Biophysical model for field potentials of networks of I&F neurons (beim Graben & Serafim 2013)

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"... Starting from a reduced three-compartment model of a single pyramidal neuron, we derive an observation model for dendritic dipole currents in extracellular space and thereby for the dendritic field potential (DFP) that contributes to the local field potential (LFP) of a neural population. ... Our reduced three-compartment scheme allows to derive networks of leaky integrate-and-fire (LIF) models, which facilitates comparison with existing neural network and observation models. ..."
1 . beim Graben P, Rodrigues S (2013) A biophysical observation model for field potentials of networks of leaky integrate-and-fire neurons Frontiers in Computational Neuroscience 6:1-13 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Extracellular;
Brain Region(s)/Organism:
Cell Type(s): Abstract integrate-and-fire leaky neuron;
Gap Junctions:
Simulation Environment: Brian; Python;
Model Concept(s): Extracellular Fields;
This is the readme for the Brian code associated with the paper:

Peter beim Graben and Serafim Rodrigues

A Biophysical observation model for field potentials of networks of
leaky-integrate-and-fire neurons.
Front. Comput. Neurosci, 04 January 2013
doi: 10.3389/fncom.2012.00100

This python code,, requires and runs under the Brian

Note for developers:

Note 1 : As it stands, the code is not effiecient (fast) as it does
not use the facilties vector processing and uses a lot of for-loops
which is not efficient. So it can be improved.
Note 2: Periodic thalamic input is not yet implemented.


1) This is a network of 5000 neurons, 80% of which excitatory, and 20%
2) The network is randomy connected (between pairs) with connection
   probability = 0.2.
3) Both Excitatory and Inhibitory neurons are described via LIF model.
4) The currents are double exponetial, but the excitatory currents can
   recieve external noise.

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