Neuronal computations are matched to optimally encode the sensory information that is available and relevant for the animal. However, the physical distribution of sensory information is often shaped by the animal’s own behavior. One prominent example is the encoding of optic flow fields that are generated during self-motion of the animal and will therefore depend on the type of locomotion. How evolution has matched computational resources to the behavioral constraints of an animal is not known. Here we use in vivo two photon imaging to record from a population of >3.500 local-direction selective cells. Our data show that the local direction-selective T4/T5 neurons in Drosophila form a population code that is matched to represent optic flow fields generated during translational and rotational self-motion of the fly. This coding principle for optic flow is reminiscent to the population code of local direction-selective ganglion cells in the mouse retina, where four direction-selective ganglion cells encode four different axes of self-motion encountered during walking (Sabbah et al., 2017). However, in flies we find six different subtypes of T4 and T5 cells that, at the population level, represent six axes of self-motion of the fly. The four uniformly tuned T4/T5 subtypes described previously represent a local snapshot (Maisak et al. 2013). The encoding of six types of optic flow in the fly as compared to four types of optic flow in mice might be matched to the high degrees of freedom encountered during flight. Thus, a population code for optic flow appears to be a general coding principle of visual systems, resulting from convergent evolution, but matching the individual ethological constraints of the animal.

The motion of passive or active agents in soft materials generates long ranged deformation fields with signatures informed by hydrodynamics and the properties of the soft matter host. These signatures are even more complex when the soft matter host itself is an active material. Measurement of these fields reveals mechanics of the soft materials and hydrodynamics central to understanding self-organization. In this talk, I first introduce a new method based on correlated displacement velocimetry, and use the method to measure flow fields around particles trapped at the interface between immiscible fluids. These flow fields, decomposed into interfacial hydrodynamic multipoles, including force monopole and dipole flows, provide key insights essential to understanding the interface’s mechanical response. I then extend this method to various actomyosin systems to measure local strain fields around myosin molecular motors. I show how active stresses propagate in 2d liquid crystalline structures and in disordered networks that are formed by the actin filaments. In particular, the response functions of contractile and stable gels are characterized. Through similar analysis, I also measure the retrograde flow fields of stress fibers in single cells to understand subcellular mechanochemical systems.

I shall present two recent piece of work investigating how shape effects the transport of active particles in shear. Firstly we will consider the sedimentation of particles in 2D laminar flow fields of increasing complexity; and how insights from this can help explain why turbulence can enhance the sedimentation of negatively buoyant diatoms [1]. Secondly, we will consider the 3D transport of elongated active particles under the action of an aligning force (e.g. gyrotactic swimmers) in some simple flow fields; and will see how shape can influence the vertical distribution, for example changing the structure of thin layers [2]. [1] Enhanced sedimentation of elongated plankton in simple flows (2018). IMA Journal of Applied Mathematics W Clifton, RN Bearon, & MA Bees. [2] Elongation enhances migration through hydrodynamic shear (in Prep), RN Bearon & WM Durham.