BOiS—Berlin Object in Scene Database: Controlled Photographic Images for Visual Search Experiments with Quantified Contextual Priors

Photographic stimuli are often used for studying human perception. To faithfully represent our natural viewing environment, these stimuli should be free of potential artifacts. If stimulus material for scientific experiments is generated from photographs that were created for a different purpose, such as advertisement or art, the scene layout and focal depth might not be typical for our visual world. For instance in advertising photos, particular objects are often centered and focused. In visual search experiments, this can lead to the so-called central viewing bias and an unwanted pre-segmentation of focused objects (Wichmann et al., 2010). Also the photographic process itself can result in artifacts, such as optical, color and geometric distortions, or introduce noise. Furthermore, some image compression methods introduce artifacts that may influence human viewing behavior. In some studies, objects are pasted into scenes using graphics editing. In this case inconsistencies in color, shading or lighting between the object and the local scene background could lead to deviations from natural viewing behavior. In order to meet the needs for publicly available stimulus material in which these artifacts are avoided, we introduce in this paper the BOiS—Berlin Object in Scene database, which provides controlled photographic stimulus material for the assessment of human visual search behavior under natural conditions. The BOiS database comprises high-resolution photographs of 130 cluttered scenes. In each scene, one particular object was chosen as search target. The scene was then photographed three times: with the target object at an expected location, at an unexpected location, or absent. Moreover, the database contains 240 different views of each target object in front of a black background. These images provide different visual cues of the target before the search is initiated. All photos were taken under controlled conditions with respect to photographic parameters and layout and were corrected for optical distortions. The BOiS database allows investigating the top-down influence of scene context, by providing contextual prior maps of each scene that quantify people's expectations to find the target object at a particular location. These maps were obtained by averaging the individual expectations of 10 subjects and can be used to model context effects on the search process. Last not least, the database includes segmentation masks of each target object in the two corresponding scene images, as well as a list of semantic information on the target object, the scene, and the two chosen locations. Moreover, we provide bottom-up saliency measures and contextual prior values at the two target object locations. While originally aimed at visual search, our database can also provide stimuli for experiments on scene viewing and object recognition, or serve as test environment for computer vision algorithms.BMBF, 01GQ0850, Bernstein Fokus Neurotechnologie - Nichtinvasive Neurotechnologie für Mensch-Maschine Interaktio

How adaptation shapes spike rate oscillations in recurrent neuronal networks

Neural mass signals from in-vivo recordings often show oscillations with frequencies ranging from <1 Hz to 100 Hz. Fast rhythmic activity in the beta and gamma range can be generated by network based mechanisms such as recurrent synaptic excitation-inhibition loops. Slower oscillations might instead depend on neuronal adaptation currents whose timescales range from tens of milliseconds to seconds. Here we investigate how the dynamics of such adaptation currents contribute to spike rate oscillations and resonance properties in recurrent networks of excitatory and inhibitory neurons. Based on a network of sparsely coupled spiking model neurons with two types of adaptation current and conductance based synapses with heterogeneous strengths and delays we use a mean-field approach to analyze oscillatory network activity. For constant external input, we find that spike-triggered adaptation currents provide a mechanism to generate slow oscillations over a wide range of adaptation timescales as long as recurrent synaptic excitation is sufficiently strong. Faster rhythms occur when recurrent inhibition is slower than excitation and oscillation frequency increases with the strength of inhibition. Adaptation facilitates such network based oscillations for fast synaptic inhibition and leads to decreased frequencies. For oscillatory external input, adaptation currents amplify a narrow band of frequencies and cause phase advances for low frequencies in addition to phase delays at higher frequencies. Our results therefore identify the different key roles of neuronal adaptation dynamics for rhythmogenesis and selective signal propagation in recurrent networks

Patterned brain stimulation, what a framework with rhythmic and noisy components might tell us about recovery maximization

Brain stimulation is having remarkable impact on clinical neurology. Brain stimulation can modulate neuronal activity in functionally segregated circumscribed regions of the human brain. Polarity-, frequency and noise specific stimulation can induce specific manipulations on neural activity.. In contrast to neocortical stimulation, deep-brain stimulation has become a tool that can dramatically improve the impact clinicians can possibly have on movement disorders. In contrast, neocortical brain stimulation is proving to be remarkably susceptible to intrinsic brain-states. Although evidence is accumulating that brain stimulation can facilitate recovery processes in patients with cerebral stroke, the high variability of results impedes successful clinical implementation. Interestingly, recent data in healthy subjects suggests that brain-state dependent patterned stimulation might help resolve some of the intrinsic variability found in previous studies. In parallel, other studies suggest that noisy stochastic resonance -like processes are a non-negligible component in NBS studies.The hypothesis developed in this manuscript is that stimulation patterning with noisy and oscillatory components will help patients recover from stroke related deficits more reliably. To address this hypothesis we focus on two factors common to both neural computation (intrinsic variables) as well as brain stimulation (extrinsic variables): noise and oscillation. We review diverse theoretical and experimental evidence that demonstrates that subject-function specific brain-states are associated with specific oscillatory activity patterns. These states are transient and can be maintained by noisy processes. The resulting control procedures can resemble homeostatic or stochastic resonance processes. In this context we try to extend awareness for inter-individual differences and the use of individualized stimulation in the recovery maximization of stroke patients