The future lies in a pair of tactile hands.

Advancing robot hand dexterity with optical tactile sensing raises questions about humanoid robotics.

Neuromorphic Tactile Edge Orientation Classification in an Unsupervised Spiking Neural Network.

Dexterous manipulation in robotic hands relies on an accurate sense of artificial touch. Here we investigate neuromorphic tactile sensation with an event-based optical tactile sensor combined with spiking neural networks for edge orientation detection. The sensor incorporates an event-based vision system (mini-eDVS) into a low-form factor artificial fingertip (the NeuroTac). The processing of tactile information is performed through a Spiking Neural Network with unsupervised Spike-Timing-Dependent Plasticity (STDP) learning, and the resultant output is classified with a 3-nearest neighbours classifier. Edge orientations were classified in 10-degree increments while tapping vertically downward and sliding horizontally across the edge. In both cases, we demonstrate that the sensor is able to reliably detect edge orientation, and could lead to accurate, bio-inspired, tactile processing in robotics and prosthetics applications.

Degenerate boundaries for multiple-alternative decisions.

Integration-to-threshold models of two-choice perceptual decision making have guided our understanding of human and animal behavior and neural processing. Although such models seem to extend naturally to multiple-choice decision making, consensus on a normative framework has yet to emerge, and hence the implications of threshold characteristics for multiple choices have only been partially explored. Here we consider sequential Bayesian inference and a conceptualisation of decision making as a particle diffusing in n-dimensions. We show by simulation that, within a parameterised subset of time-independent boundaries, the optimal decision boundaries comprise a degenerate family of nonlinear structures that jointly depend on the state of multiple accumulators and speed-accuracy trade-offs. This degeneracy is contrary to current 2-choice results where there is a single optimal threshold. Such boundaries support both stationary and collapsing thresholds as optimal strategies for decision-making, both of which result from stationary representations of nonlinear boundaries. Our findings point towards a normative theory of multiple-choice decision making, provide a characterisation of optimal decision thresholds under this framework, and inform the debate between stationary and dynamic decision boundaries for optimal decision making.

Artificial SA-I, RA-I and RA-II/vibrotactile afferents for tactile sensing of texture.

Robot touch can benefit from how humans perceive tactile textural information, from the stimulation mode to which tactile channels respond, then the tactile cues and encoding. Using a soft biomimetic tactile sensor (the TacTip) based on the physiology of the dermal-epidermal boundary, we construct two biomimetic tactile channels based on slowly adapting SA-I and rapidly adapting RA-I afferents, and introduce an additional sub-modality for vibrotactile information with an embedded microphone interpreted as an artificial RA-II channel. These artificial tactile channels are stimulated dynamically with a set of 13 artificial rigid textures comprising raised-bump patterns on a rotating drum that vary systematically in roughness. Methods employing spatial, spatio-temporal and temporal codes are assessed for texture classification insensitive to stimulation speed. We find: (i) spatially encoded frictional cues provide a salient representation of texture; (ii) a simple transformation of spatial tactile features to model natural afferent responses improves the temporal coding; and (iii) the harmonic structure of induced vibrations provides a pertinent code for speed-invariant texture classification. Just as human touch relies on an interplay between slowly adapting (SA-I), rapidly adapting (RA-I) and vibrotactile (RA-II) channels, this tripartite structure may be needed for future robot applications with human-like dexterity, from prosthetics to materials testing, handling and manipulation.

Artificial SA-I and RA-I afferents for tactile sensing of ridges and gratings.

