Non-reviewed Conference Papers and Abstracts

Background: The cerebellum is crucial for motor control (e.g. of gait and posture) and motor learning. Therefore, motor rehabilitation in patients with degenerative cerebellar disease is challenging, and the capability of motor improvements for these patients is not fully understood. We have recently shown, that a 8 weeks motor training program based on playing whole-body controlled video games can lead to a reduction of ataxia symptoms and an improvement in gait in children with degenerative cerebellar disease (Ilg 2012). In this study, we examined quantitatively, whether this motor training leads to - specific improvements in motor control of complex whole-body movements, which are relevant in everyday life and which cannot be explained simply by improvements in general fitness Methods: To assess the specific effects of motor training, we analyzed the movement behavior during playing the Xbox Kinect™ game “Light Race” of 10 children with degenerative cerebellar disease versus 10 age-matched controls. Here, subjects have to control an avatar performing one minute sequences of rapid stepping movements towards different goals. Cerebellar children were tested in this game before and after an 8 weeks training program including different video games focusing on dynamic balance, trunk-limb coordination and goal-directed movements. The rapid stepping sequences during game playing were analyzed with respect to dynamic stability (Hof 2005), multi-joint coordination, anticipatory postural adjustments and movement variability. Results: After 8 weeks training, children improved their general game play with respect to games scores, increased averaged velocity and dynamic stability. In addition, specific measures revealed (a) improved anticipatory postural adjustments before stepping (p=0.04), (b) decreased movement decomposition (p=0.01), (c) decreased movement variability during stepping (p=0.04) as well as increased dynamic stability at the end of the stepping movements (p=0.01). Conclusion: Despite progressive cerebellar degeneration children are able to improve specific aspects of motor performance in complex whole-body movements which are relevant in everyday life (e.g. rapid stepping movements to compensate for gait perturbations). Therefore, directed training of whole-body controlled video games present a highly motivational, cost-efficient and home-based rehabilitation strategy to train dynamic balance, multi-joint coordination and interaction with dynamic environments in a large variety of young-onset neurological conditions. References: Hof A, et al. J Biomech 38: 1-8, 2005. Ilg W, et al. Neurology 79: 2056-2060, 2012.

Facial expressions are essentially dynamic. However, most existing research has focused on static pictures of faces. The computational neural functions that underlie the processing of dynamic faces are largely unknown. Combining multiple physiologically relevant neural encoding principles, we propose a neural model that accomplishes the recognition of facial expressions robustly over different facial identities. Our model is based on a physiologically plausible hierarchical model of the ventral stream for the extraction of form features, building on a previous model for the processing of identity from static pictures of faces [Giese {{&}} Leopold, 2005, Neurocomputing]. It combines norm-referenced as well as example based coding of patterns, and different physiologically-inspired mechanisms for the encoding of temporal sequences. In example-based coding, 'snapshot neurons' that are selective for frames (snapshots) form the dynamic face sequence, they are modeled by radial basis function units (see figure). These neurons are laterally coupled, resulting in a network which is a dynamic neural field with an asymmetric interaction kernel. This makes the snapshot neurons sequence selective: we find only a weak response if frames occur in an incorrect temporal order. Facial expression neurons at highest level sum activity over the neural field that encodes one facial expression (e.g. ‘happy’ or ‘sad’). In norm-referenced encoding, face-selective neurons encode distance and direction of the stimulus relative to a norm stimulus, here neutral expressions. This computational function can be implemented by a simple feed-forward neural network [Giese {{&}} Leopold, 2005, Neurocomputing]. For static face processing this norm-referenced mechanism accounts better for the neurophysiological data than an example-based mechanism. In the dynamic case, the evolution of facial expression corresponds to a vector with increasing length in the direction of the extreme expression; face neurons show monotonic increases (or decreases) of activity during the time-course of the expression. Their output is fed into ‘differentiator neurons’ which are detecting raising flanks in their input, thus becoming selective to dynamic facial expressions in the correct temporal order, while they fail to respond to static expressions and ones with inverse temporal order. This proposed mechanism is more efficient in terms of neural hardware, since it encodes only neutral faces and the extreme expressions. The model is tested with movies showing real monkey expressions (‘threat’ and ‘coo-call‘) and a standard data basis containing a large number of human expressions of different individuals. The performance of different physiologically plausible circuits for the recognition of dynamic facial expressions is evaluated, and specific predictions for the behavior of different classes of dynamic faceselective neurons are discussed, which might e.g. be suitable to distinguish different computational mechanisms based on single-cell recordings from dynamic face-selective neurons.

