Temporal precision and reliability in the velocity regime of a hair-cell sensory system: the mechanosensory lateral line of goldfish,Carassius auratus

Abstract

Fish and aquatic frogs detect minute water motion by means of a specialized mechanosensory system, the lateral line. Ubiquitous in fish, the lateral-line system is characterized by hair-cell based sensory structures across the fish's surface called neuromasts. These neuromasts occur free-standing on the skin as superficial neuromasts (SN) or are recessed into canals as canal neuromasts. SNs respond to rapid changes of water velocity in a small layer of fluid around the fish, including the so-called boundary layer. Although omnipresent, the boundary layer's impact on the SN response is still a matter of debate. For the first time using an information-theoretic approach to this sensory system, we have investigated the SN afferents encoding capabilities. Combining covariance analysis, phase analysis, and modeling of recorded neuronal responses of primary lateral line afferents, we show that encoding by the SNs is adequately described as a linear, velocity-responsive mechanism. Afferent responses display a bimodal distribution of opposite Wiener kernels that likely reflected the two hair-cell populations within a given neuromast. Using frozen noise stimuli, we further demonstrate that SN afferents respond in an extremely precise manner and with high reproducibility across a broad frequency band (10-150 Hz), revealing that an optimal decoder would need to rely extensively on a temporal code. This was further substantiated by means of signal reconstruction of spike trains that were time shifted with respect to their original. On average, a time shift of 3.5 ms was enough to diminish the encoding capabilities of primary afferents by 70%. Our results further demonstrate that the SNs' encoding capability is linearly related to the stimulus outside the boundary layer, and that the boundary layer can, therefore, be neglected while interpreting lateral line response of SN afferents to hydrodynamic stimuli.

Superficial neuromasts facilitate non-visual feeding by larval striped bass (Morone saxatilis)

“…However, physically blocking the canal organs of the blind cave fish Anoptichthys jordani indicated that superficial neuromasts detected prey items that produce vibratory stimuli (Abdel-Latif et al, 1990). The vibrations emitted by Artemia are ≤10Hz (Barlow and Sleigh, 1980), at the low end of the detection range of superficial neuromasts, at least in goldfish, Carassius auratus (Goulet et al, 2012).…”

Multiplexed temporal coding of electric communication signals in mormyrid fishes

“…Here we focus on temporal codes, in which the precise timing of action potentials carries information (Lestienne, 2001;Theunissen and Miller, 1995). Temporal codes have been implicated in the processing of somatosensory (Jones et al, 2004), olfactory (Laurent, 1997), gustatory (Di Lorenzo et al, 2009), visual (Victor and Purpura, 1996), auditory (deCharms and Merzenich, 1996), vestibular (Sadeghi et al, 2007), mechanosensory lateral line (Goulet et al, 2012) and electrosensory stimuli (Carlson, 2008b).…”

Sensing External and Self-Motion with Hair Cells: A Comparison of the Lateral Line and Vestibular Systems from a Developmental and Evolutionary Perspective

“…The force acting on the cupula and thus the shear stress that is acting on the neuromast [Prandtl 1904; Schlichting and Gersten 1979] depends on the water velocity as higher water velocities result in a thinner boundary layer. This renders the movement of the cupula, and hence the responsiveness of the superficial neuromasts, sensitive to the velocity of local water motion [Chagnaud et al 2008a; Chagnaud et al 2008b; Engelmann et al 2002; Goulet et al 2008; Goulet et al 2012; Kroese and Schellart 1992; Kroese et al 1978; Voigt et al 2000]. The sensitivity of canal neuromasts depends on both, the filter properties of the cupula (see above) and the morphology of the canal.…”

“…For the superficial neuromast system the precision of this encoding strategy is remarkable (Fig. 5C) [Goulet et al 2012], suggesting that the low sensitivity pathway is well suited to encode the minute spatio-temporal differences characteristic for global flow fields, i.e., hydrodynamic patterns spanning an animals sensory surface (Fig. 5B).…”