Biomedical Engineering Reference
In-Depth Information
that quantifi es the contact area between the neuron and the CNT should be inter-
preted only as average parameters. Similarly, R sp depends on the electrolyte resistiv-
ity and on geometrical parameters. As opposed to conventional extracellular coupling
of the membrane of cultured neurons with microtransducers integrated in the culture
substrate (Grattarola and Martinoia 1993 ; Martinoia et al. 2004 ) , R s is probably
strongly affected by the extremely intimate contact reported by our previous studies
(Mazzatenta et al. 2007 ). As anticipated in the model of Fig. 8b, d , pinching and
complete penetration of CNTs in the membrane lipid layers are certainly possible,
although so far only based on the interpretation of TEM microscopy images (Cellot
et al. 2008 ). In such a case, the biophysical model predicts a signal transduction
fundamentally different from metal electrodes, reminiscent of an intracellular,
though noninvasive, recording.
The full model implementation is available online at the ModelDB database for
the NEURON simulation environment (
ShowModel.asp?model=112086 ). The great advantage of employing a standard
neural simulator as NEURON, conceived specifi cally for biophysically detailed
models of neuronal excitability, is represented by the more natural implementation
of individual ion current kinetics accounting for neuronal excitability and signal
propagation even in complex morphologies (Traub and Miles 1991 ). There is in fact
no need to invoke time-invariant electrical equivalent circuits (Martinoia et al. 2004 ;
Storace et al. 1997 ; Chua 1980 ) for the convenience of specifi cation under standard
electrical circuit simulators (e.g., HSPICE, NGSPICE, etc.). Arbitrarily complex
morphologies and network architectures (Migliore et al. 2006 ; Markram 2006 ) can
be, then, immediately implemented. A large set of available channel kinetics and
neuronal point process can be also included in the model to account for a more
realistic biophysical description (e.g., from the SenseLab ModelDB - http://sense- ; Hines et al. 2004 ). Furthermore, the availability of opti-
mized numerical routines represents an explicit advantage for accuracy and
computation speed. Finally, the recent versions of the NEURON simulation envi-
ronment allows one to simulate additional linear (and nonlinear) circuit models by
no effort (Gold et al. 2006 ; Carnevale and Hines 2006 ), making the modeling of
extracellular signal recording and stimulation very natural in terms of electrical
equivalent models of the neuron-nanotube interface.
Toward a Deeper Understanding of CNT-Neuron Junctions
At the present time, our understanding of the biophysics of the CNT-neuron junction
is preliminary and the description of the electrical properties of nanotubes' mesh-
work outlined in the previous section is a simplifi ed one. The attempts, from our
group and from others (Gheith et al. 2006 ; Liopo et al. 2006 ; Mazzatenta et al. 2007 ) ,
to engineer CNTs as cell-culturing substrates are also elementary (see also Gabay
et al. 2007 ). In addition, the actual electron transport in a dense CNT meshwork is
certainly depending on additional details (Snow et al. 2003 ) . In particular, electrostatic
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