The neuronal basis of motion perception

The marmoset monkey as a model for visual neuroscience

The common marmoset (Callithrix jacchus) has been valuable as a primate model in biomedical research. Interest in this species has grown recently, in part due to the successful demonstration of transgenic marmosets. Here we examine the prospects of the marmoset model for visual neuroscience research, adopting a comparative framework to place the marmoset within a broader evolutionary context. The marmoset's small brain bears most of the organizational features of other primates, and its smooth surface offers practical advantages over the macaque for areal mapping, laminar electrode penetration, and two-photon and optical imaging. Behaviorally, marmosets are more limited at performing regimented psychophysical tasks, but do readily accept the head restraint that is necessary for accurate eye tracking and neurophysiology, and can perform simple discriminations. Their natural gaze behavior closely resembles that of other primates, with a tendency to focus on objects of social interest including faces. Their immaturity at birth and routine twinning also makes them ideal for the study of postnatal visual development. These experimental factors, together with the theoretical advantages inherent in comparing anatomy, physiology, and behavior across related species, make the marmoset an excellent model for visual neuroscience.

Early visual learning induces long-lasting connectivity changes during rest in the human brain

Spontaneous fluctuations in resting state activity can change in response to experience-dependent plasticity and learning. Visual learning is fast and can be elicited in an MRI scanner. Here, we showed that a random dot motion coherence task can be learned within one training session. While the task activated primarily visual and parietal brain areas, learning related changes in neural activity were observed in the hippocampus. Crucially, even this rapid learning affected resting state dynamics both immediately after the learning and 24 h later. Specifically, the hippocampus changed its coupling with the striatum, in a way that was best explained as a consolidation of early learning related changes. Our findings suggest that long-lasting changes in neuronal coupling are accompanied by changes in resting state activity.

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Early learning of sensory task changes hippocampal activity.

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Coupling changes between hippocampus and striatum.

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Resting-state changes are consolidated during sleep.

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Stochastic DCM is a tool for resting state analysis.

Velocity computation in the primate visual system

Computational neuroscience combines theory and experiment to shed light on the principles and mechanisms of neural computation. This approach has been highly fruitful in the ongoing effort to understand velocity computation by the primate visual system. This Review describes the success of spatiotemporal-energy models in representing local-velocity detection. It shows why local-velocity measurements tend to differ from the velocity of the object as a whole. Certain cells in the middle temporal area are thought to solve this problem by combining local-velocity estimates to compute the overall pattern velocity. The Review discusses different models for how this might occur and experiments that test these models. Although no model is yet firmly established, evidence suggests that computing pattern velocity from local-velocity estimates involves simple operations in the spatiotemporal frequency domain.

Motion processing in the macaque: revisited with functional magnetic resonance imaging

A great deal is known about the response properties of single neurons processing sensory information. In contrast, less is understood about the collective characteristics of networks of neurons that may underlie sensory capacities of animals. We used functional magnetic resonance imaging to study the emergent properties of populations of neurons processing motion across different brain areas. Using a visual adaptation paradigm, we localized a distributed network of visual areas that process information about the direction of motion as expected from single-cell recording studies. However, we found an apparent discrepancy between the directional signals in certain visual areas as measured with blood oxygenation level-dependent imaging compared with an estimate based on the spiking of single neurons. We propose a hypothesis that may account for this difference based on the postulate that neuronal selectivity is a function of the state of adaptation. Consequently, neurons classically thought to lack information about certain attributes of the visual scene may nevertheless receive and process this information. We further hypothesize that this adaptation-dependent selectivity may arise from intra- or inter-area cellular connections, such as feedback from higher areas. This network property may be a universal principle the computational goal of which is to enhance the ability of neurons in earlier visual areas to adapt to statistical regularities of the input and therefore increase their sensitivity to detect changes along these stimulus dimensions.