@ARTICLE{Demas2003,
  title       = "Developmental loss of synchronous spontaneous activity in the
                 mouse retina is independent of visual experience",
  author      = "Demas, Jay and Eglen, Stephen J and Wong, Rachel O L",
  affiliation = "Department of Anatomy and Neurobiology, Washington University
                 School of Medicine, St. Louis, Missouri 63110, USA.",
  abstract    = "In the immature retina, correlated spontaneous activity in the
                 form of propagating waves is thought to be necessary for the
                 refinement of connections between the retina and its targets.
                 The continued presence of this activity in the mature retina
                 would interfere with the transmission of information about the
                 visual scene. The mechanisms responsible for the disappearance
                 of retinal waves are not well understood, but one hypothesis
                 is that visual experience is important. To test this
                 hypothesis, we monitored the developmental changes in
                 spontaneous retinal activity of both normal mice and mice
                 reared in the dark. Using multi-electrode array recordings, we
                 found that retinal waves in normally reared mice are present
                 at postnatal day (P) 9 and begin to break down shortly after
                 eye opening, around P15. By P21, waves have disappeared, and
                 synchronous firing is comparable with that observed in the
                 adult (6 weeks). In mice raised in the dark, we found a
                 similar time course for the disappearance of waves. However,
                 at P15, dark-reared retinas occasionally showed abnormally
                 long periods of relative inactivity, not seen in controls.
                 Apart from this quiescence, we found no striking differences
                 between the patterns of spontaneous retinal activity from
                 normal and dark-reared mice. We therefore suggest that visual
                 experience is not required for the loss of synchronous
                 spontaneous activity.",
  journal     = "J. Neurosci.",
  volume      =  23,
  issue       =  7,
  pages       = "2851--2860",
  day         =  1,
  month       =  apr,
  year        =  2003,
  kind        = "Research Article",
  issn        = "0270-6474",
  pmid        = "12684472"
}

@ARTICLE{Eytan2006,
  title       = "Dynamics and effective topology underlying synchronization in
                 networks of cortical neurons",
  author      = "Eytan, Danny and Marom, Shimon",
  affiliation = "Department of Physiology and Biophysics, Faculty of Medicine,
                 Technion-Israel Institute of Technology, Haifa 32000, Israel.",
  abstract    = "Cognitive processes depend on synchronization and propagation
                 of electrical activity within and between neuronal assemblies.
                 In vivo measurements show that the size of individual
                 assemblies depends on their function and varies considerably,
                 but the timescale of assembly activation is in the range of
                 0.1-0.2 s and is primarily independent of assembly size. Here
                 we use an in vitro experimental model of cortical assemblies
                 to characterize the process underlying the timescale of
                 synchronization, its relationship to the effective topology of
                 connectivity within an assembly, and its impact on propagation
                 of activity within and between assemblies. We show that the
                 basic mode of assembly activation, {"}network spike,{"} is a
                 threshold-governed, synchronized population event of 0.1-0.2 s
                 duration and follows the logistics of neuronal recruitment in
                 an effectively scale-free connected network. Accordingly, the
                 sequence of neuronal activation within a network spike is
                 nonrandom and hierarchical; a small subset of neurons is
                 consistently recruited tens of milliseconds before others.
                 Theory predicts that scale-free topology allows for
                 synchronization time that does not increase markedly with
                 network size; our experiments with networks of different
                 densities support this prediction. The activity of
                 early-to-fire neurons reliably forecasts an upcoming network
                 spike and provides means for expedited propagation between
                 assemblies. We demonstrate this capacity by observing the
                 dynamics of two artificially coupled assemblies in vitro,
                 using neuronal activity of one as a trigger for electrical
                 stimulation of the other.",
  journal     = "J. Neurosci.",
  volume      =  26,
  issue       =  33,
  pages       = "8465--8476",
  day         =  16,
  month       =  aug,
  year        =  2006,
  kind        = "Research Article",
  issn        = "0270-6474",
  pmid        = "16914671",
  doi         = "10.1523/JNEUROSCI.1627-06.2006"
}

