Bayesian model of dynamic image stabilization in the visual system.

Humans can resolve the fine details of visual stimuli although the image projected on the retina is constantly drifting relative to the photoreceptor array. Here we demonstrate that the brain must take this drift into account when performing high acuity visual tasks. Further, we propose a decoding strategy for interpreting the spikes emitted by the retina, which takes into account the ambiguity caused by retinal noise and the unknown trajectory of the projected image on the retina. A main difficulty, addressed in our proposal, is the exponentially large number of possible stimuli, which renders the ideal Bayesian solution to the problem computationally intractable. In contrast, the strategy that we propose suggests a realistic implementation in the visual cortex. The implementation involves two populations of cells, one that tracks the position of the image and another that represents a stabilized estimate of the image itself. Spikes from the retina are dynamically routed to the two populations and are interpreted in a probabilistic manner. We consider the architecture of neural circuitry that could implement this strategy and its performance under measured statistics of human fixational eye motion. A salient prediction is that in high acuity tasks, fixed features within the visual scene are beneficial because they provide information about the drifting position of the image. Therefore, complete elimination of peripheral features in the visual scene should degrade performance on high acuity tasks involving very small stimuli.

How the brain generates movement.

In this study, we assume that the brain uses a general-purpose pattern generator to transform static commands into basic movement segments. We hypothesize that this pattern generator includes an oscillator whose complete cycle generates a single movement segment. In order to demonstrate this hypothesis, we construct an oscillator-based model of movement generation. The model includes an oscillator that generates harmonic outputs whose frequency and amplitudes can be modulated by external inputs. The harmonic outputs drive a number of integrators, each activating a single muscle. The model generates muscle activation patterns composed of rectilinear and harmonic terms. We show that rectilinear and fundamental harmonic terms account for known properties of natural movements, such as the invariant bell-shaped hand velocity profile during reaching. We implement these dynamics by a neural network model and characterize the tuning properties of the neural integrator cells, the neural oscillator cells, and the inputs to the system. Finally, we propose a method to test our hypothesis that a neural oscillator is a central component in the generation of voluntary movement.

Motor learning with unstable neural representations.

It is often assumed that learning takes place by changing an otherwise stable neural representation. To test this assumption, we studied changes in the directional tuning of primate motor cortical neurons during reaching movements performed in familiar and novel environments. During the familiar task, tuning curves exhibited slow random drift. During learning of the novel task, random drift was accompanied by systematic shifts of tuning curves. Our analysis suggests that motor learning is based on a surprisingly unstable neural representation. To explain these results, we propose that motor cortex is a redundant neural network, i.e., any single behavior can be realized by multiple configurations of synaptic strengths. We further hypothesize that synaptic modifications underlying learning contain a random component, which causes wandering among synaptic configurations with equivalent behaviors but different neural representations. We use a simple model to explore the implications of these assumptions.

Patterns of ongoing activity and the functional architecture of the primary visual cortex.

Ongoing spontaneous activity in the cerebral cortex exhibits complex spatiotemporal patterns in the absence of sensory stimuli. To elucidate the nature of this ongoing activity, we present a theoretical treatment of two contrasting scenarios of cortical dynamics: (1) fluctuations about a single background state and (2) wandering among multiple "attractor" states, which encode a single or several stimulus features. Studying simplified network rate models of the primary visual cortex (V1), we show that the single state scenario is characterized by fast and high-dimensional Gaussian-like fluctuations, whereas in the multiple state scenario the fluctuations are slow, low dimensional, and highly non-Gaussian. Studying a more realistic model that incorporates correlations in the feed-forward input, spatially restricted cortical interactions, and an experimentally derived layout of pinwheels, we show that recent optical-imaging data of ongoing activity in V1 are consistent with the presence of either a single background state or multiple attractor states encoding many features.

Cortical representation of bimanual movements.

It is well established that the discharge of neurons in primate motor cortex is tuned to the movement direction of the contralateral arm. Interestingly, it has been found that these neurons exhibit a directional tuning to the ipsilateral arm as well and that the preferred directions to both arms tend to be similar. A recent study showed that motor cortex cells are also directionally selective to bimanual movements, but the relationship between the bimanual and unimanual representations remains unclear. To address this issue, we analyzed the responses of motor cortical neurons recorded from two macaque monkeys during unimanual and bimanual reaching movements. We decomposed the bimanual movement representation into contralateral and ipsilateral directionally tuned components. Our major finding is that the movement of the contralateral arm modifies the tuning of the cells to the ipsilateral arm such that: (1) the offset and modulation depth of the tuning are suppressed; and (2) the preferred directions are randomly shifted. Both these effects eliminate the correlation between the contralateral and ipsilateral representations during bimanual movements. We suggest that the modification of the ipsilateral arm representation is caused by the recruitment of local inhibition that conveys callosal inputs during bimanual movements. This hypothesis is supported by the analysis of a model of two motor cortical networks, coupled with sparse random interhemispheric projections that reproduce the main features observed in the data. Finally, we show that the modification of the ipsilateral arm representation reduces the interference between the movements of both arms.

Spike synchronization in the cortex/basal-ganglia networks of Parkinsonian primates reflects global dynamics of the local field potentials.

Cortical local field potentials (LFPs) reflect synaptic potentials and accordingly correlate with neuronal discharge. Because LFPs are coherent across substantial cortical areas, we hypothesized that cortical spike correlations could be predicted from them. Because LFPs recorded in the basal ganglia (BG) are also correlated with neuronal discharge and are clinically accessible in Parkinson's disease patients, we were interested in testing this hypothesis in the BG, as well. We recorded LFPs and unit discharge from multiple electrodes, which were placed in primary motor cortex or in the basal ganglia (striatum and pallidum) of two monkeys before and after rendering them parkinsonian with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine. We used the method of partial spectra to construct LFP predictors of the spike cross-correlation functions (CCFs). The predicted CCF is an estimate of the correlation between two neurons under the assumption that their association is explained solely by the association of each with the LFP recorded on a third electrode. In the normal condition, the predictors account for cortical rate covariations but not for the association among the tonically active neurons of the striatum. In the parkinsonian condition, with the appearance of 10 Hz oscillations throughout the cortex-basal ganglia networks, the LFP predictors account remarkably better for the CCFs in both the cortex and the basal ganglia. We propose that, in the parkinsonian condition, the cortex-basal ganglia networks are more tightly related to global modes of brain dynamics that are echoed in the LFP.