Exploration biases how forelimb reaches to a spatial target are learned.

The brain can learn to generate actions, such as reaching to a target, using different movement strategies. Understanding how different variables bias which strategies are learned to produce such a reach is important for our understanding of the neural bases of movement. Here we introduce a novel spatial forelimb target task in which perched head-fixed mice learn to reach to a circular target area from a set start position using a joystick. These reaches can be achieved by learning to move into a specific direction or to a specific endpoint location. We find that mice gradually learn to successfully reach the covert target. With time, they refine their initially exploratory complex joystick trajectories into controlled targeted reaches. The execution of these controlled reaches depends on the sensorimotor cortex. Using a probe test with shifting start positions, we show that individual mice learned to use strategies biased to either direction or endpoint-based movements. The degree of endpoint learning bias was correlated with the spatial directional variability with which the workspace was explored early in training. Furthermore, we demonstrate that reinforcement learning model agents exhibit a similar correlation between directional variability during training and learned strategy. These results provide evidence that individual exploratory behavior during training biases the control strategies that mice use to perform forelimb covert target reaches.

Automated circuit fabrication and direct characterization of carbon nanotube vibrations.

Since their discovery, carbon nanotubes have fascinated many researchers due to their unprecedented properties. However, a major drawback in utilizing carbon nanotubes for practical applications is the difficulty in positioning or growing them at specific locations. Here we present a simple, rapid, non-invasive and scalable technique that enables optical imaging of carbon nanotubes. The carbon nanotube scaffold serves as a seed for nucleation and growth of small size, optically visible nanocrystals. After imaging the molecules can be removed completely, leaving the surface intact, and thus the carbon nanotube electrical and mechanical properties are preserved. The successful and robust optical imaging allowed us to develop a dedicated image processing algorithm through which we are able to demonstrate a fully automated circuit design resulting in field effect transistors and inverters. Moreover, we demonstrate that this imaging method allows not only to locate carbon nanotubes but also, as in the case of suspended ones, to study their dynamic mechanical motion.