In complex natural environments, sensory systems are constantly exposed to a large stream of inputs. Novel or rare stimuli, which are often associated with behaviorally important events, are typically processed differently than the steady sensory background, which has less relevance. Neural signatures of such differential processing, commonly referred to as novelty detection, have been identified on the level of EEG recordings as mismatch negativity (MMN) and on the level of single neurons as stimulus-specific adaptation (SSA). Here, we propose a multi-scale recurrent network with synaptic depression to explain how novelty detection can arise in the whisker-related part of the somatosensory thalamocortical loop. The "minimalistic" architecture and dynamics of the model presume that neurons in cortical layer 6 adapt, via synaptic depression, specifically to a frequently presented stimulus, resulting in reduced population activity in the corresponding cortical column when compared with the population activity evoked by a rare stimulus. This difference in population activity is then projected from the cortex to the thalamus and amplified through the interaction between neurons of the primary and reticular nuclei of the thalamus, resulting in rhythmic oscillations. These differentially activated thalamic oscillations are forwarded to cortical layer 4 as a late secondary response that is specific to rare stimuli that violate a particular stimulus pattern. Model results show a strong analogy between this late single neuron activity and EEG-based mismatch negativity in terms of their common sensitivity to presentation context and timescales of response latency, as observed experimentally. Our results indicate that adaptation in L6 can establish the thalamocortical dynamics that produce signatures of SSA and MMN and suggest a mechanistic model of novelty detection that could generalize to other sensory modalities.

Sensory information is conveyed by populations of neurons, and coding strategies cannot always be deduced when considering individual neurons. Moreover, information coding depends on the number of neurons available and on the composition of the population when multiple classes with different response properties are available. Here, we study population coding in human tactile afferents by employing a recently developed simulator of mechanoreceptor firing activity. First, we highlight the interplay of afferents within each class. We demonstrate that the optimal afferent density to convey maximal information depends on both the tactile feature under consideration and the afferent class. Second, we find that information is spread across different classes for all tactile features and that each class encodes both redundant and complementary information with respect to the other afferent classes. Specifically, combining information from multiple afferent classes improves information transmission and is often more efficient than increasing the density of afferents from the same class. Finally, we examine the importance of temporal and spatial contributions, respectively, to the joint spatiotemporal code. On average, destroying temporal information is more destructive than removing spatial information, but the importance of either depends on the stimulus feature analyzed. Overall, our results suggest that both optimal afferent innervation densities and the composition of the population depend in complex ways on the tactile features in question, potentially accounting for the variety in which tactile peripheral populations are assembled in different regions across the body.

In many sensory systems, information transmission is constrained by a bottleneck, where the number of output neurons is vastly smaller than the number of input neurons. Efficient coding theory predicts that in these scenarios the brain should allocate its limited resources by removing redundant information. Previous work has typically assumed that receptors are uniformly distributed across the sensory sheet, when in reality these vary in density, often by an order of magnitude. How, then, should the brain efficiently allocate output neurons when the density of input neurons is nonuniform? Here, we show analytically and numerically that resource allocation scales nonlinearly in efficient coding models that maximize information transfer, when inputs arise from separate regions with different receptor densities. Importantly, the proportion of output neurons allocated to a given input region changes depending on the width of the bottleneck, and thus cannot be predicted from input density or region size alone. Narrow bottlenecks favor magnification of high density input regions, while wider bottlenecks often cause contraction. Our results demonstrate that both expansion and contraction of sensory input regions can arise in efficient coding models and that the final allocation crucially depends on the neural resources made available.

The estimation of parameters that affect the dynamics of objects—such as viscosity or internal degree of freedom—is an important step in autonomous and dexterous robotic manipulation of objects. However, accurate and efficient estimation of these object parameters may be challenging due to complex, highly nonlinear underlying physical processes. To improve on the quality of otherwise hand-crafted solutions, automatic generation of control strategies can be helpful. We present a framework that uses active learning to help with sequential gathering of data samples,using information-theoretic ciriteria to find the optimal actions to perform at each time step. We demonstrate the usefulness of our approach on a robotic hand-arm setup, where the task involves shaking bottles of different liquids in order to determine the liquid's viscosity from only tactile feedback. We optimize the shaking frequency and the rotation angle of shaking in an online manner in order to speed up convergence of estimates.

Abstract—One of the main challenges in service robotics is to equip dexterous robotic hands with sensitive tactile sensors in order to cope with the inherent problems posed by unknown and unstructured environments. As the increasing mechatronic integration of complex robotic hands leaves little additional space for proprioceptive sensors, exteroceptive tactile sensors become more and more important. We present a novel tactile sensor design, based on piezo-resistive soft material and a crossed-wire approach. We present the development of a first prototype and its evaluation in various classification tasks, showing promising results. I.