Quantifying the influence of stimulation protocols on neural network connectivity inference to optimize rapid network measurements.

Connectivity is key to understanding neural circuit computations. However, estimating in vivo connectivity using recording of activity alone is challenging. Issues include common input and bias errors in inference, and limited temporal resolution due to large data requirements. Perturbations (e.g. stimulation) can improve inference accuracy and accelerate estimation. However, optimal stimulation protocols for rapid network estimation are not yet established. Here, we use neural network simulations to identify stimulation protocols that minimize connectivity inference errors when using generalized linear model inference. We find that stimulation parameters that balance excitatory and inhibitory activity minimize inference error. We also show that pairing optimized stimulation with adaptive protocols that choose neurons to stimulate via Bayesian inference may ultimately enable rapid network inference.

From Point to Filament Defects in Hybrid Nematic Films.

We have studied nematic hybrid films with homeotropic alignment at the top surface and various controlled degrees of in plane ordering, going from a random degenerate organization to a completely uniform alignment along one direction, at the bottom one. We show, by Monte Carlo (MC) computer simulations and experiments on photopatterned films with the bottom support surface fabricated with in-plane order similar to the simulated ones, that the point defects observed in the case of random planar orientations at the bottom tend to arrange along a filament as the surface ordering is sufficiently increased. MC simulations complement the polarized microscopy texture observations allowing to inspect the 3D structure of the defects and examine the role of elastic constants.

Topologically Protected Generation of Stable Wall Loops in Nematic Liquid Crystals.

The nematic liquid crystal (LC) director field can contain defects that are both singular and nonsingular, but nonsingular defects with an integer winding number of the director are typically metastable because of their high energy. We demonstrate topology-mediated generation and stabilization of nonsingular wall loops in a sandwich-type LC cell by combining a patterned substrate with a planar substrate. We implement a design which imposes a topological constraint on a singular disclination loop such that it irreversibly annihilates upon application of a field, and it results in the generation of a stable nonsingular wall loop when the field is removed. Theoretical modeling agrees with experimental observations, providing insight into the wall generation mechanism and its stability. The concept to stabilize high-energy structures through orientation-patterning-defined topological constraints extends our ability to control orientationally ordered matter.

Shape control of surface-stabilized disclination loops in nematic liquid crystals.

Recent studies on topological defects in conventional and active nematic liquid crystals have revealed their potential as sources of advanced functionality whereby the collective behavior of the constituent molecules or cells is controlled. On the other hand, the fact that they have high energies and are metastable makes their shape control a nontrivial issue. Here, we demonstrate stabilization of arbitrary-shaped closed disclination loops with 1/2 strength floating in the bulk by designing the twist angle distribution in a liquid crystal cell. Continuous variation of the twist angle from below to above |π/2| allows us to unambiguously position reverse twist disclinations at will. We also analyze the elastic free energy and uncover the relationship between the twist angle pattern and shrink rate of the surface-stabilized disclination loop.