@ARTICLE{Gourevitch2007,
  title       = "A nonparametric approach for detection of bursts in spike
                 trains",
  author      = "Gour\'{e}vitch, Boris and Eggermont, Jos J",
  affiliation = "Department of Physiology and Biophysics, Department of
                 Psychology, University of Calgary, 2500 University Drive N.W.,
                 Calgary, Alberta, Canada. boris@pi314.net",
  abstract    = "In spike-train data, bursts are considered as a unit of neural
                 information and are of potential interest in studies of
                 responses to any sensory stimulus. Consequently, burst
                 detection appears to be a critical problem for which the
                 Poisson-surprise (PS) method has been widely used for 20
                 years. However, this method has faced some recurrent criticism
                 about the underlying assumptions regarding the interspike
                 interval (ISI) distributions. In this paper, we avoid such
                 assumptions by using a nonparametric approach for burst
                 detection based on the ranks of ISI in the entire spike train.
                 Similar to the PS statistic, a {"}Rank surprise{"} (RS)
                 statistic is extracted. A new algorithm performing an
                 exhaustive search of bursts in the spike trains is also
                 presented. Compared to the performances of the PS method on
                 realizations of gamma renewal processes and spike trains
                 recorded in cat auditory cortex, we show that the RS method is
                 very robust for any type of ISI distribution and is based on
                 an elementary formalization of the definition of a burst. It
                 presents an alternative to the PS method for non-Poisson spike
                 trains and is simple to implement.",
  journal     = "J. Neurosci. Methods",
  volume      =  160,
  issue       =  2,
  pages       = "349--358",
  day         =  15,
  month       =  mar,
  year        =  2007,
  kind        = "Research Article",
  issn        = "0165-0270",
  pmid        = "17070926",
  doi         = "10.1016/j.jneumeth.2006.09.024"
}

@ARTICLE{Legendy1985,
  title    = "Bursts and recurrences of bursts in the spike trains of
              spontaneously active striate cortex neurons",
  author   = "Leg\'{e}ndy, C R and Salcman, M",
  abstract = "Simultaneous recordings were made from small collections (2-7) of
              spontaneously active single units in the striate cortex of
              unanesthetized cats, by means of chronically implanted
              electrodes. The recorded spike trains were computer scanned for
              bursts of spikes, and the bursts were catalogued and studied. The
              firing rates of the neurons ranged from 0.16 to 32 spikes/s; the
              mean was 8.9 spikes/s, the standard deviation 7.0 spikes/s.
              Bursts of spikes were assigned a quantitative measure, termed
              Poisson surprise (S), defined as the negative logarithm of their
              probability in a random (Poisson) spike train. Only bursts having
              S greater than 10, corresponding to an occurrence rate of about
              0.01 bursts/1,000 spikes in a random spike train, were considered
              to be of interest. Bursts having S greater than 10 occurred at a
              rate of about 5-15 bursts/1,000 spikes, or about 1-5 bursts/min.
              The rate slightly increased with spike rate; averaging about 2
              bursts/min for neurons having 3 spikes/s and about 4.5 bursts/min
              for neurons having 30 spikes/s. About 21\% of the recorded units
              emitted significantly fewer bursts than the rest (below 1
              burst/1,000 spikes). The percentage of these neurons was
              independent of spike rate. The spike rate during bursts was found
              to be about 3-6 times the average spike rate; about the same for
              longer as for shorter bursts. Bursts typically contained 10-50
              spikes and lasted 0.5-2.0 s. When the number of spikes in the
              successively emitted bursts was listed, it was found that in some
              neurons these numbers were not distributed at random but were
              clustered around one or more preferred values. In this sense,
              bursts occasionally {"}recurred{"} a few times in a few minutes.
              The finding suggests that neurons are highly reliable. When
              bursts of two or more simultaneously recorded neurons were
              compared, the bursts often appeared to be temporally close,
              especially between pairs of neurons recorded by the same
              electrode; but bursts seldom started and ended simultaneously on
              two channels. Recurring bursts emitted by one neuron were
              occasionally accompanied by time-locked recurring bursts by other
              neurons.",
  journal  = "J. Neurophysiol.",
  volume   =  53,
  issue    =  4,
  pages    = "926--939",
  day      =  1,
  month    =  apr,
  year     =  1985,
  kind     = "Research Article",
  issn     = "0022-3077",
  pmid     = "3998798"
}

