Dynamic divisive normalization circuits explain and predict change detection in monkey area MT.

Sudden changes in visual scenes often indicate important events for behavior. For their quick and reliable detection, the brain must be capable to process these changes as independently as possible from its current activation state. In motion-selective area MT, neurons respond to instantaneous speed changes with pronounced transients, often far exceeding the expected response as derived from their speed tuning profile.

Visual Stimulus Content in V4 Is Conveyed by Gamma-Rhythmic Information Packages.

Selective visual attention allows the brain to focus on behaviorally relevant information while ignoring irrelevant signals. As a possible mechanism, routing-by-synchronization was proposed: neural populations receiving attended signals align their gamma-rhythmic activity to that of the sending populations, such that incoming spikes arrive at excitability peaks of receiving populations, enhancing signal transfer. Conversely, non-attended signals arrive unaligned to the receiver's oscillation, reducing signal transfer.

Constrained inference in sparse coding reproduces contextual effects and predicts laminar neural dynamics.

When probed with complex stimuli that extend beyond their classical receptive field, neurons in primary visual cortex display complex and non-linear response characteristics. Sparse coding models reproduce some of the observed contextual effects, but still fail to provide a satisfactory explanation in terms of realistic neural structures and cortical mechanisms, since the connection scheme they propose consists only of interactions among neurons with overlapping input fields. Here we propose an extended generative model for visual scenes that includes spatial dependencies among different features.

Causally Investigating Cortical Dynamics and Signal Processing by Targeting Natural System Attractors With Precisely Timed (Electrical) Stimulation.

Electrical stimulation is a promising tool for interacting with neuronal dynamics to identify neural mechanisms that underlie cognitive function. Since effects of a single short stimulation pulse typically vary greatly and depend on the current network state, many experimental paradigms have rather resorted to continuous or periodic stimulation in order to establish and maintain a desired effect. However, such an approach explicitly leads to forced and "unnatural" brain activity.

Attention Selectively Gates Afferent Signal Transmission to Area V4.

Selective attention allows focusing on only part of the incoming sensory information. Neurons in the extrastriate visual cortex reflect such selective processing when different stimuli are simultaneously present in their large receptive fields. Their spiking response then resembles the response to the attended stimulus when presented in isolation.

Contour Integration in Dynamic Scenes: Impaired Detection Performance in Extended Presentations.

Since scenes in nature are highly dynamic, perception requires an on-going and robust integration of local information into global representations. In vision, contour integration (CI) is one of these tasks, and it is performed by our brain in a seemingly effortless manner. Following the rule of good continuation, oriented line segments are linked into contour percepts, thus supporting important visual computations such as the detection of object boundaries.

Contextual Interactions in Grating Plaid Configurations Are Explained by Natural Image Statistics and Neural Modeling.

Processing natural scenes requires the visual system to integrate local features into global object descriptions. To achieve coherent representations, the human brain uses statistical dependencies to guide weighting of local feature conjunctions. Pairwise interactions among feature detectors in early visual areas may form the early substrate of these local feature bindings.

A model for attentional information routing through coherence predicts biased competition and multistable perception.

Selective attention allows to focus on relevant information and to ignore distracting features of a visual scene. These principles of information processing are reflected in response properties of neurons in visual area V4: if a neuron is presented with two stimuli in its receptive field, and one is attended, it responds as if the nonattended stimulus was absent (biased competition). In addition, when the luminance of the two stimuli is temporally and independently varied, local field potentials are correlated with the modulation of the attended stimulus and not, or much less, correlated with the nonattended stimulus (information routing).

Marginally subcritical dynamics explain enhanced stimulus discriminability under attention.

Recent experimental and theoretical work has established the hypothesis that cortical neurons operate close to a critical state which describes a phase transition from chaotic to ordered dynamics. Critical dynamics are suggested to optimize several aspects of neuronal information processing. However, although critical dynamics have been demonstrated in recordings of spontaneously active cortical neurons, little is known about how these dynamics are affected by task-dependent changes in neuronal activity when the cortex is engaged in stimulus processing.

The role of eye movements in a contour detection task.

Vision combines local feature integration with active viewing processes, such as eye movements, to perceive complex visual scenes. However, it is still unclear how these processes interact and support each other. Here, we investigated how the dynamics of saccadic eye movements interact with contour integration, focusing on situations in which contours are difficult to find or even absent.

Dynamics of dual prism adaptation: relating novel experimental results to a minimalistic neural model.

