Serial interval statistics of spontaneous activity in cortical neurons in vivo and in vitro

Neurocomputing

Volumn 70, Issue 10-12, 2007, Pages 1717-1722

  Nawrot, Martin P ^a,b,c   Boucsein, Clemens ^c   Rodriguez Molina, Victor ^c   Aertsen, Ad ^c   Grün, Sonja ^a,b   Rotter, Stefan ^c  

^a FREIE UNIVERSITÄT BERLIN   (Germany)

^b BERNSTEIN CENTER FOR COMPUTATIONAL NEUROSCIENCE   (Germany)

^c UNIVERSITY OF FREIBURG   (Germany)

Author keywords

Fano factor; Markov order; Renewal process; Serial interval correlation

Indexed keywords

BIOACTIVITY; COMPUTER SIMULATION; CORRELATION METHODS; MARKOV PROCESSES; STATISTICS;

CORTICAL NEURONS; FANO FACTOR; MARKOV ORDER; SERIAL INTERVAL CORRELATION; SPONTANEOUS ACTIVITY;

NEURONS;

ANIMAL CELL; ANIMAL TISSUE; ARTICLE; BRAIN CELL; BRAIN SLICE; CONTROLLED STUDY; CORRELATION ANALYSIS; EXPERIMENTAL MODEL; IN VITRO STUDY; IN VIVO STUDY; INTRACELLULAR RECORDING; MATHEMATICAL ANALYSIS; NEOCORTEX; NERVE CELL MEMBRANE STEADY POTENTIAL; NERVE CELL STIMULATION; NONHUMAN; PATCH CLAMP; PRIORITY JOURNAL; SPIKE WAVE;

EID: 34247537902     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2006.10.101     Document Type: Article

References (27)

1. Baddeley R., Abbott L.F., Booth M.J.A., Sengpiel F., Freeman T., Wakeman E.A., and Rolls E.T. Responses of neurons in primary and inferior temporal visual cortices to natural scenes. Proc. Roy. Soc. (Lond.) 264 (1997) 1775-1783
2. Benda J., and Herz V.M. A universal model for spike-frequency adaptation. Neural. Comput. 15 (2003) 2523-2546
3. Boucsein C., Nawrot M.P., Rotter S., Aertsen A., and Heck D. Controlling synaptic input patterns in vitro by dynamic photo stimulation. J. Neurophysiol. 94 (2005) 2948-2958
4. Brandman R., and Nelson M.E. A simple model of long-term spike train regularization. Neural. Comput. 14 (2002)
5. Burkitt A.N. A review on the integrate-and-fire neuron model: I. Homogenous synaptic input. Biol. Cybern. 95 1 (2006) 1-19
6. Chacron M.J., Lindner B., and Longtin A. Noise shaping by interval correlations increases information transfer. Phys. Rev. Lett. 92 8 (2004) 11-14
7. Chacron M.J., Longtin A., and Maler L. Negative interspike interval correlations increase the neuronal capacity for encoding time-dependent stimuli. J. Neurosci. 21 14 (2001) 5328-5343
8. Chacron M.J., Longtin A., St-Hilaire M., and Maler L. Suprathreshold stochastic firing dynamic with memory in p-type electroreceptors. Phys. Rev. Lett. 85 7 (2000) 1576-1579
9. Chacron M.J., Pakdaman K., and Longtin A. Interspike interval correlations, memory, adaptation, and refractoriness in a leaky integrate-and-fire model with threshold fatigue. Neural. Comput. 15 (2003) 253-278
10. Cox D.R., and Lewis P.A.W. The Statistical Analysis of Series of Events, Methuen's Monographs on Applied Probability and Statistics (1966), Methuen, London
11. Goense J.B.M., and Ratnam R. Continuous detection of weak sensory signals in afferent spike trains: the role of anti-correlated interspike intervals in detection performance. J. Comput. Physiol. 189 (2003)
12. Johnson D.H. Point process models of single-neuron discharges. J. Comput. Neurosci. 3 4 (1996) 275-299
13. Leger J.-F., Stern E.A., Aertsen A., and Heck D. Synaptic integration in rat frontal cortex shaped by network activity. J. Neurophysiol. 93 (2005) 281-293
14. Lindner B. Interspike interval statistics of neurons driven by colored noise. Phys. Rev. E 69 (2004) 11-14
15. Lüdtke N., and Nelson M.E. Short-term synaptic plasticity can enhance weak signal detectability in non-renewal spike trains. Neural. Comput. 18 12 (2006) 2879-2916
16. Middleton J., Chacron M.J., Lindner B., and Longtin A. Firing statistics of a neuron driven by long-range correlated noise. Phys. Rev. E 68 (2003) 1-8
17. Nakahama H., Yamamoto M., Ishii N., Fujii H., and Aya K. Dependency as a measure to estimate the order and the values of Markov processes. Biol. Cybern. 25 (1977) 209-226
18. Nawrot M.P., Aertsen A., and Rotter S. Single-trial estimation of neuronal firing rates: from single-neuron spike trains to population activity. J. Neurosci. Meth. 94 (1999) 81-92
19. M.P. Nawrot, C. Boucsein, V. Rodriguez Molina, A. Aertsen, S. Rotter, Unbiased and time-resolved measurement of variability in neuronal spike trains, submitted.
20. M.P. Nawrot, C. Mehring, V. Rodriguez-Molina, D. Heck, A. Aertsen, S. Rotter, How renewal-like are cortical neurons? Investigation of serial interval statistics in vitro and in vivo, in: Proceedings of the Sixth International IBRO World Congress 2003, Prague, 2003, p. 4382.
21. M.P. Nawrot, Ongoing activity in cortical networks: noise, variability and context, Ph.D. Thesis, Albert-Ludwigs-University, Freiburg, June 2003.
22. Ratnam R., and Nelson M.E. Nonrenewal statistics of electrosensory afferent spike trains: implications for the detection of weak sensory signals. J. Neurosci. 20 17 (2000) 6672-6683
23. Reich D.S., Victor J.D., and Knight B.W. The power ratio and the interval map: spiking models and extracellular recordings. J. Neurosci. 18 (1998) 10090-10104
24. Teich M.C., Turcott R.G., and Siegel R.M. Temporal correlation in cat striate-cortex neural spike trains. IEEE Eng. Med. Biol. Mag. 15 5 (1996) 79-87
25. Tuckwell H.C. Introduction to Theoretical Neurobiology vol. 2 (1988), Cambridge University Press, Cambridge
26. Wang X.-J. Calcium coding and adaptive temporal computation in cortical pyramidal neurons. J. Neurophysiol. 79 (1998) 1549-1566
27. Zacksenhouse M., Johnson D.H., Williams J., and Tsuchitani C. Single-neuron modeling of LSO unit responses. J. Neurophysiol. 79 (1998) 3098-3110