Search PubMed⌕ Search

Biomedical subjects

Lee E Miller

Publications and source records attributed to Lee E Miller.

5 recordsLinked to original sources

Deciding when and how to correct a movement: discrete submovements as a decision making process.

Rapid reaching movements of human and non-human primates are often characterized by irregular multi-peaked velocity profiles. How to interpret these irregularities is still under debate. While some reports assert that these irregularities are the result of a continuous controller interacting with the environment, we and others hold that the velocity irregularities are evidence for a controller that produces discrete movement corrections. Here we analyze rapid pronation/supination wrist movements in monkey during a 1D step-tracking task, where visual perturbations of the target were randomly introduced at movement onset. We use our recently introduced algorithm (Fishbach et al. in Exp Brain Res 164:442-457, 2005) to decompose an irregular movement into a primary movement and one or more discrete, corrective submovements. We first show that the visual perturbation has almost no effect on primary movements. In contrast, this perturbation influences the type and the extent of the corrective submovements that often follow primary movements. Secondly, we show that the highly variable timing of overlapping submovements does not depend directly on the visual perturbation but rather on an estimate of the movement error and on the movement's extent-to-go at the time of correction initiation. These results are consistent with a forward-model based intermittent controller with a non-linearity that depends both on a prediction of the magnitude and direction of the movement's error and on its variance. Corrections are initiated only when the predicted error is statistically significant. A simple abstract model that implements these principles accounts for the type and timing of the corrections observed in our data.

Algorithms↗

Identification of multiple-input systems with highly coupled inputs: application to EMG prediction from multiple intracortical electrodes.

A robust identification algorithm has been developed for linear, time-invariant, multiple-input single-output systems, with an emphasis on how this algorithm can be used to estimate the dynamic relationship between a set of neural recordings and related physiological signals. The identification algorithm provides a decomposition of the system output such that each component is uniquely attributable to a specific input signal, and then reduces the complexity of the estimation problem by discarding those input signals that are deemed to be insignificant. Numerical difficulties due to limited input bandwidth and correlations among the inputs are addressed using a robust estimation technique based on singular value decomposition. The algorithm has been evaluated on both simulated and experimental data. The latter involved estimating the relationship between up to 40 simultaneously recorded motor cortical signals and peripheral electromyograms (EMGs) from four upper limb muscles in a freely moving primate. The algorithm performed well in both cases: it provided reliable estimates of the system output and significantly reduced the number of inputs needed for output prediction. For example, although physiological recordings from up to 40 different neuronal signals were available, the input selection algorithm reduced this to 10 neuronal signals that made significant contributions to the recorded EMGs.

Algorithms↗

Kinematic properties of on-line error corrections in the monkey.

Despite the abundant experimental evidence for the irregular, multipeaked velocity profiles that often characterize rapid human limb movements, there is currently little agreement on how to interpret these phenomena. While in some studies these irregularities have been interpreted as reflecting a continuous control process, in others the irregularities are considered to be evidence for the existence of discrete movement primitives that are initiated by an intermittent controller. Here we introduce a novel "soft symmetry" method for analyzing irregular movements and decomposing them into their discrete movement primitives. We applied this method to analyze rapid pronation/supination wrist movements in monkeys during a one-dimensional tracking task. We showed that the properties of the extracted overlapping submovements (OSMs) were very similar to those of single, regular movements, despite the fact that the decomposition algorithm did not restrict the extracted submovements to a particular shape. In addition we showed that the movement primitives corrected preceding primitives and that the correction initiation time was highly variable, and thus could not be explained by the relatively fixed sensorimotor delay. These results argue against the interpretation of movement irregularities as reflecting a continuous control process and reinforce the hypothesis that movement irregularities result from an intermittent control mechanism. Demonstrating these phenomena in non-human primates will allow neurophysiological investigation of the neural mechanisms involved in these corrections.

Algorithms↗

Limb movement: getting a handle on grasp.

The mechanical complexity of the hand is indisputable, but there is increasing evidence that its control is simplified in many tasks around synergic groups of muscles that effectively decrease the number of controlled degrees of freedom.

Animals↗

Brain-machine interfaces: computational demands and clinical needs meet basic neuroscience.

As long as 150 years ago, when Fritz and Hitzig demonstrated the electrical excitability of the motor cortex, scientists and fiction writers were considering the possibility of interfacing a machine with the human brain. Modern attempts have been driven by concrete technological and clinical goals. The most advanced of these has brought the perception of sound to thousands of deaf individuals by means of electrodes implanted in the cochlea. Similar attempts are underway to provide images to the visual cortex and to allow the brains of paralyzed patients to re-establish control of the external environment via recording electrodes. This review focuses on two challenges: (1) establishing a 'closed loop' between sensory input and motor output and (2) controlling neural plasticity to achieve the desired behavior of the brain-machine system. Meeting these challenges is the key to extending the impact of the brain-machine interface.

Animals↗