Stimulus filtering and electroreception: Tuberous electroreceptors in three species of Gymnotoid fish

Hopkins, Carl D.

doi:10.1007/bf00605531

Cited by 261 publications

(160 citation statements)

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“…P-type afferents respond to the strength of amplitude modulations (AMs) by increasing or decreasing their probability of firing (Scheich et al, 1973;Bastian, 1981; for review, see Zakon, 1986). Their AM response characteristics have been well studied (Hagiwara et al, 1965;Scheich et al, 1973;Hopkins, 1976;Bastian, 1981;Shumway, 1989;Wessel et al, 1996;Xu et al, 1996;Nelson et al, 1997), but variability of baseline spike activity has not been fully characterized.Infrared video recordings of prey capture behavior in Apteronotus performed in our laboratory have been used to estimate the behavioral threshold for detecting small prey (Daphnia magna, 2-3 mm in length) in the dark. At the time of detection, we estimate that the prey gives rise to an AM signal that transiently changes the firing probability of P-type afferents by only ϳ1% (Nelson and MacIver, 1999).…”

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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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mentioning

confidence: 99%

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

2000

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“…The fish were able to extract the intensity of any harmonic (at least up to the third) close to their EOD frequency from any waveform, whether the frequency was subharmonic or not. This is what should be expected from the known properties of electroreceptors (T-and especially Preceptors) which are approximately tuned to the EOD frequency, resembling broad bandpass filters (Scheich et al 1973;Hopkins 1976;Viancour 1979a,b). These properties also explain why the strength of the JAR is progressively reduced for frequencies that are two, three or more times the EOD frequency.…”

Section: Principles Of Wave Analysis As Revealed By Jar Experimentsmentioning

confidence: 53%

“…From the approximate match of "best" frequencies of tuberous electroreceptors with discharge frequencies (Scheich et al 1973, Hopkins 1976), recognition of conspecifics by their discharge frequency was inferred (Hopkins and Heiligenberg 1978). Only the fundamental frequency component of the discharge would be important, all other harmonics being filtered out by the bandpass properties of the receptors.…”

Section: Gymnotiform Wave Speciesmentioning

confidence: 99%

Electrocommunication in Teleost Fishes: Behavior and Experiments

Hagedorn¹,

Krämer²

1992

Copeia

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“…The baseline EOD oscillation serves as a carrier signal and a target object induces an amplitude modulation (AM) of this carrier signal. P-type units are tuned to the carrier frequency of the fish's own EOD, with a V-shaped tuning curve (Hopkins 1976). This filtering helps improve the SNR by filtering out background electrical noise in frequency bands other than those close to the fish's own EOD that are functionally important for electrolocation and electrocommunication.…”

Section: Information Coding Properties Of P-type Unitsmentioning

confidence: 99%

Target Detection, Image Analysis, and Modeling

Nelson

Electroreception

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Stimulus filtering and electroreception: Tuberous electroreceptors in three species of Gymnotoid fish

Cited by 261 publications

References 45 publications

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

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

Electrocommunication in Teleost Fishes: Behavior and Experiments

Target Detection, Image Analysis, and Modeling

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