Rate fluctuations and fractional power-law noise recorded from cells in the lower auditory pathway of the cat

Teich, Malvin C.; Johnson, Don H.; Kumar, Anand R.; Turcott, Robert G.

doi:10.1016/0378-5955(90)90138-f

Cited by 64 publications

(36 citation statements)

References 23 publications

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“…We also calculated FF using different counting windows ranging from 1 to 35 ms (data not shown) and the resulting mean FF values were stable in the 5-35 ms range. At shorter durations, the FF increased and approached one, consistent with previous findings that report Poisson statistics for very short counting windows (Teich et al, 1990).…”

Section: Analysis Of Spike-count Reliabilitysupporting

confidence: 81%

“…Discharge patterns recorded from a variety of sensory systems, including auditory nerve, exhibit predominantly sub-Poisson statistics manifest by FFs less than one for time windows on the order of tens of milliseconds, suggesting reliable, nonrandom spike discharges (Teich and Khanna, 1985;Young and Barta, 1986;Berry et al, 1997;de Ruyter van Steveninck et al, 1997;DeWeese et al, 2003;Schaette et al, 2005). This is in contrast to the predominantly supra-Poisson statistics observed with longer counting windows (Teich et al, 1990;Lowen and Teich, 1992;Kelly et al, 1996). Over the counting window analyzed in this study, all auditory nerve fibers responded with sub-Poisson spike-count reliability (Figs.…”

Section: Spike-count Reliabilitycontrasting

confidence: 52%

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Adaptation Reduces Spike-Count Reliability, But Not Spike-Timing Precision, of Auditory Nerve Responses

Avissar¹,

Furman²,

Saunders³

et al. 2007

Journal of Neuroscience

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Sensory systems use adaptive coding mechanisms to filter redundant information from the environment to efficiently represent the external world. One such mechanism found in most sensory neurons is rate adaptation, defined as a reduction in firing rate in response to a constant stimulus. In auditory nerve, this form of adaptation is likely mediated by exhaustion of release-ready synaptic vesicles in the cochlear hair cell. To better understand how specific synaptic mechanisms limit neural coding strategies, we examined the trial-to-trial variability of auditory nerve responses during short-term rate-adaptation by measuring spike-timing precision and spike-count reliability. After adaptation, precision remained unchanged, whereas for all but the lowest-frequency fibers, reliability decreased. Modeling statistical properties of the hair cell-afferent fiber synapse suggested that the ability of one or a few vesicles to elicit an action potential reduces the inherent response variability expected from quantal neurotransmitter release, and thereby confers the observed count reliability at sound onset. However, with adaptation, depletion of the readily releasable pool of vesicles diminishes quantal content and antagonizes the postsynaptic enhancement of reliability. These findings imply that during the course of short-term adaptation, coding strategies that employ a rate code are constrained by increased neural noise because of vesicle depletion, whereas those that employ a temporal code are not.

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Section: Analysis Of Spike-count Reliabilitysupporting

confidence: 81%

Section: Spike-count Reliabilitycontrasting

confidence: 52%

Adaptation Reduces Spike-Count Reliability, But Not Spike-Timing Precision, of Auditory Nerve Responses

Avissar¹,

Furman²,

Saunders³

et al. 2007

Journal of Neuroscience

View full text Add to dashboard Cite

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“…Examples include the openings and closings of ion channels (Läuger, 1988;Millhauser et al, 1988;Liebovitch and Tóth, 1990;Teich et al, 1991;Lowen and Teich, 1993a-c); patterns of action-potential firings in the auditory system (Teich, 1989(Teich, , 1992Teich et al, 1990;Powers and Salvi, 1992;Kumar and Johnson, 1993;Kelly et al, 1996;Lowen and Teich, 1996), visual system (Turcott et al, 1995Teich et al, 1996aTeich et al, ,b, 1997, somatosensory cortex (Wise, 1981), and mesencephalic reticular formation (Grüneis et al, 1993); and even the sequence of human heartbeats (Kobayashi and Musha, 1982;Saul et al, 1988;Turcott andTeich, 1993, 1996). In almost all of these cases, the upper limit of the observed time over which fractal correlations exist is imposed by the duration of the recording.…”

