Multicompartment leaky integrate and fire neuron modeling with multiexponentials
© McTavish et al; licensee BioMed Central Ltd. 2009
Published: 13 July 2009
Dendritic processing is paramount in the mammalian olfactory bulb where mitral cells modulate each other via granule cell interneurons that synapse on the mitral cells' lateral dendrites. This motivated us to construct computationally lean multicompartment leaky integrate and fire (LIF) neurons such that each compartment's membrane potential is described by six exponentials. This enables the model to avoid differential equations and calculate the membrane potential only upon receiving synaptic events and, like the LIF point neuron, employs simple rules for propagating and attenuating dendritic action potentials.
Methods and results
We first demonstrate the application of a multiexponential fitting algorithm  by casting the passive infinite cable response to a delta function at various normalized distances as a sum of six exponential functions. Because the multi-exponential fit is so accurate (see Figure 1 for normalized distances of X = 1 and X = 3), we, in turn, account for branching, tapering, and reflections in finite cables by employing a variant of the method proposed by Abbott , using our six exponentials as our "Green's function". Multicompartment cells built with these exponentials then process new events simply by updating the magnitudes of a compartment's six exponential functions.
Supported by NIH-NLM grant T5 LM009451-01 and NIH-NIDCD grant F31 DC009369-01A1.
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