Coding of Stimuli by Ampullary Afferents inGnathonemus petersii

Abstract

Weakly electric fish use electroreception for both active and passive electrolocation and for electrocommunication. While both active and passive electrolocation systems are prominent in weakly electric Mormyriform fishes, knowledge of their passive electrolocation ability is still scarce. To better estimate the contribution of passive electric sensing to the orientation toward electric stimuli in weakly electric fishes, we investigated frequency tuning applying classical input-output characterization and stimulus reconstruction methods to reveal the encoding capabilities of ampullary receptor afferents. Ampullary receptor afferents were most sensitive (threshold: 40 μV/cm) at low frequencies (<10 Hz) and appear to be tuned to a mix of amplitude and slope of the input signals. The low-frequency tuning was corroborated by behavioral experiments, but behavioral thresholds were one order of magnitude higher. The integration of simultaneously recorded afferents of similar frequency-tuning resulted in strongly enhanced signal-to-noise ratios and increased mutual information rates but did not increase the range of frequencies detectable by the system. Theoretically the neuronal integration of input from receptors experiencing opposite polarities of a stimulus (left and right side of the fish) was shown to enhance encoding of such stimuli, including an increase of bandwidth. Covariance and coherence analysis showed that spiking of ampullary afferents is sufficiently explained by the spike-triggered average, i.e., receptors respond to a single linear feature of the stimulus. Our data support the notion of a division of labor of the active and passive electrosensory systems in weakly electric fishes based on frequency tuning. Future experiments will address the role of central convergence of ampullary input that we expect to lead to higher sensitivity and encoding power of the system.

Sensory coding in oscillatory electroreceptors of paddlefish

“…Therefore, RR coherence is used to estimate an upper bound of mutual information rate. 24,45 Closeness of the SR coherence to the square root of the RR coherence indicates that a linear encoding scenario is optimal, whereas large departures indicate that a linear encoding model is not appropriate.…”

Adaptive responses of peripheral lateral line nerve fibres to sinusoidal wave stimuli

“…Neuronally, sensory adaptation is characterized by a decrease in responsiveness over the duration of a constant-amplitude stimulus (Adrian 1928). This phenomenon, generally described as spike frequency adaptation, has been found in numerous studies in both vertebrate and invertebrate sensory systems, e.g., in the visual (e.g., Laughlin 1989;Peron and Gabbiani 2009), olfactory (e.g., Demmer and Kloppenburg 2009), electrosensory (e.g., Nelson et al 1997;Benda et al 2005;Engelmann et al 2010), and auditory system (e.g., Westermann and Smith 1984;Epping 1990; Givois and Pollack 2000;Ronacher and Hennig 2004). Adaptive responses can result from different mechanisms, including the transduction process of receptor neurons (Hudspeth et al 2000;Gollisch and Herz 2004;Albert et al 2007), synaptic depression (Abbott et al 1997;Chance et al 1998), inhibitory inputs (Finlayson and Adam 1997;Ingham and McAlpine 2004), or ionic currents modulating spike generation (e.g., Benda and Herz 2003;Benda and Tabak 2014;Benda et al 2010).…”

“…As recently shown, the ampullary receptors of bony fish are evolutionary derived from lateral line receptors (Modrell et al 2011). Various reports have demonstrated that the responses to sustained DC step stimuli of afferent nerve fibres innervating ampullary (Engelmann et al 2010) and tuberous receptors (Xu et al 1996;Benda et al 2005) are adapting. Different models have been applied to in vivo data, including exponential, logarithmic, and power law models, to describe the time course of adaptation of electrosensory afferents (e.g., Benda et al 2010;Clarke et al 2013;Engelmann et al 2010;Xu et al 1996).…”

“…Various reports have demonstrated that the responses to sustained DC step stimuli of afferent nerve fibres innervating ampullary (Engelmann et al 2010) and tuberous receptors (Xu et al 1996;Benda et al 2005) are adapting. Different models have been applied to in vivo data, including exponential, logarithmic, and power law models, to describe the time course of adaptation of electrosensory afferents (e.g., Benda et al 2010;Clarke et al 2013;Engelmann et al 2010;Xu et al 1996). The stimuli applied in the present study were sinusoidal and ramped and thus differ from those used to stimulate ampullary receptors (Engelmann et al 2010) but bot from this used to stimulate tuberous receptors (Xu et al 1996;Benda et al 2005) which encode the amplitude modulation of a quasisine-wave stimulus (the fish's electric organ discharge).…”

“…It was shown that increasing stimulus duration resulted in proportionally longer adaptation, i.e., greater time constants among electrosensory afferents, and that adaptation disappeared when stimulus amplitude was increased slowly with a constant slope over more than a second or exponentially (Engelmann et al 2010). In the present study, we varied the duration of stimulus rise time.…”