INVESTIGADORES
MONGIAT lucas Alberto
congresos y reuniones científicas
Título:
3. A Bayesian approach for fitting integrate-and-fire model parameters to electrophysiological data obtained from adult-born neurons
Autor/es:
TUOMO MÄKI-MARTTUNEN; ANTONIA MARÍN-BURGIN; LUCAS A. MONGIAT; EMILIO KROPFF; M BELEN PARDI; MARJA-LEENA LINNE; ALEJANDRO F. SCHINDER
Lugar:
Huerta Grande, Cordoba
Reunión:
Congreso; Reunión Anual Sociedad Argentina de Investigación en Neurociencia; 2012
Institución organizadora:
Sociedad Argentina de Investigación en Neurociencia
Resumen:
We have recently shown that immature granule cells (GCs)
of the adult dentate gyrus display a low threshold for activation rendered by
their high excitation/inhibition balance [1,2]. To better understand the underlying intrinsic
and synaptic mechanisms we now present and apply a data integrative method for
fitting integrate-and-fire model parameters. We show how prior data on neuronal
properties, such as membrane resistance and threshold potential for spiking [1],
can be combined with data obtained from electrophysiological recordings in
acute hippocampal slices to obtain a single-neuron model capable of predicting
the output to a given input. The data consists of 1) spiking frequency measured
against varying injected current and 2) spike trains of GCs observed as a
response for entorhinal inputs [2]. For the latter, post-synaptic excitatory
and inhibitory currents are collected and used for the parameter fitting and
prediction. Our results show that the presented Bayesian method succeeds well
in the integrative fitting of the data and that qualitatively correct
predictions can be obtained using the model, despite the simplicity of
integrate-and-fire dynamics.