Understanding how semantic information is represented in the brain has been an important research focus of neuroscience in the past few years. Unlike 'traditional' neural (de)coding approaches, which study the relationship between stimulus and neural response, we are interested in higher-order relational coding: we ask how perceived relationships between stimuli (e.g. similarity) are connected to corresponding relationships in the neural activity. Our approach addresses the semantical problem, i.e. how terms (here stimuli) come to have their (possibly subjective) meaning, from the perspective of the network theory of semantics (Churchland 1984). This theory posits that meaning arises from the network of concepts within which a given term is embedded. We showed previously (Endres et al 2010, AMAI) that Formal Concept Analysis (FCA, (Ganter {{&}} Wille 1999)) can reveal interpretable semantic information (e.g. specialization hierarchies, or feature-based representation) from electrophysiological data. Unlike other analysis methods (e.g. hierarchical clustering), FCA does not impose inappropriate structure on the data. FCA is a mathematical formulation of the explicit coding hypothesis (Foldiak, 2009, Curr. Biol.) Here, we investigate whether similar findings can be obtained from fMRI BOLD responses recorded from human subjects. While the BOLD response provides only an indirect measure of neural activity on a much coarser spatio-temporal scale than electrophysiological recordings, it has the advantage that it can be recorded from humans, which can be questioned about their perceptions during the experiment, thereby obviating the need of interpreting animal behavioural responses. Furthermore, the BOLD signal can be recorded from the whole brain simultaneously. In our experiment, a single human subject was scanned while viewing 72 grayscale pictures of animate and inanimate objects in a target detection task (Siemens Trio 3T scanner, GE-EPI, TE=40ms, 38 axial slices, TR=3.08s, 48 sessions, amounting to a total of 10,176 volume images). These pictures comprise the formal objects for FCA. We computed formal attributes by learning a hierarchical Bayesian classifier, which maps BOLD responses onto binary features, and these features onto object labels. The connectivity matrix between the binary features and the object labels can then serve as the formal context. In line with previous reports, FCA revealed a clear dissociation between animate and inanimate objects in a high-level visual area (inferior temporal cortex, IT), with the inanimate category including plants. The inanimate category was subdivided into plants and non-plants when we increased the number of attributes extracted from the fMRI responses. FCA also highlighted organizational differences between the IT and the primary visual cortex, V1. We show that subjective familiarity and similarity ratings are strongly correlated with the attribute structure computed from the fMRI signal (Endres et al. 2012, ICFCA).

Similarity ratings are a widely used tool for the assessment of high-level perceptual similarity. Several approaches to conceptualizing similarity exist. We are concerned with the featural approach which was developed by [Tversky, 1977, Psychological Review 84:327-352] and mathematically formalized in [Lengnink, 1996, PhD Dissertation, TU Darmstadt]. This formalization posits a partial order between pairs of objects (stimuli) as the fundamental mathematical structure of similarity, traditional similarity measures (e.g. Russell-Rao, Jaccard etc.) are conceived as order-preserving mappings from the partial order between pairs into the (real) numbers. This approach preserves the main structural features of Tversky's model, and makes additional predictions about the (non-)comparability of similarity between pairs of objects. We tested these predictions experimentally: a) subjects rated the similarity between natural images on a 7-point Likert scale, and b) they ordered pairs of images by their perceived similarity. We find that the ordering predictions of ratings are well preserved (>85%). One drawback of similarity ratings is that they provide only an implicit measure of “relatedness”. We employ theoretical framework of Formal Concept Analysis [Ganter {{&}} Wille, 1996, Formal Concept Analysis, Springer, New York] to make the relationships explicit as concept lattices, which generalizes traditional approaches based on hierarchical clustering. [Support from EU Commission, EC FP7-ICT-248311 AMARSi, ABC PITN-GA-011-290011: DFG GI 305/4-1, DFG GZ: KA 1258/15-1, BMBF, FKZ: 01GQ1002A]