MEMS based hair flow-sensors as model systems for acoustic perception studies

Abstract

Arrays of MEMS fabricated flow sensors inspired by the acoustic flow-sensitive hairs found on the cerci of crickets, have been designed, fabricated and characterized. The hairs consist of up to 1 mm long SU-8 structures mounted on suspended membranes with normal translational and rotational degrees of freedom. Electrodes on the membrane and on the substrate form variable capacitors allowing for capacitive read-out. Capacitance versus voltage, frequency dependency and directional sensitivity measurements have been successfully carried out on fabricated sensor arrays, showing the viability of the concept. The sensors form a model-system allowing for investigations on sensory acoustics by their arrayed nature, their adaptivity via electrostatic interaction (frequency tuning and parametric amplification) and their susceptibility to noise (stochastic resonance)

Interaction between arthropod filiform hairs in a fluid environment

“…We are interested in how the information about the air currents is represented by the motion of the filiform hairs, translated into the neural activity of the hair-attached afferent neurons and processed by the small set of interneurons in the terminal ganglion, before being passed on to higher processing stages. Apart from understanding the entire information pathway, we are also interested in uncovering operational principles that may be common with those in the auditory system in humans, or may be applicable to design of MEMS-based hair flow-sensors [19]. Since the cercal system evolved under the physical constraints imposed by the interaction with air at a low Reynolds number, it is likely that its operational characteristics reflect these constraints.…”

“…The total force F IRS,j at the j-th point on the hair is the sum (19) In the text we will occasionally refer to F IRS for an entire hair. F IRS is the concatenation of F IRS,j over all points j of the hair.…”

“…The equations (4), (9), (19), (20), (21), (22), and (23) describe the model cercal system, and are listed below for reference. We rescale these equations (see Appendix A) before solving them.…”

“…Our goal is to explicitly compute θ̈ (j) for j = 1, …, N in the expression of the velocity v on the right hand side of (24). Note that u IRS depends directly on θ̈ (j) since u IRS is a function of , which depends on θ̈ (j) (see (19)). Since u bc and u con depend on v these velocities depend on θ̈ (j) as well.…”

Variability in Sensory Ecology: Expanding the Bridge Between Physiology and Evolutionary Biology

“…Sensory ecology has grown rapidly over the past decade, in part because of rapid technological advances, such as high-speed cameras (Buskey and Hartline 2003;Dangles et al 2007), field portable spectrometers (Cronin and Shashar 2001;Smith et al 2003;Johnsen 2007;Ryan 2007), panoramic image devices (Zeil et al 2003), acoustic flight path tracking (Jones and Holderied 2007), particle image velocimetry (Stacey et al 2002;Casas et al 2008), mathematical simulations (Humphrey et al 1993;Chittka 1996a;Magal et al 2006), and neural networks (Phelps 2007). Sensory ecology has also been incorporated into diverse areas of biology such as conservation biology (Rabin et al 2003;Slabbekoorn and Ripmeester 2008) and biomimetics (Peremans and Reijniers 2005;Krijnen et al 2006). the need to bridge sensory and evolutionary biology Sensory ecology has traditionally focused on the experimental and mechanistic study of the physiology and neuroethology of sensory organs.…”