@ARTICLE{Tokdar2010,
  title       = "Detection of bursts in extracellular spike trains using hidden
                 semi-Markov point process models",
  author      = "Tokdar, Surya and Xi, Peiyi and Kelly, Ryan C and Kass, Robert
                 E",
  affiliation = "Department of Statistical Science, Duke University, Durham, NC
                 27708, USA. st118@stat.duke.edu",
  abstract    = "Neurons in vitro and in vivo have epochs of bursting or {"}up
                 state{"} activity during which firing rates are dramatically
                 elevated. Various methods of detecting bursts in extracellular
                 spike trains have appeared in the literature, the most widely
                 used apparently being Poisson Surprise (PS). A natural
                 description of the phenomenon assumes (1) there are two hidden
                 states, which we label {"}burst{"} and {"}non-burst,{"} (2)
                 the neuron evolves stochastically, switching at random between
                 these two states, and (3) within each state the spike train
                 follows a time-homogeneous point process. If in (2) the
                 transitions from non-burst to burst and burst to non-burst
                 states are memoryless, this becomes a hidden Markov model
                 (HMM). For HMMs, the state transitions follow exponential
                 distributions, and are highly irregular. Because observed
                 bursting may in some cases be fairly regular-exhibiting
                 inter-burst intervals with small variation-we relaxed this
                 assumption. When more general probability distributions are
                 used to describe the state transitions the two-state point
                 process model becomes a hidden semi-Markov model (HSMM). We
                 developed an efficient Bayesian computational scheme to fit
                 HSMMs to spike train data. Numerical simulations indicate the
                 method can perform well, sometimes yielding very different
                 results than those based on PS.",
  journal     = "J. Comput. Neurosci.",
  volume      =  29,
  issue       = "1-2",
  pages       = "203--212",
  day         =  21,
  month       =  aug,
  year        =  2010,
  kind        = "Research Article",
  issn        = "0929-5313",
  pmid        = "19697116",
  doi         = "10.1007/s10827-009-0182-2"
}

@ARTICLE{Wong1993,
  title       = "Transient period of correlated bursting activity during
                 development of the mammalian retina",
  author      = "Wong, R. O. L. and Meister, M. and Shatz, C. J.",
  affiliation = "Department of Neurobiology, Stanford University School of
                 Medicine, California 94305.",
  abstract    = "The refinement of early connections in the visual pathway
                 requires electrical activity in the retina before the onset of
                 vision. Using a multielectrode array, we have shown that the
                 spontaneous activity of cells in the neonatal ferret retina is
                 correlated by patterns of periodically generated traveling
                 waves. Here, we examine developmental changes in the
                 characteristics of the waves and show that retinal ganglion
                 cells participate in these patterns of activity, which are
                 seen during the same period as synaptic modification in the
                 lateral geniculate nucleus; that the waves subside gradually
                 as the connectivity in the lateral geniculate nucleus
                 stabilizes; and that their spatial structure allows for
                 refinement of the retinotopic map, as well as for eye-specific
                 segregation in the lateral geniculate nucleus.",
  journal     = "Neuron",
  volume      =  11,
  issue       =  5,
  pages       = "923--938",
  day         =  1,
  month       =  nov,
  year        =  1993,
  kind        = "Research Article",
  annote      = "waves-p",
  issn        = "0896-6273",
  pmid        = "8240814",
  doi         = "10.1016/0896-6273(93)90122-8"
}


@MANUAL{neuroexplorer,
  title  = "{NeuroExplorer} Manual",
  author = "{NexTechnologies}",
  year   =  2012
}