In everyday life, humans interact with a dynamic environment often requiring rapid adaptation of visual perception and motor control. In particular, new visuo-motor mappings must be learned while old skills have to be kept, such that after adaptation, subjects may be able to quickly change between two different modes of generating movements ('dual-adaptation'). A fundamental question is how the adaptation schedule determines the acquisition speed of new skills.

Toward high performance, weakly invasive brain computer interfaces using selective visual attention.

Brain-computer interfaces have been proposed as a solution for paralyzed persons to communicate and interact with their environment. However, the neural signals used for controlling such prostheses are often noisy and unreliable, resulting in a low performance of real-world applications. Here we propose neural signatures of selective visual attention in epidural recordings as a fast, reliable, and high-performance control signal for brain prostheses.

Optimality of human contour integration.

For processing and segmenting visual scenes, the brain is required to combine a multitude of features and sensory channels. It is neither known if these complex tasks involve optimal integration of information, nor according to which objectives computations might be performed. Here, we investigate if optimal inference can explain contour integration in human subjects.

Perceptual inference predicts contextual modulations of sensory responses.

Sensory receptive fields (RFs) vary as a function of stimulus properties and measurement methods. Previous stimuli or surrounding stimuli facilitate, suppress, or change the selectivity of sensory neurons' responses. Here, we propose that these spatiotemporal contextual dependencies are signatures of efficient perceptual inference and can be explained by a single neural mechanism, input targeted divisive inhibition.

Attention improves object representation in visual cortical field potentials.

Selective attention improves perception and modulates neuronal responses, but how attention-dependent changes of cortical activity improve the processing of attended objects is an open question. Changes in total signal strength or enhancements in signal-to-noise ratio have been proposed as putative mechanisms. However, it is still not clear whether, and to what extent, these processes contribute to the large perceptual improvements.

Self-organized critical noise amplification in human closed loop control.

When humans perform closed loop control tasks like in upright standing or while balancing a stick, their behavior exhibits non-Gaussian fluctuations with long-tailed distributions. The origin of these fluctuations is not known. Here, we investigate if they are caused by self-organized critical noise amplification which emerges in control systems when an unstable dynamics becomes stabilized by an adaptive controller that has finite memory.

Modeling spatial and temporal aspects of visual backward masking.

Visual backward masking is a versatile tool for understanding principles and limitations of visual information processing in the human brain. However, the mechanisms underlying masking are still poorly understood. In the current contribution, the authors show that a structurally simple mathematical model can explain many spatial and temporal effects in visual masking, such as spatial layout effects on pattern masking and B-type masking.

Efficient computation based on stochastic spikes.

The speed and reliability of mammalian perception indicate that cortical computations can rely on very few action potentials per involved neuron. Together with the stochasticity of single-spike events in cortex, this appears to imply that large populations of redundant neurons are needed for rapid computations with action potentials. Here we demonstrate that very fast and precise computations can be realized also in small networks of stochastically spiking neurons.

Visual backward masking: Modeling spatial and temporal aspects.

In modeling visual backward masking, the focus has been on temporal effects. More specifically, an explanation has been sought as to why strongest masking can occur when the mask is delayed with respect to the target. Although interesting effects of the spatial layout of the mask have been found, only a few attempts have been made to model these phenomena.

Towards on-line adaptation of neuro-prostheses with neuronal evaluation signals.

Many experiments have successfully demonstrated that prosthetic devices for restoring lost body functions can in principle be controlled by brain signals. However, stable long-term application of these devices, required for paralyzed patients, may suffer substantially from on-going signal changes for example adapting neural activities or movements of the electrodes recording brain activity. These changes currently require tedious re-learning procedures which are conducted and supervised under laboratory conditions, hampering the everyday use of such devices.

Extending the shine-through effect to classical masking paradigms.

A vernier, presented for a short time, shines through a following grating if the grating contains nine and more elements but remains largely invisible for smaller gratings. Therefore, extended grating masks yield, surprisingly, less masking than smaller ones. Here, we show that this mask size effect is not unique to grating masks.

Local interactions in neural networks explain global effects in Gestalt processing and masking.

One of the fundamental and puzzling questions in vision research is how objects are segmented from their backgrounds and how object formation evolves in time. The recently discovered shine-through effect allows one to study object segmentation and object formation of a masked target depending on the spatiotemporal Gestalt of the masking stimulus (Herzog & Koch, 2001). In the shine-through effect, a vernier (two abutting lines) precedes a grating for a very short time.