Phantom touch illusion, an unexpected phenomenological effect of tactile gating in the absence of tactile stimulation.

We report the presence of a tingling sensation perceived during self-touch without physical stimulation. We used immersive virtual reality scenarios in which subjects touched their body using a virtual object. This touch resulted in a tingling sensation corresponding to the location touched on the virtual body. We called it "phantom touch illusion" (PTI). Interestingly, the illusion was also reported when subjects touched invisible (inferred) parts of their limb. We reason that this PTI results from tactile gating process during self-touch if there is no tactile input to supress. The reported PTI when touching invisible body parts indicates that tactile gating is not exclusively based on vision, but rather on multi-sensory, top-down input involving body schema. This supplementary finding shows that representations of one's own body are defined top-down, beyond the available sensory information.

Quantifying the alignment error and the effect of incomplete somatosensory feedback on motor performance in a virtual brain-computer-interface setup.

Invasive brain-computer-interfaces (BCIs) aim to improve severely paralyzed patient's (e.g. tetraplegics) quality of life by using decoded movement intentions to let them interact with robotic limbs. We argue that the performance in controlling an end-effector using a BCI depends on three major factors: decoding error, missing somatosensory feedback and alignment error caused by translation and/or rotation of the end-effector relative to the real or perceived body. Using a virtual reality (VR) model of an ideal BCI decoder with healthy participants, we found that a significant performance loss might be attributed solely to the alignment error. We used a shape-drawing task to investigate and quantify the effects of robot arm misalignment on motor performance independent from the other error sources. We found that a 90° rotation of the robot arm relative to the participant leads to the worst performance, while we did not find a significant difference between a 45° rotation and no rotation. Additionally, we compared a group of subjects with indirect haptic feedback with a group without indirect haptic feedback to investigate the feedback-error. In the group without feedback, we found a significant difference in performance only when no rotation was applied to the robot arm, supporting that a form of haptic feedback is another important factor to be considered in BCI control.

SpikeDeep-classifier: a deep-learning based fully automatic offline spike sorting algorithm.

Objective.Advancements in electrode design have resulted in micro-electrode arrays with hundreds of channels for single cell recordings. In the resulting electrophysiological recordings, each implanted electrode can record spike activity (SA) of one or more neurons along with background activity (BA). The aim of this study is to isolate SA of each neural source. This process is called spike sorting or spike classification. Advanced spike sorting algorithms are time consuming because of the human intervention at various stages of the pipeline. Current approaches lack generalization because the values of hyperparameters are not fixed, even for multiple recording sessions of the same subject. In this study, a fully automatic spike sorting algorithm called 'SpikeDeep-Classifier' is proposed. The values of hyperparameters remain fixed for all the evaluation data.Approach.The proposed approach is based on our previous study (SpikeDeeptector) and a novel background activity rejector (BAR), which are both supervised learning algorithms and an unsupervised learning algorithm (K-means). SpikeDeeptector and BAR are used to extract meaningful channels and remove BA from the extracted meaningful channels, respectively. The process of clustering becomes straight-forward once the BA is completely removed from the data. Then, K-means with a predefined maximum number of clusters is applied on the remaining data originating from neural sources only. Lastly, a similarity-based criterion and a threshold are used to keep distinct clusters and merge similar looking clusters. The proposed approach is called cluster accept or merge (CAOM) and it has only two hyperparameters (maximum number of clusters and similarity threshold) which are kept fixed for all the evaluation data after tuning.Main results.We compared the results of our algorithm with ground-truth labels. The algorithm is evaluated on data of human patients and publicly available labeled non-human primates (NHPs) datasets. The average accuracy of BAR on datasets of human patients is 92.3% which is further reduced to 88.03% after (K-means + CAOM). In addition, the average accuracy of BAR on a publicly available labeled dataset of NHPs is 95.40% which reduces to 86.95% after (K-mean + CAOM). Lastly, we compared the performance of the SpikeDeep-Classifier with two human experts, where SpikeDeep-Classifier has produced comparable results.Significance.The SpikeDeep-Classifier is evaluated on the datasets of multiple recording sessions of different species, different brain areas and different electrode types without further retraining. The results demonstrate that 'SpikeDeep-Classifier' possesses the ability to generalize well on a versatile dataset and henceforth provides a generalized and fully automated solution to offline spike sorting.Clinical trial registration numberThe clinical trial registration number for patients implanted with the Utah array isNCT 01849822.For the epilepsy patients, approval from the local ethics committee at the Ruhr-University Bochum, Germany, was obtained prior to implantation. The Clinical trial registration number for the epilepsy patients implanted with microwires is16-5670.

Does Holistic Processing Require a Large Brain? Insights From Honeybees and Wasps in Fine Visual Recognition Tasks.

The expertise of humans for recognizing faces is largely based on holistic processing mechanism, a sophisticated cognitive process that develops with visual experience. The various visual features of a face are thus glued together and treated by the brain as a unique stimulus, facilitating robust recognition. Holistic processing is known to facilitate fine discrimination of highly similar visual stimuli, and involves specialized brain areas in humans and other primates. Although holistic processing is most typically employed with face stimuli, subjects can also learn to apply similar image analysis mechanisms when gaining expertise in discriminating novel visual objects, like becoming experts in recognizing birds or cars. Here, we ask if holistic processing with expertise might be a mechanism employed by the comparatively miniature brains of insects. We thus test whether honeybees (Apis mellifera) and/or wasps (Vespula vulgaris) can use holistic-like processing with experience to recognize images of human faces, or Navon-like parameterized-stimuli. These insect species are excellent visual learners and have previously shown ability to discriminate human face stimuli using configural type processing. Freely flying bees and wasps were consequently confronted with classical tests for holistic processing, the part-whole effect and the composite-face effect. Both species could learn similar faces from a standard face recognition test used for humans, and their performance in transfer tests was consistent with holistic processing as defined for studies on humans. Tests with parameterized stimuli also revealed a capacity of honeybees, but not wasps, to process complex visual information in a holistic way, suggesting that such sophisticated visual processing may be far more spread within the animal kingdom than previously thought, although may depend on ecological constraints.

Conceptualization of relative size by honeybees.

The ability to process visual information using relational rules allows for decisions independent of the specific physical attributes of individual stimuli. Until recently, the manipulation of relational concepts was considered as a prerogative of large mammalian brains. Here we show that individual free flying honeybees can learn to use size relationship rules to choose either the larger or smaller stimulus as the correct solution in a given context, and subsequently apply the learnt rule to novel colors and shapes providing that there is sufficient input to the long wavelength (green) photoreceptor channel. Our results add a novel, size-based conceptual rule to the set of relational concepts that honeybees have been shown to master and underline the value of bees as an animal model for studying the emergence of conceptualization abilities.