NENS220 Computational methods in Neuroscience. John Huguenard and Terry Sanger. Goals of the course. Overview of computational methods Mathematical techniques for creating models of neural behavior - the tools of computational methods. Computational Modeling.
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NENS220 Computational methods in Neuroscience
John Huguenard and Terry Sanger
Modeling of realistic neurons and networks
Neuroelectronics, Part I
Pyramidal Neurons in Layer V
Feng et al., (2000) Neuron 28:41
Recurrent excitatory connections are prominent.
Function: Amplification of signals for enhanced feature detection.
Rodney Douglas & Kevan Martin
Modified From: Karube et al., (2004) J Neurosci 24:2853-65
Mainen & Sejnowski, 1996
Electrical properties of neurons
Dominated by membrane capacitance
Neurons are integrators
whose time constant is dynamically variable
Spike output depends on voltage-dependent gating of ion channels
Passive properties of neurons
Semipermeable lipid bilayer membrane with high [K+]i maintained by electrogenic pump (ATPase)
Equivalent radius ~ 25 mm,
Surface Area ~ 8000 mm2=.008 mm2=8e-5cm2
EK = -75 mV
ENa = +50 mV
ECl = -60 mV
ECa = +100 mV
Parallel conductance model
g = slope
Better description than
ohmic for some channels
e.g. Ca2+, K+