Biomedical subjects

Nicholas Hatsopoulos

Publications and source records attributed to Nicholas Hatsopoulos.

4 recordsLinked to original sources

PubMed2006

Evidence against a single coordinate system representation in the motor cortex.

Understanding the coordinate systems in which the motor cortical cells encode movement parameters such as direction is a fundamental yet unresolved issue. Although many studies have assumed that motor cortex encodes direction in an extrinsic, Cartesian (CA) coordinate system, other studies have provided evidence for encoding in intermediate coordinate systems such as a shoulder-centered (SC) or in a purely intrinsic, joint-angle-based (JA) coordinate frame. By simultaneously recording from multiple single units in primary motor cortex, we examined movement direction encoding under each of these three coordinate systems. We directly compared the degree of directional tuning invariance over multiple sub-regions in the workspace. We also compared the mutual information between neuronal firing rate and movement direction in the three systems. We observed a broad range of directional invariance in all three coordinate systems with no strong dominance of any single coordinate system. The mutual information analyses corroborated this observation. However, we found a small but significant bias toward the SC coordinate frame, which was also supported by population vector decoding. Similar results were found when we compared hand/torque force direction encoding in all three coordinate systems. These results suggest that the motor cortex employs a coordinate system that is yet to be discovered or perhaps that the motor cortex should not be viewed as a substrate for any coordinate system representation.

Action Potentials↗

PubMed2004

Decoding continuous and discrete motor behaviors using motor and premotor cortical ensembles.

Decoding motor behavior from neuronal signals has important implications for the development of a brain-machine interface (BMI) but also provides insights into the nature of different movement representations within cortical ensembles. Motor control can be hierarchically characterized as the selection and planning of discrete movement classes and/or postures followed by the execution of continuous limb trajectories. Based on simultaneous recordings in primary motor (MI) and dorsal premotor (PMd) cortices in behaving monkeys, we demonstrate that an MI ensemble can reconstruct hand or joint trajectory more accurately than an equally sized PMd ensemble. In contrast, PMd can more precisely predict the future occurrence of one of several discrete targets to be reached. This double dissociation suggests that a general-purpose BMI could take advantage of multiple cortical areas to control a wider variety of motor actions. These results also support the hierarchical view that MI ensembles are involved in lower-level movement execution, whereas PMd populations represent the early intention to move to visually presented targets.

Animals↗

PubMed2003

Robustness of neuroprosthetic decoding algorithms.

We assessed the ability of two algorithms to predict hand kinematics from neural activity as a function of the amount of data used to determine the algorithm parameters. Using chronically implanted intracortical arrays, single- and multineuron discharge was recorded during trained step tracking and slow continuous tracking tasks in macaque monkeys. The effect of increasing the amount of data used to build a neural decoding model on the ability of that model to predict hand kinematics accurately was examined. We evaluated how well a maximum-likelihood model classified discrete reaching directions and how well a linear filter model reconstructed continuous hand positions over time within and across days. For each of these two models we asked two questions: (1) How does classification performance change as the amount of data the model is built upon increases? (2) How does varying the time interval between the data used to build the model and the data used to test the model affect reconstruction? Less than 1 min of data for the discrete task (8 to 13 neurons) and less than 3 min (8 to 18 neurons) for the continuous task were required to build optimal models. Optimal performance was defined by a cost function we derived that reflects both the ability of the model to predict kinematics accurately and the cost of taking more time to build such models. For both the maximum-likelihood classifier and the linear filter model, increasing the duration between the time of building and testing the model within a day did not cause any significant trend of degradation or improvement in performance. Linear filters built on one day and tested on neural data on a subsequent day generated error-measure distributions that were not significantly different from those generated when the linear filters were tested on neural data from the initial day (p<0.05, Kolmogorov-Smirnov test). These data show that only a small amount of data from a limited number of cortical neurons appears to be necessary to construct robust models to predict kinematic parameters for the subsequent hours. Motor-control signals derived from neurons in motor cortex can be reliably acquired for use in neural prosthetic devices. Adequate decoding models can be built rapidly from small numbers of cells and maintained with daily calibration sessions.

Action Potentials↗

PubMedDate not supplied

Multiplication and stimulus invariance in a looming-sensitive neuron.

Multiplicative operations and invariance of neuronal responses are thought to play important roles in the processing of neural information in many sensory systems. Yet the biophysical mechanisms that underlie both multiplication and invariance of neuronal responses in vivo, either at the single cell or at the network level, remain to a large extent unknown. Recent work on an identified neuron in the locust visual system (the LGMD neuron) that responds well to objects looming on a collision course towards the animal suggests that this cell represents a good model to investigate the biophysical basis of multiplication and invariance at the single neuron level. Experimental and theoretical results are consistent with multiplication being implemented by subtraction of two logarithmic terms followed by exponentiation via active membrane conductances, according to a x 1/b = exp(log(a) - log(b)). Invariance appears to be in part due to non-linear integration of synaptic inputs within the dendritic tree of this neuron.

Action Potentials↗