffusive plasticity are implemented in networks of rate-based neurons, and lead to the emergence of spatial structure in the synaptic connectivity of the network.
Reference:
1 . Sweeney Y, Clopath C (2016) Emergent spatial synaptic structure from diffusive plasticity. Eur J Neurosci :1-11 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type:
Brain Region(s)/Organism:
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s): NO;
Simulation Environment: Python;
Model Concept(s): Hebbian plasticity; Long-term Synaptic Plasticity; Volume transmission;
Implementer(s): Sweeney, Yann [yann.sweeney at ed.ac.uk];
Search NeuronDB for information about:  NO;
#Y Sweeney and C Clopath. 
# Emergent spatial synaptic structure from diffusive plasticity. European Journal of Neuroscience (2016).

Python with numpy (http://www.numpy.org/) will need to be installed. 
IPython Notebook (http://ipython.org/notebook.html) will be required in order to interact with the IPython Notebooks which launch the simulations and create the figures.

Functions for running simulations of receptive field development in a feedforward network with diffusive BCM are contained in ff_network_functions_spatial_clean.py. Receptive_field_formation_dBCM.ipynb is an IPython notebook which runs a simulation and produces Figure 5C,E from the paper.

For any queries, comments or requests please do not hesitate to contact me at y.sweeney@imperial.ac.uk 

Loading data, please wait...