- Oral presentation
- Open Access
Mean field analysis gives accurate predictions of the behaviour of large networks of sparsely coupled and heterogeneous neurons
© Nicola et al; licensee BioMed Central Ltd. 2014
- Published: 21 July 2014
- Pyramidal Neuron
- Large Network
- Model Neuron
- Network Simulation
- Network Behaviour
Large networks of integrate-and-fire (IF) model neurons are often used to simulate and study the behaviour of biologically realistic networks. However, to fully study the large network behaviour requires an exploration of large regions of a multidimensional parameter space. Such exploration is generally not feasible with large network models, due to the computational time required to simulate a network with biologically significant size. To circumvent these difficulties we use a mean-field approach, based on the work of .
We consider a sparsely coupled, excitatory network of 10,000 Izhikevich model neurons , with Destexhe-type synapses . The cellular models were fit to hippocampal CA1 pyramidal neurons and have heterogeneous applied currents with a normal distribution. We derived a mean-field system for the network which consists of differential equations for the mean of the adaptation current and the synaptic conductance.
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