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.

Thermistor-based breathing sensor for circadian rhythm evaluation.

One of the most frequently used methods to sense breathing pattern is to detect airflow using a nasal thermistor or a thermocouple sensor. Prolonged, minimally intrusive measurement of the breathing pattern is particularly important for polysomnography, sleeping disorders, stress monitoring, biofeedback techniques, and circadian rhythm analysis. Although most applications require only breathing pattern, some applications and diagnostic procedures require monitoring of the rhythm of change of dominant nostril. In this paper we present our design of a differential thermistor-based breathing sensor for prolonged monitoring during the normal activity. The system is designed using a low power microcontroller Texas Instruments MSP430F149 with an on-chip A/D converter for data acquisition and signal processing. We use wireless RF link to a PC for long-term data acquisition and storage. Precise measurement requires decreasing zero and sensitivity errors of the measurement. We discuss signal processing methods, calibration and parameters used to characterize breathing patterns necessary for circadian breathing rhythm evaluation.