1,721,016 research outputs found
Emergence of behavioral primitives in self-organizing control and composition of behavior for autonomous robots
Full text linkNeural networks approach to clustering of activity in fMRI data
No full textClusters of correlated activity in functional magnetic resonance imaging data can identify regions of interest and indicate interacting brain areas. Because the extraction of clusters is computationally complex, we apply an approximative method which is based on artificial neural networks. It allows one to find clusters of various degrees of connectivity ranging between the two extreme cases of cliques and connectivity components. We propose a criterion which allows to evaluate the relevance of such structures based on the robustness with respect to parameter variations. Exploiting the intracluster correlations, we can show that regions of substantial correlation with an external stimulus can be unambiguously separated from other activity
MODULATED EXPLORATORY DYNAMICS CAN SHAPE SELF-ORGANIZED BEHAVIOR
No full textWe study an adaptive controller that adjusts its internal parameters by self-organization of its interaction with the environment. We show that the parameter changes that occur in this low-level learning process can themselves provide a source of information to a higher-level context-sensitive learning mechanism. In this way, the context is interpreted in terms of the concurrent low-level learning mechanism. The dual learning architecture is studied in realistic simulations of a foraging robot and of a humanoid hand that manipulated an object. Both systems are driven by the same low-level scheme, but use the second-order information in different ways. While the low-level adaptation continues to follow a set of rigid learning rules, the second-order learning modulates the elementary behaviors and affects the distribution of the sensory inputs via the environment.Bernstein Centers for Computational Neuroscience [01GQ0432
Cross-Modal Distortion of Time Perception: Demerging the Effects of Observed and Performed Motion
Full text linkTemporal information is often contained in multi-sensory stimuli, but it is currently unknown how the brain combines e.g. visual and auditory cues into a coherent percept of time. The existing studies of cross-modal time perception mainly support the ?modality appropriateness hypothesis?, i.e. the domination of auditory temporal cues over visual ones because of the higher precision of audition for time perception. However, these studies suffer from methodical problems and conflicting results. We introduce a novel experimental paradigm to examine cross-modal time perception by combining an auditory time perception task with a visually guided motor task, requiring participants to follow an elliptic movement on a screen with a robotic manipulandum. We find that subjective duration is distorted according to the speed of visually observed movement: The faster the visual motion, the longer the perceived duration. In contrast, the actual execution of the arm movement does not contribute to this effect, but impairs discrimination performance by dual-task interference. We also show that additional training of the motor task attenuates the interference, but does not affect the distortion of subjective duration. The study demonstrates direct influence of visual motion on auditory temporal representations, which is independent of attentional modulation. At the same time, it provides causal support for the notion that time perception and continuous motor timing rely on separate mechanisms, a proposal that was formerly supported by correlational evidence only. The results constitute a counterexample to the modality appropriateness hypothesis and are best explained by Bayesian integration of modality-specific temporal information into a centralized ?temporal hub?.</p
Pinwheel stability in a non-Euclidean model of pattern formation in the visual cortex
No full textThe structure of neural maps in the primary visual cortex arises from the problem of representing a high-dimensional stimulus manifold on an essentially two-dimensional piece of cortical tissue. In order to treat the problem theoretically, stimuli are usually represented by a set of features, such as centroid position, orientation, spatial frequency, phase etc. Inputs to the cortex are, however, activity distributions over afferent nerve fibers; i.e., they require, in principle, a description as high-dimensional vectors. We study the relation between high-dimensional maps, which can be assumed to rely on a Euclidean geometry, and low-dimensional feature maps, which need to be formulated in Riemannian space in order to represent high-dimensional maps to a good accuracy. We show numerically that the Riemannian framework allows for a suggestive explanation of the abundance of typical structural units ("pinwheels") in feature maps emerging in the course of the adaptation process from an initially unstructured state
Pinwheel stability in a non-Euclidean model of pattern formation in the visual cortex
No full textThe structure of neural maps in the primary visual cortex arises from the problem of representing a high-dimensional stimulus manifold on an essentially two-dimensional piece of cortical tissue. In order to treat the problem theoretically, stimuli are usually represented by a set of features, such as centroid position, orientation, spatial frequency, phase etc. Inputs to the cortex are, however, activity distributions over afferent nerve fibers; i.e., they require, in principle, a description as high-dimensional vectors. We study the relation between high-dimensional maps, which can be assumed to rely on a Euclidean geometry, and low-dimensional feature maps, which need to be formulated in Riemannian space in order to represent high-dimensional maps to a good accuracy. We show numerically that the Riemannian framework allows for a suggestive explanation of the abundance of typical structural units ("pinwheels") in feature maps emerging in the course of the adaptation process from an initially unstructured state
Localized activations in a simple neural field model
No full textA quarter of a century ago Amari (Biol. Cybernet. 27 (1977) 77-87) has presented a comprehensive and very elegant solution of the one-dimensional neural field equation. In the two-dimensional case analytical results on localized solutions are available under the assumption of rotational invariance, as numerical evidence indicates that no other stable solutions exist. We present analytic results for a special case of a "tophat" interaction function, which partially justifies the implicit assumption of circular solutions and allows us to discuss the possibility of non-generic deviations from circularity. (c) 2004 Elsevier B.V. All rights reserved
Optimal mass distribution for passivity-based bipedal robots
No full textThis paper reports how and to what extent the mass distribution of a passive dynamic walker can be tuned to maximize walking speed and stability. An exploration of the complete parameter space of a bipedal walker is performed by numerical optimization, and optimal manifolds are found in terms of speed, the form of which can be explained by a physical analysis of step periods. Stability, quantified by the minimal basin of attraction, is also shown to be high along these manifolds, but with a maximum at only moderate speeds. Furthermore, it is examined how speed and stability change on different ground slopes. The observed dependence of the stability measure oil the slope is consistent with the interpretation of the walking cycle as a feedback loop, which also provides an explanation for the destabilization of the gait at higher slopes. Regarding speed, an unexpected decrease at higher slopes is observed. This effect reveals another important feature of passive dynamic walking, a swing-back phase of the swing leg near the end of a step, which decreases walking speed on the one hand, but seems to be crucial for the stability of the gait on the other hand. In conclusion, maximal robustness and highest walking speed are shown to be partly conflicting objectives of optimization
Nonlinear integration of evidence in a dynamic motor task
Full text linkReaching movements are governed by estimates of sensory and environmental quantities. If performed under uncertainty they are often based on prior expectations that summarise previous relevant information. We study the temporal evolution of the decision priors in a twoalternative forced-choice movement task
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