Preferred Directions Of Neurons Research Articles

Interplay between external inputs and recurrent dynamics during movement preparation and execution in a network model of motor cortex.

The primary motor cortex has been shown to coordinate movement preparation and execution through computations in approximately orthogonal subspaces. The underlying network mechanisms, and the roles played by external and recurrent connectivity, are central open questions that need to be answered to understand the neural substrates of motor control. We develop a recurrent neural network model that recapitulates the temporal evolution of neuronal activity recorded from the primary motor cortex of a macaque monkey during an instructed delayed-reach task. In particular, it reproduces the observed dynamic patterns of covariation between neural activity and the direction of motion. We explore the hypothesis that the observed dynamics emerges from a synaptic connectivity structure that depends on the preferred directions of neurons in both preparatory and movement-related epochs, and we constrain the strength of both synaptic connectivity and external input parameters from data. While the model can reproduce neural activity for multiple combinations of the feedforward and recurrent connections, the solution that requires minimum external inputs is one where the observed patterns of covariance are shaped by external inputs during movement preparation, while they are dominated by strong direction-specific recurrent connectivity during movement execution. Our model also demonstrates that the way in which single-neuron tuning properties change over time can explain the level of orthogonality of preparatory and movement-related subspaces.

Accuracy in Estimating Motor Commands Using Neuronal Population Coding

We consider the problem of estimating motor commands from an ensemble of neuronal activities. The population vector algorithm proposed by Georgopoulos provides largely biased estimations when preferred directions of neurons are non-uniformly distributed. To improve this, various decoding methods have been proposed. However, dependence of decoding accuracy on the motor command and other features of neural activities, such as baseline firing rates or amplitudes of tuning curves, have not been quantitatively discussed. In this study, we propose a new method to estimate the motor command in the maximum likelihood estimation framework, which is analytically tractable. We find that the estimation accuracy is independent of the motor command. Using our estimation method, we can estimate the motor command with equal accuracy in all directions.

Context-Dependent Changes in Functional Circuitry in Visual Area MT

Animals can flexibly change their behavior in response to a particular sensory stimulus; the mapping between sensory and motor representations in the brain must therefore be flexible as well. Changes in the correlated firing of pairs of neurons may provide a metric of changes in functional circuitry during behavior. We studied dynamic changes in functional circuitry by analyzing the noise correlations of simultaneously recorded MT neurons in two behavioral contexts: one that promotes cooperative interactions between the two neurons and another that promotes competitive interactions. We found that identical visual stimuli give rise to differences in noise correlation in the two contexts, suggesting that MT neurons receive inputs of central origin whose strength changes with the task structure. The data are consistent with a mixed feature-based attentional strategy model in which the animal sometimes alternates attention between opposite directions of motion and sometimes attends to the two directions simultaneously.

Are the preferred directions of neurons in cat extrastriate cortex related to optic flow?

It has been proposed that one area of extrastriate cortex in the cat, the lateral suprasylvian area (LS), plays an important role in visual analysis during locomotion (Rauschecker et al., 1987). Cells in LS reportedly tend to prefer directions along a trajectory originating at the center of gaze, and passing outward through the receptive-field center. Such directions coincide with the directions of image motion in an optic flow field, the pattern seen by locomoting observers when they fixate the point towards which they are heading (Gibson, 1950). We re-examined this issue for cells in LS with receptive fields in the lower visual field. Cells recorded posterior to Horsley-Clarke A2 showed a clear correlation between preferred direction and receptive-field location, but not that predicted: preferred directions were generally orthogonal to "optic flow" directions. Since these cells were all located posterior to those in studies showing a bias for "optic flow" directions, we hypothesized that there are two cell populations within LS, an anterior population that tends to prefer radial-outward directions, and a posterior population that tends to prefer directions orthogonal to radial. Data from earlier mapping experiments (Sherk & Mulligan, 1993) supported this idea.

Microstimulation in visual area MT: effects of varying pulse amplitude and frequency

We have previously shown that perceptual judgements of motion direction are based in part on the activity of direction selective neurons in extrastriate visual area MT (Salzman et al., 1990, 1992). In those experiments, we applied low-amplitude microstimulation pulses (10 microA, 200 Hz) to clusters of MT neurons whose preferred directions were similar. The effect of microstimulation was to bias the monkeys' choices on a direction discrimination task toward the preferred direction of neurons at the stimulation site. The results suggest that microstimulation generated a directionally specific cortical signal by activating selectively neurons near the electrode tip. To test this notion more directly, we have now examined the behavioral effects of varying current amplitude, current frequency, and electrode position. In the majority of experiments, the directional bias in the monkeys' choices was reduced or eliminated as current amplitude increased to 80 microA. In addition, 80 microA stimulating pulses frequently impaired overall performance as measured by the percentage of correct responses. This decrement in performance indicated that 80 microA pulses introduced "noise" into the neural circuitry encoding motion direction, presumably by increasing current spread to activate a larger population of neurons representing all directions of motion. In contrast, increasing current frequency to 500 Hz (10 microA pulses) preserved the directional specificity of microstimulation effects. The precise position of the stimulating electrode also influenced the magnitude of microstimulation effects; in some cases, differences in position on the order of 100 microns determined whether an experiment yielded a very large effect or no effect at all. Thus, directionally specific activation of cortical circuitry within MT can be disrupted by increases in current spread or by small changes in electrode position. These observations suggest that the effects of low-amplitude microstimulation depend upon direct activation of a well-localized population of neurons.