Lars Holmstrom

Efficient Encoding of Vocalizations in the Auditory Midbrain

An important question in sensory neuroscience is what coding strategies and mechanisms are used by the brain to detect and discriminate among behaviorally relevant stimuli. There is evidence that sensory systems migrate from a distributed and redundant encoding strategy at the periphery to a more heterogeneous encoding in cortical structures. It has been hypothesized that heterogeneity is an efficient encoding strategy that minimizes the redundancy of the neural code and maximizes information throughput. Evidence of this mechanism has been documented in cortical structures. In this study, we examined whether heterogeneous encoding of complex sounds contributes to efficient encoding in the auditory midbrain by characterizing neural responses to behaviorally relevant vocalizations in the mouse inferior colliculus (IC). We independently manipulated the frequency, amplitude, duration, and harmonic structure of the vocalizations to create a suite of modified vocalizations. Based on measures of both spike rate and timing, we characterized the heterogeneity of neural responses to the natural vocalizations and their perturbed variants. Using information theoretic measures, we found that heterogeneous response properties of IC neurons contribute to efficient encoding of behaviorally relevant vocalizations.

Mobility Lab to Assess Balance and Gait with Synchronized Body-worn Sensors.

This paper is a commentary to introduce how rehabilitation professionals can use a new, body-worn sensor system to obtain objective measures of balance and gait. Current assessments of balance and gait in clinical rehabilitation are largely limited to subjective scales, simple stop-watch measures, or complex, expensive machines not practical or largely available. Although accelerometers and gyroscopes have been shown to accurately quantify many aspects of gait and balance kinematics, only recently a comprehensive, portable system has become available for clinicians. By measuring body motion during tests that clinicians are already performing, such as the Timed Up and Go test (TUG) and the Clinical Test of Sensory Integration for Balance (CITSIB), the additional time for assessment is minimal. By providing instant analysis of balance and gait and comparing a patients performance to age-matched control values, therapists receive an objective, sensitive screening profile of balance and gait strategies. This motion screening profile can be used to identify mild abnormalities not obvious with traditional clinical testing, measure small changes due to rehabilitation, and design customized rehabilitation programs for each individuals specific balance and gait deficits.

Upper limb joint angle tracking with inertial sensors

Wearable inertial systems have recently been used to track human movement in and outside of the laboratory. Continuous monitoring of human movement can provide valuable information relevant to individuals level of physical activity and functional ability. Traditionally, orientation has been calculated by integrating the angular velocity from gyroscopes. However, a small drift in the measured velocity leads to large integration errors that grow with time. To compensate for that drift, complementary data from accelerometers are normally fused into the tracking systems using the Kalman or extended Kalman filter (EKF). In this study, we combine kinematic models designed for control of robotic arms with the unscented Kalman filter (UKF) to continuously estimate the angles of human shoulder and elbow using two wearable sensors. This methodology can easily be generalized to track other human joints. We validate the method with an optical motion tracking system and demonstrate correlation consistently greater than 0.9 between the two systems.

Validity and reliability of an IMU-based method to detect APAs prior to gait initiation

Anticipatory postural adjustments (APAs) prior to gait initiation have been largely studied in traditional, laboratory settings using force plates under the feet to characterize the displacement of the center of pressure. However clinical trials and clinical practice would benefit from a portable, inexpensive method for characterizing APAs. Therefore, the main objectives of this study were (1) to develop a novel, automatic IMU-based method to detect and characterize APAs during gait initiation and (2) to measure its test-retest reliability. Experiment I was carried out in the laboratory to determine the validity of the IMU-based method in 10 subjects with PD (OFF medication) and 12 control subjects. Experiment II was carried out in the clinic, to determine test-retest reliability of the IMU-based method in a different set of 17 early-to-moderate, treated subjects with PD (tested ON medication) and 17 age-matched control subjects. Results showed that gait initiation characteristics (both APAs and 1st step) detected with our novel method were significantly correlated to the characteristics calculated with a force plate and motion analysis system. The size of APAs measured with either inertial sensors or force plate was significantly smaller in subjects with PD than in control subjects (p<0.05). Test-retest reliability for the gait initiation characteristics measured with inertial sensors was moderate-to-excellent (0.56<ICC<0.82) for both groups. Our findings support the feasibility of automatically characterizing postural preparation and gait initiation with body-worn inertial sensors that would be practical for unsupervised clinical and home settings.

Multiharmonic tracking using marginalized particle filters

Man-made and natural systems often generate signals with multi-harmonic components, and the accurate estimation of the harmonically related components of these signals is critical for various applications. The posterior distribution of frequency estimates for this class of signal is multi-model - posing a challenge for frequency tracking algorithms which may lock onto a super or sub harmonic of the fundamental frequency. We propose a multi-harmonic tracker based on a sequential Monte Carlo method (SMCM) which can account for the multi-modality of the posterior distribution to track the harmonically related components of a signal more accurately than a tracker based on local linearization. We compare the SMCM multi-harmonic tracker with the extended Kalman filter (EKF) multi-harmonic tracker by applying them to real biomedical signals including electrocardiograms (ECG) and arterial blood pressure (ABP) signals. The results clearly show the superior performance of the proposed multi-harmonic tracker over the EKF tracker.

Stimulus design for auditory neuroethology using state space modeling and the extended Kalman smoother.

A new method for designing vocalization based stimuli for experiments in auditory neurophysiology is described. This analysis-synthesis technique leverages a state space statistical signal model and the extended Kalman smoother for tracking the frequency, amplitude, and phase information of harmonically related components in recorded vocalizations. Using the same state space model, these parameters can then be used to synthesize the vocalizations and random or deterministic variants of the vocalizations. This method is shown to outperform short-time Fourier transform based frequency tracking methods in both noisy and noise-free synthetic test signals. It is further shown to accurately track recorded hummingbird, human, and bat vocalizations while removing recording artifacts such as noise, echo, and digital aliasing in the synthesis phase.

Experience Based Surface Discernment by a Quadruped Robot

The task of autonomous surface discernment by an AIBO robotic dog is addressed. Different surface textures (plywood board, thin foam, short carpet, shag carpet) as well as different inclines (0 and 10 degrees) are considered. Using a genetic algorithm, gaits are designed which allow the robot to traverse each of these surfaces in an (approximately) optimal fashion. Frequency domain analysis of actuator readings from individual leg joints is performed for data collected using each gait on each surface type. It is found that the spectral content of these signals is significantly dependent on the characteristics of both the gait in use and the surface being walked upon. Using tap-delay Adaline neural networks to integrate actuator readings from 15 independent joints into a set of models of different gait/surface experiences, an algorithm is designed which uses these experiences to yield high classification rates across surface transitions and with low latency

Alleviating freezing of gait using phase-dependent tactile biofeedback

In this feasibility study, we present a novel, wearable prototype of tactile biofeedback to alleviate gait disturbances, such as freezing of gait in Parkinsons disease. We designed and tested a phase-dependent tactile biofeedback system that can be easily worn on the feet, with a simple switch to turn it on or off. Preliminary validation was performed in 8 subjects with Parkinsons disease who show freezing during a turning in place test. A metronome, control condition was used to compare effectiveness in alleviating freezing. Promising results were obtained, both in term of acceptability of the device, and improving motor performance.