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Nerve growth cones (GCs) are chemical sensors that convert extracellular cues into oriented motion. Although families of guidance signals have been uncovered, the mechanisms by which GCs quantitatively process directional information are still poorly known, largely due to the limitations of standard guidance assays. Here, we probe the response of dissociated neurons to controlled gradients using novel shear-free microfluidic devices. By measuring and quantitatively modeling the polarization of GABAA chemoreceptors at the GC membrane, we analyze the amplification and filtering properties of nerve GCs during GABA directional sensing. We find that: (i) GCs are able to non-adaptively amplify extracellular gradients, with a dependence on the ligand concentration determined by the saturable response of chemoreceptors, (ii) GCs act as low-pass temporal filters with a cut-off frequency independent of stimuli conditions. These experiments pave the way for an integrative approach of the GC response to complex spatiotemporal stimuli patterns, from a molecular to a systems-level.

Animals’ ability to demonstrate both stereotyped and adaptive locomotor behavior is largely dependent on the interplay between centrally-generated motor patterns and the sensory inputs that shape them. Theoretical predictions suggest that the degree to which sensory feedback is used for coordinating movement depends on the specific properties of the movement and the environment; i.e when animals navigate slowly through a complex environment where great precision is required, motor activity is expected to be mostly modulated by neural reflexes and sensory information. In contrast, during fast running or under noisy conditions, the relatively slow neural processing makes feedback-based coordination unlikely. The research project I would like to present is our attempt to study the relative importance of central coupling of pattern generating networks vs. intersegmental afferents for locomotion in the cockroach, an animal that is renowned for rapid and stable running. In order to do so, we combine neurophysiological experiments with simulations of stochastic models of coupled oscillators. Specifically, we record activity patterns from leg motor neurons in semi-intact preparations whose legs movement is controlled. The recorded traces are then compared with model generated activity to estimate underlying physiological parameters using a maximum likelihood technique. Our findings suggest segmental hierarchies, speed-dependent control and provide insights into how sensory information from a moving leg dynamically modulates centrally generated patterns. I will discuss these and suggest movement-based feedback in cockroach locomotion as a model system to study the bidirectional interactions between motor control and sensory processing in general.

The simple equation which describes a particle diffusing in a logarithmic potential arises in diverse physical problems such as condensation processes, denaturation of DNA molecules, and momentum diffusion of atoms in optical traps. A study of the approach of such systems to equilibrium via a scaling analysis reveals three surprising features: (i) the solution is given by two distinct scaling forms, corresponding to a diffusive and a subdiffusive length scale, respectively, (ii) the overall scaling function is selected by the initial condition, and (iii) depending on the tail of the initial condition, the scaling exponent which characterizes the scaling function is found to exhibit a transition from a continuously varying to a fixed value. I will present the general scaling solution and discuss its practical and theoretical applications.

Robotic therapy affords substantial, durable benefits for upper-extremity
rehabilitation following neurological injury. That success is based on insight
from motor neuroscience, including theories of control based on internal
models. However, the form of any internal model remains unclear. Typical
assumptions borrow from engineering mechanics, implicitly assuming a Riemannian
metric. I will review experiments showing that no Riemannian metric can account
for sensorimotor behavior.

Motor tasks are usually redundant: many control actions (e.g. joint motions)
yield equivalent task performance (e.g. hand motion). One appealing theory is
that motor redundancy is exploited using synergies to simplify control and
robustify performance. Unfortunately, most evidence presented to support this
hypothesis is based on analysis of a covariance matrix. I will show that this
is sensitive to the coordinate frame and metric structure assumed in the
analysis. Other methods (e.g. Independent Component Analysis) rely on
higher-order statistics but I will show that with a nonlinear change of
variables the coordinate sensitivity reappears.

An alternative approach identifies two sets of variables, one describing how a
task is executed, the other describing the corresponding result. Perfect task
performance defines a solution manifold at the extremum of the result function
relating the two variable sets. It defines a task-dependent synergy which is
completely independent of any assumptions about the coordinates of execution
space. I will discuss coordinate sensitivity of methods to identify this
task-dependent synergy.