PMID- 14758060
OWN - NLM
STAT- MEDLINE
DCOM- 20040419
LR  - 20220309
IS  - 0929-5313 (Print)
IS  - 0929-5313 (Linking)
VI  - 16
IP  - 2
DP  - 2004 Mar-Apr
TI  - State-dependent effects of Na channel noise on neuronal burst generation.
PG  - 87-112
AB  - We explore the effects of stochastic sodium (Na) channel activation on the 
      variability and dynamics of spiking and bursting in a model neuron. The complete 
      model segregates Hodgin-Huxley-type currents into two compartments, and undergoes 
      applied current-dependent bifurcations between regimes of periodic bursting, 
      chaotic bursting, and tonic spiking. Noise is added to simulate variable, finite 
      sizes of the population of Na channels in the fast spiking compartment. During 
      tonic firing, Na channel noise causes variability in interspike intervals (ISIs). 
      The variance, as well as the sensitivity to noise, depend on the model's 
      biophysical complexity. They are smallest in an isolated spiking compartment; 
      increase significantly upon coupling to a passive compartment; and increase again 
      when the second compartment also includes slow-acting currents. In this full 
      model, sufficient noise can convert tonic firing into bursting. During bursting, 
      the actions of Na channel noise are state-dependent. The higher the noise level, 
      the greater the jitter in spike timing within bursts. The noise makes the burst 
      durations of periodic regimes variable, while decreasing burst length duration 
      and variance in a chaotic regime. Na channel noise blurs the sharp transitions of 
      spike time and burst length seen at the bifurcations of the noise-free model. 
      Close to such a bifurcation, the burst behaviors of previously periodic and 
      chaotic regimes become essentially indistinguishable. We discuss biophysical 
      mechanisms, dynamical interpretations and physiological implications. We suggest 
      that noise associated with finite populations of Na channels could evoke very 
      different effects on the intrinsic variability of spiking and bursting 
      discharges, depending on a biological neuron's complexity and applied 
      current-dependent state. We find that simulated channel noise in the model neuron 
      qualitatively replicates the observed variability in burst length and interburst 
      interval in an isolated biological bursting neuron.
FAU - Rowat, Peter F
AU  - Rowat PF
AD  - Institute for Neural Computation, University of California at San Diego, 9500 
      Gillman Drive, La Jolla, CA 92093-0523, USA. prowat@ucsd.edu
FAU - Elson, Robert C
AU  - Elson RC
LA  - eng
PT  - Comparative Study
PT  - Journal Article
PT  - Research Support, U.S. Gov't, Non-P.H.S.
PL  - United States
TA  - J Comput Neurosci
JT  - Journal of computational neuroscience
JID - 9439510
RN  - 0 (Sodium Channels)
SB  - IM
MH  - Action Potentials/physiology
MH  - Animals
MH  - Electrophysiology
MH  - *Models, Neurological
MH  - Neurons/*physiology
MH  - *Noise
MH  - Nonlinear Dynamics
MH  - *Periodicity
MH  - Sodium Channels/*physiology
MH  - Stochastic Processes
EDAT- 2004/02/06 05:00
MHDA- 2004/04/20 05:00
CRDT- 2004/02/06 05:00
PHST- 2004/02/06 05:00 [pubmed]
PHST- 2004/04/20 05:00 [medline]
PHST- 2004/02/06 05:00 [entrez]
AID - 5265889 [pii]
AID - 10.1023/B:JCNS.0000014104.08299.8b [doi]
PST - ppublish
SO  - J Comput Neurosci. 2004 Mar-Apr;16(2):87-112. doi: 
      10.1023/B:JCNS.0000014104.08299.8b.