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Negative Interspike Interval Correlations Increase the Neuronal Capacity for Encoding Time-Dependent Stimuli
- J. Neurosci
, 2001
"... this paper, we show that negative interspike interval (ISI) correlations, i.e., the tendency for long ISIs to be followed by short ISIs (and vice versa), reduce spike count variability, whereas positive ISI correlations increase spike count variability. Together, these effects lead to an optimal spi ..."
Abstract
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Cited by 22 (11 self)
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this paper, we show that negative interspike interval (ISI) correlations, i.e., the tendency for long ISIs to be followed by short ISIs (and vice versa), reduce spike count variability, whereas positive ISI correlations increase spike count variability. Together, these effects lead to an optimal spike counting time at which discriminability is maximal
Nonrenewal Statistics of Electrosensory Afferent Spike Trains: Implications for the Detection of Weak Sensory Signals
- J. Neurosci
, 2000
"... this paper, we analyze the variability of baseline spike activity recorded from P-type (probability coding) electrosensory afferent fibers in the weakly electric fish Apteronotus leptorhynchus (brown ghost knife fish). Objects near the fish that differ in impedance from the surrounding water modulat ..."
Abstract
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Cited by 16 (5 self)
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this paper, we analyze the variability of baseline spike activity recorded from P-type (probability coding) electrosensory afferent fibers in the weakly electric fish Apteronotus leptorhynchus (brown ghost knife fish). Objects near the fish that differ in impedance from the surrounding water modulate the self-generated electric field because of the fish's electric organ discharge (EOD). These modulations provide sensory cues that allow the fish to hunt and navigate in the dark using electrolocation (Rasnow, 1996) (for review, see Bullock and Heiligenberg, 1986). 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 f ully characterized
Transformations of an Auditory Temporal Code in the Medulla of a Sound-Producing Fish
"... this paper we demonstrate that one of the fundamental transformations of information in the auditory system of a sound-producing fish, Pollimyrus, takes place in the auditory medulla. We discovered a class of neurons in which evoked spiking patterns were relatively independent of the stimulus fine s ..."
Abstract
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this paper we demonstrate that one of the fundamental transformations of information in the auditory system of a sound-producing fish, Pollimyrus, takes place in the auditory medulla. We discovered a class of neurons in which evoked spiking patterns were relatively independent of the stimulus fine structure and appeared to reflect intrinsic properties of the neurons. These neurons generated sustained responses but were poorly phase-locked to tones compared with the primary afferents. The interval histograms showed that spike timing was regular. However, in contrast to primary afferents, the mode of the interspike interval distribution was independent of the period of tonal stimuli. The tuning of the neurons was broad, with best sensitivity in the same spectral region where these animals concentrate energy in their communication sounds. The physiology of these neurons was similar to that of the chopper neurons known in the auditory brainstem of mammals. Our findings suggest that this medullary transformation, from phase-locked afferent input to chopper-like physiology, is basic to vertebrate auditory processing, even within lineages that have not evolved a cochlea.

