Long-range temporal anti-correlations in paddlefish electroreceptors

Abstract

The paddlefish perceives its world through an array of electroreceptor organs spread over its rostrum. Using Detrended-Fluctuation Analysis (DFA) we find long-range anticorrelations in sequences of time intervals between neural action potentials recorded from spontaneously firing electroreceptor afferents. In contrast, spontaneous discharges from mechanoreceptor afferents in the crayfish tailfan lack long-range correlations altogether. We interpret this finding as a consequence of the self-sustained oscillatory nature of the electroreceptor organ compared with the threshold-type dynamics of the mechanoreceptor.Short-time anti-correlations have been known in sensory biology for many years. An early report on electroreceptors similar to those studied here [1] showed that short time intervals tend to be followed by long time intervals and vice versa. This phenomenon has been attributed to the action of a noise-mediated, subthreshold oscillator [2]. A noise excursion may cause a premature threshold crossing (generating a spike) prior to the maximum in one cycle leading to a short time interval. The next spike will occur near the maximum of the subsequent cycle, lengthening the following time interval. Such anti-correlations must therefore decay with time constants comparable to the period of the slow oscillator (typically 20-300 milliseconds), and indeed such anti-correlations with characteristic timescales up to a few hundred milliseconds were recently reported for electroreceptor organs of the catfish [2] and the knifefish [3], using standard Markovian analysis.In contrast, we report here observations of long-range (time) anti-correlations (LRACs) (in the range 10-200 seconds) in fluctuations of the time intervals generated by the spontaneous firing of unstimulated ampullary electroreceptors (ER) [4] of the paddlefish, Polyodon spathula.

Nonrenewal spike train statistics: causes and functional consequences on neural coding

“…Alternating ISI correlations have been observed experimentally in paddlefish electroreceptors (Bahar et al 2001;Neiman and Russell 2001. The sensory epithelial hair cells display ~25-Hz oscillations that can be recorded as voltage or current fluctuations in the canals leading from skin pores to the sensory epithelia.…”

“…a negative ISI correlation coefficient is followed by a positive one, which is then followed by a negative one that is weaker in magnitude, etc…). This is, perhaps, seen most prominently in the electroreceptors of the paddlefish in which these serial correlation coefficients decay over hundreds of lags (Bahar et al 2001;Russell 2001, 2004). Some bursting electroreceptors of weakly electric fish display alternating ISI correlations, but they tend to decay over at most a few lags (Chacron et al 2001b(Chacron et al , 2005a.…”

“…The oldest report that is known to the authors is in the activity of motoneurons in humans (Hagiwara 1949). Subsequent studies have found nonrenewal spiking statistics in retinal cat retinal ganglion cells (Kuffler et al 1957;Rodieck 1967;Levine 1980), cat superior olivary neurons (Goldberg et al 1964;Tsuchitani and Johnson 1985), cat peripheral auditory fibers (Lowen and Teich 1992), cat cochlear nuclear neurons (Goldberg and Greenwood 1966), cat medullary sympathetic neurons (Lewis et al 2001), cat sympathetic efferent fibers (Floyd et al 1982), pigeon vestibular afferents (Correia and Landolt 1977), weakly electric fish electroreceptor neurons (Longtin and Racicot 1997;Chacron et al 2000;Ratnam and Nelson 2000;Chacron et al 2001b;Chacron et al 2005b;Gussin et al 2007), paddlefish electroreceptors (Bahar et al 2001;Neiman and Russell 2001;Neiman and Russell 2004), catfish electroreceptors (Schäfer et al 1995), weakly electric fish electrosensory pyramidal neurons (Doiron et al 2003;Chacron et al 2007), honeybee mushroom body neurons (Farkhooi et al 2009), grasshopper auditory neurons (Schwalger et al 2010), primate spinothalamic neurons (Surmeier et al 1989), rat mesencephalic reticular neurons (Lansky and Radil 1987), primate somatosensory cortical neurons (Yamamoto and Nakahama 1983;Lebedev and Nelson 1996;Nawrot et al 2007), as well as rat entorhinal cortical pyramidal and interneurons (Engel et al 2008). We note that the last five studies challenge the notion that cortical neurons can be described by a renewal process such as the Poisson process and shall return to this point later.…”

“…Organism and neuron type ISI correlation structure Bahar et al (2001), Neiman and Russell (2001), Neiman and Russell (2004) Paddlefish electroreceptors Alternating ISI correlations that decay over several lags Chacron et al (2000), Chacron et al (2005b), Gussin et al (2007), Longtin and Racicot (1997), Ratnam and Nelson (2000) Weakly electric fish electroreceptors Negative ISI correlation at lag 1 and positive ISI correlations that decay over several lags Chacron et al (2001b) Weakly electric fish electroreceptors Alternating ISI correlations that decay over a few lags Chacron et al (2007), Doiron et al (2003) Weakly electric fish electrosensory pyramidal neurons…”

Size Effects on Correlation Measures

“…Many examples can be mentioned: long-range correlations have been observed in DNA sequences [1,2], in cardiac dynamics (measuring interbeat time intervals) [3][4][5][6][7], in human electroencephalographic fluctuations [8,9], in human motor activity [10] and gait [11][12][13], in sensory receptors in neural systems [14], etc. Outside biophysical examples, long-range correlations are also present in meteorology [15][16][17][18][19], seismic signals [20], economics [21,22] and even in the properties of music [23].…”