Remembering Forward: Neural Correlates of Memory and Prediction in Human Motor Adaptation

We used functional MR imaging (FMRI), a robotic manipulandum and systems identification techniques to examine neural correlates of predictive compensation for spring-like loads during goal-directed wrist movements in neurologically-intact humans. Although load changed unpredictably from one trial to the next, subjects nevertheless used sensorimotor memories from recent movements to predict and compensate upcoming loads. Prediction enabled subjects to adapt performance so that the task was accomplished with minimum effort. Population analyses of functional images revealed a distributed, bilateral network of cortical and subcortical activity supporting predictive load compensation during visual target capture. Cortical regions – including prefrontal, parietal and hippocampal cortices – exhibited trial-by-trial fluctuations in BOLD signal consistent with the storage and recall of sensorimotor memories or “states” important for spatial working memory. Bilateral activations in associative regions of the striatum demonstrated temporal correlation with the magnitude of kinematic performance error (a signal that could drive reward-optimizing reinforcement learning and the prospective scaling of previously learned motor programs). BOLD signal correlations with load prediction were observed in the cerebellar cortex and red nuclei (consistent with the idea that these structures generate adaptive fusimotor signals facilitating cancelation of expected proprioceptive feedback, as required for conditional feedback adjustments to ongoing motor commands and feedback error learning). Analysis of single subject images revealed that predictive activity was at least as likely to be observed in more than one of these neural systems as in just one. We conclude therefore that motor adaptation is mediated by predictive compensations supported by multiple, distributed, cortical and subcortical structures

How Laminar Frontal Cortex and Basal Ganglia Circuits Interact to Control Planned and Reactive Saccades

The basal ganglia and frontal cortex together allow animals to learn adaptive responses that acquire rewards when prepotent reflexive responses are insufficient. Anatomical studies show a rich pattern of interactions between the basal ganglia and distinct frontal cortical layers. Analysis of the laminar circuitry of the frontal cortex, together with its interactions with the basal ganglia, motor thalamus, superior colliculus, and inferotemporal and parietal cortices, provides new insight into how these brain regions interact to learn and perform complexly conditioned behaviors. A neural model whose cortical component represents the frontal eye fields captures these interacting circuits. Simulations of the neural model illustrate how it provides a functional explanation of the dynamics of 17 physiologically identified cell types found in these areas. The model predicts how action planning or priming (in cortical layers III and VI) is dissociated from execution (in layer V), how a cue may serve either as a movement target or as a discriminative cue to move elsewhere, and how the basal ganglia help choose among competing actions. The model simulates neurophysiological, anatomical, and behavioral data about how monkeys perform saccadic eye movement tasks, including fixation; single saccade, overlap, gap, and memory-guided saccades; anti-saccades; and parallel search among distractors.Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-l-0409, N00014-92-J-1309, N00014-95-1-0657); National Science Foundation (IRI-97-20333)

Learning the meaning of new stimuli increases the cross-correlated activity of prefrontal neurons

The prefrontal cortex (PF) has a key role in learning rules and generating associations between stimuli and responses also called conditional motor learning. Previous studies in PF have examined conditional motor learning at the single cell level but not the correlation of discharges between neurons at the ensemble level. In the present study, we recorded from two rhesus monkeys in the dorsolateral and the mediolateral parts of the prefrontal cortex to address the role of correlated firing of simultaneously recorded pairs during conditional motor learning. We trained two rhesus monkeys to associate three stimuli with three response targets, such that each stimulus was mapped to only one response. We recorded the neuronal activity of the same neuron pairs during learning of new associations and with already learned associations. In these tasks after a period of fixation, a visual instruction stimulus appeared centrally and three potential response targets appeared in three positions: right, left, and up from center. We found a higher number of neuron pairs significantly correlated and higher cross-correlation coefficients during stimulus presentation in the new than in the familiar mapping task. These results demonstrate that learning affects the PF neural correlation structure

Cortical Models for Movement Control

Defense Advanced Research Projects Agency and Office of Naval Research (N0014-95-l-0409)

Economic choices can be made using only stimulus values

Decision-making often involves choices between different stimuli, each of which is associated with a different physical action. A growing consensus suggests that the brain makes such decisions by assigning a value to each available option and then comparing them to make a choice. An open question in decision neuroscience is whether the brain computes these choices by comparing the values of stimuli directly in goods space or instead by first assigning values to the associated actions and then making a choice over actions. We used a functional MRI paradigm in which human subjects made choices between different stimuli with and without knowledge of the actions required to obtain the different stimuli. We found neural correlates of the value of the chosen stimulus (a postdecision signal) in ventromedial prefrontal cortex before the actual stimulus–action pairing was revealed. These findings provide support for the hypothesis that the brain is capable of making choices in the space of goods without first transferring values into action space

Temporal context and conditional associative learning

<p>Abstract</p> <p>Background</p> <p>We investigated how temporal context affects the learning of arbitrary visuo-motor associations. Human observers viewed highly distinguishable, fractal objects and learned to choose for each object the one motor response (of four) that was rewarded. Some objects were consistently preceded by specific other objects, while other objects lacked this task-irrelevant but predictive context.</p> <p>Results</p> <p>The results of five experiments showed that predictive context consistently and significantly accelerated associative learning. A simple model of reinforcement learning, in which three successive objects informed response selection, reproduced our behavioral results.</p> <p>Conclusions</p> <p>Our results imply that not just the representation of a current event, but also the representations of past events, are reinforced during conditional associative learning. In addition, these findings are broadly consistent with the prediction of attractor network models of associative learning and their prophecy of a persistent representation of past objects.</p

Visual motion processing and human tracking behavior

The accurate visual tracking of a moving object is a human fundamental skill that allows to reduce the relative slip and instability of the object's image on the retina, thus granting a stable, high-quality vision. In order to optimize tracking performance across time, a quick estimate of the object's global motion properties needs to be fed to the oculomotor system and dynamically updated. Concurrently, performance can be greatly improved in terms of latency and accuracy by taking into account predictive cues, especially under variable conditions of visibility and in presence of ambiguous retinal information. Here, we review several recent studies focusing on the integration of retinal and extra-retinal information for the control of human smooth pursuit.By dynamically probing the tracking performance with well established paradigms in the visual perception and oculomotor literature we provide the basis to test theoretical hypotheses within the framework of dynamic probabilistic inference. We will in particular present the applications of these results in light of state-of-the-art computer vision algorithms