PMID- 26203657
OWN - NLM
STAT- MEDLINE
DCOM- 20160502
LR  - 20181113
IS  - 1932-6203 (Electronic)
IS  - 1932-6203 (Linking)
VI  - 10
IP  - 7
DP  - 2015
TI  - Random Sampling with Interspike-Intervals of the Exponential Integrate and Fire 
      Neuron: A Computational Interpretation of UP-States.
PG  - e0132906
LID - 10.1371/journal.pone.0132906 [doi]
LID - e0132906
AB  - Oscillations between high and low values of the membrane potential (UP and DOWN 
      states respectively) are an ubiquitous feature of cortical neurons during slow 
      wave sleep and anesthesia. Nevertheless, a surprisingly small number of 
      quantitative studies have been conducted only that deal with this phenomenon's 
      implications for computation. Here we present a novel theory that explains on a 
      detailed mathematical level the computational benefits of UP states. The theory 
      is based on random sampling by means of interspike intervals (ISIs) of the 
      exponential integrate and fire (EIF) model neuron, such that each spike is 
      considered a sample, whose analog value corresponds to the spike's preceding ISI. 
      As we show, the EIF's exponential sodium current, that kicks in when balancing a 
      noisy membrane potential around values close to the firing threshold, leads to a 
      particularly simple, approximative relationship between the neuron's ISI 
      distribution and input current. Approximation quality depends on the frequency 
      spectrum of the current and is improved upon increasing the voltage baseline 
      towards threshold. Thus, the conceptually simpler leaky integrate and fire neuron 
      that is missing such an additional current boost performs consistently worse than 
      the EIF and does not improve when voltage baseline is increased. For the EIF in 
      contrast, the presented mechanism is particularly effective in the 
      high-conductance regime, which is a hallmark feature of UP-states. Our 
      theoretical results are confirmed by accompanying simulations, which were 
      conducted for input currents of varying spectral composition. Moreover, we 
      provide analytical estimations of the range of ISI distributions the EIF neuron 
      can sample from at a given approximation level. Such samples may be considered by 
      any algorithmic procedure that is based on random sampling, such as Markov Chain 
      Monte Carlo or message-passing methods. Finally, we explain how spike-based 
      random sampling relates to existing computational theories about UP states during 
      slow wave sleep and present possible extensions of the model in the context of 
      spike-frequency adaptation.
FAU - Steimer, Andreas
AU  - Steimer A
AD  - Department of Neurology, Inselspital\Bern University Hospital\University Bern, 
      Bern, Switzerland.
FAU - Schindler, Kaspar
AU  - Schindler K
AD  - Department of Neurology, Inselspital\Bern University Hospital\University Bern, 
      Bern, Switzerland.
LA  - eng
SI  - figshare/10.6084/M9.FIGSHARE.14​05577
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20150723
PL  - United States
TA  - PLoS One
JT  - PloS one
JID - 101285081
SB  - IM
MH  - Action Potentials/*physiology
MH  - *Computer Simulation
MH  - *Models, Neurological
MH  - Neurons/*physiology
MH  - Sampling Studies
MH  - Time Factors
PMC - PMC4512685
COIS- Competing Interests: The authors have declared that no competing interests exist.
EDAT- 2015/07/24 06:00
MHDA- 2016/05/03 06:00
PMCR- 2015/07/23
CRDT- 2015/07/24 06:00
PHST- 2015/01/19 00:00 [received]
PHST- 2015/06/22 00:00 [accepted]
PHST- 2015/07/24 06:00 [entrez]
PHST- 2015/07/24 06:00 [pubmed]
PHST- 2016/05/03 06:00 [medline]
PHST- 2015/07/23 00:00 [pmc-release]
AID - PONE-D-15-02580 [pii]
AID - 10.1371/journal.pone.0132906 [doi]
PST - epublish
SO  - PLoS One. 2015 Jul 23;10(7):e0132906. doi: 10.1371/journal.pone.0132906. 
      eCollection 2015.