Section: Discussionmentioning

confidence: 99%

Quantal Neurotransmitter Secretion Rate Exhibits Fractal Behavior

et al. 1997

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The rate of exocytic events from both neurons and nonneuronal cells exhibits fluctuations consistent with fractal (selfsimilar) behavior in time, as evidenced by a number of statistical measures. We explicitly demonstrate this for neurotransmitter secretion at Xenopus neuromuscular junctions and for rat hippocampal synapses in culture; the exocytosis of exogenously supplied neurotransmitter from cultured Xenopus myocytes and from rat fibroblasts behaves similarly. The magnitude of the fluctuations of the rate of exocytic events about the mean decreases slowly as the rate is computed over longer and longer time periods, the periodogram decreases in power-law manner with frequency, and the Allan factor (relative variance of the number of exocytic events) increases as a power-law function of the counting time. These features are hallmarks of self-similar behavior. Their description requires models that exhibit long-range correlation (memory) in event occurrences. We have developed a physiologically plausible model that accords with all of the statistical measures that we have examined. The appearance of fractal behavior at synapses, as well as in systems comprising collections of synapses, indicates that such behavior is ubiquitous in neural signaling. Key words: quantal secretion; vesicular exocytosis; miniature endplate currents; fractal; long-term correlation; lognormal process; Xenopus neuromuscular junction; myocyte autoreception; fibroblast; hippocampal synapseCommunication in the nervous system is mediated by actionpotential-initiated exocytosis of multiple vesicular packets (quanta) of neurotransmitter (Katz, 1966). Even in the absence of such action potentials, however, many neurons spontaneously release individual packets of neurotransmitter (Fatt and Katz, 1952). A packet may contain from 7000 to 10,000 molecules of acetylcholine (ACh), if we use the neuromuscular junction as an example (Kuffler and Yoshikami, 1975). On arrival at the postsynaptic membrane, the ACh molecules induce elementary endplate currents (EEC s), which take the form of nonstationary two-(or multi-) state on -off sequences (Sakmann, 1992). Current flows when the ACh channel is open (i.e., when its two binding sites are occupied by agonist) and ceases when the channel is closed. A postsynaptic miniature endplate current (M EPC) comprises some 1000 EEC s (Sakmann, 1992). It was shown by Del Castillo and Katz (1954) that superpositions of M EPC -like events comprise the postsynaptic endplate currents elicited by nerve impulses.It generally has been assumed that the sequence of MEPCs forms a memoryless stochastic process (Fatt and Katz, 1952). However, Rotshenker and Rahamimoff (1970) discovered that exocytosis in the frog neuromuscular junction can exhibit correlation (memory) over a period of seconds, provided that extracellular C a 2ϩ levels are elevated above their normal values. Cohen et al. (1974b) subsequently found such correlation (event clustering) even in the absence of elevated Ca 2ϩ levels. In this paper we study the statis...

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“…Interval distributions of experimentally observed spike trains often exhibit dependencies on prior activity and are thus nonrenewal (Kuffler et al, 1957;Werner and Mountcastle, 1963;Teich et al, 1990;L owen and Teich, 1992). Surrogate spike trains that preserved dependencies between adjacent ISIs were constructed from the afferent data.…”

Section: Binomiall Y Generated Spik E Train (B)mentioning

confidence: 99%

Nonrenewal Statistics of Electrosensory Afferent Spike Trains: Implications for the Detection of Weak Sensory Signals

2000

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The ability of an animal to detect weak sensory signals is limited, in part, by statistical fluctuations in the spike activity of sensory afferent nerve fibers. In weakly electric fish, probability coding (P-type) electrosensory afferents encode amplitude modulations of the fish's self-generated electric field and provide information necessary for electrolocation. This study characterizes the statistical properties of baseline spike activity in P-type afferents of the brown ghost knifefish, Apteronotus leptorhynchus. Shortterm variability, as measured by the interspike interval (ISI) distribution, is moderately high with a mean ISI coefficient of variation of 44%. Analysis of spike train variability on longer time scales, however, reveals a remarkable degree of regularity. The regularizing effect is maximal for time scales on the order of a few hundred milliseconds, which matches functionally relevant time scales for natural behaviors such as prey detection. Using highorder interval analysis, count analysis, and Markov-order analysis we demonstrate that the observed regularization is associated with memory effects in the ISI sequence which arise from an underlying nonrenewal process. In most cases, a Markov process of at least fourth-order was required to adequately describe the dependencies. Using an ideal observer paradigm, we illustrate how regularization of the spike train can significantly improve detection performance for weak signals. This study emphasizes the importance of characterizing spike train variability on multiple time scales, particularly when considering limits on the detectability of weak sensory signals. Key words: electrosensory afferent; electrolocation; interspike interval analysis; Markov process; spike train variability; weak signal detectionSurvival in an animal's natural environment is dependent on the ability to detect behaviorally relevant stimuli, such as those caused by predators and prey. Being able to reliably and efficiently detect such signals at weak levels confers a competitive advantage. Thus many sensory systems, including the electrosensory system discussed here, have presumably experienced selective pressures over the course of evolution to improve detection performance for weak sensory signals.The decision of whether or not a stimulus is present must ultimately be based on a change in the spike activity of primary afferent nerve fibers. In many cases, this change must be detected in the presence of ongoing spontaneous activity. Intuitively, a subtle change in spike activity caused by a weak external signal should be easier to detect when the baseline activity is regular and predictable than when it is irregular and subject to random fluctuations. To understand the limits on signal detection performance, it is thus important to characterize the variability of baseline activity in primary afferent spike trains.A common approach for characterizing spike train variability is by analysis of the first-order interspike interval (ISI) distribution (Hagiwara, 1954;Moore et al., ...

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Rate fluctuations and fractional power-law noise recorded from cells in the lower auditory pathway of the cat

Cited by 64 publications

References 23 publications

Adaptation Reduces Spike-Count Reliability, But Not Spike-Timing Precision, of Auditory Nerve Responses

Adaptation Reduces Spike-Count Reliability, But Not Spike-Timing Precision, of Auditory Nerve Responses

Quantal Neurotransmitter Secretion Rate Exhibits Fractal Behavior

Nonrenewal Statistics of Electrosensory Afferent Spike Trains: Implications for the Detection of Weak Sensory Signals

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