For robot touch to reach the capabilities of human touch, artificial tactile sensors may require transduction principles like those of natural tactile afferents. Here we propose that a biomimetic tactile sensor (the TacTip) could provide suitable artificial analogues of the tactile skin dynamics, afferent responses and population encoding. Our three-dimensionally printed sensor skin is based on the physiology of the dermal-epidermal interface with an underlying mesh of biomimetic intermediate ridges and dermal papillae, comprising inner pins tipped with markers. Slowly adapting SA-I activity is modelled by marker displacements and rapidly adapting RA-I activity by marker speeds. We test the biological plausibility of these artificial population codes with three classic experiments used for natural touch: (1a) responses to normal pressure to test adaptation of single afferents and spatial modulation across the population; (1b) responses to bars, edges and gratings to compare with measurements from monkey primary afferents; and (2) discrimination of grating orientation to compare with human perceptual performance. Our results show a match between artificial and natural touch at single afferent, population and perceptual levels. As expected, natural skin is more sensitive, which raises a challenge to fabricate a biomimetic fingertip that demonstrates human sensitivity using the transduction principles of human touch.

Tactile Model O: Fabrication and Testing of a 3D-Printed, Three-Fingered Tactile Robot Hand.

Bringing tactile sensation to robotic hands will allow for more effective grasping, along with a wide range of benefits of human-like touch. Here, we present a three-dimensional-printed, three-fingered tactile robot hand comprising an OpenHand ModelO customized to house a TacTip soft biomimetic tactile sensor in the distal phalanx of each finger. We expect that combining the grasping capabilities of this underactuated hand with sophisticated tactile sensing will result in an effective platform for robot hand research-the Tactile Model O (T-MO). The design uses three JeVois machine vision systems, with each comprising a miniature camera in the tactile fingertip with a processing module in the base of the hand. To evaluate the capabilities of the T-MO, we benchmark its grasping performance by using the Gripper Assessment Benchmark on the Yale-CMU-Berkeley object set. Tactile sensing capabilities are evaluated by performing tactile object classification on 26 objects and predicting whether a grasp will successfully lift each object. Results are consistent with the state of the art, taking advantage of advances in deep learning applied to tactile image outputs. Overall, this work demonstrates that the T-MO is an effective platform for robot hand research and we expect it to open up a range of applications in autonomous object handling.

The TacTip Family: Soft Optical Tactile Sensors with 3D-Printed Biomimetic Morphologies.

Tactile sensing is an essential component in human-robot interaction and object manipulation. Soft sensors allow for safe interaction and improved gripping performance. Here we present the TacTip family of sensors: a range of soft optical tactile sensors with various morphologies fabricated through dual-material 3D printing. All of these sensors are inspired by the same biomimetic design principle: transducing deformation of the sensing surface via movement of pins analogous to the function of intermediate ridges within the human fingertip. The performance of the TacTip, TacTip-GR2, TacTip-M2, and TacCylinder sensors is here evaluated and shown to attain submillimeter accuracy on a rolling cylinder task, representing greater than 10-fold super-resolved acuity. A version of the TacTip sensor has also been open-sourced, enabling other laboratories to adopt it as a platform for tactile sensing and manipulation research. These sensors are suitable for real-world applications in tactile perception, exploration, and manipulation, and will enable further research and innovation in the field of soft tactile sensing.

Biomimetic Active Touch with Fingertips and Whiskers.

This study provides a synthetic viewpoint that compares, contrasts, and draws commonalities for biomimetic perception over a range of tactile sensors and tactile stimuli. Biomimetic active perception is formulated from three principles: (i) evidence accumulation based on leading models of perceptual decision making; (ii) action selection with an evidence-based policy, here based on overt focal attention; and (iii) sensory encoding of evidence based on neural coding. Two experiments with each of three biomimetic tactile sensors are considered: the iCub (capacitive) fingertip, the TacTip (optical) tactile sensor, and BIOTACT whiskers. For each sensor, one experiment considers a similar task (perception of shape and location) and the other a different tactile perception task. In all experiments, active perception with a biomimetic action selection policy based on focal attention outperforms passive perception with static or random action selection. The active perception also consistently reaches superresolved accuracy (hyperacuity) finer than the spacing between tactile elements. Biomimetic active touch thus offers a common approach for biomimetic tactile sensors to accurately and robustly characterize and explore non-trivial, uncertain environments analogous to how animals perceive the natural world.

Embodied choice: how action influences perceptual decision making.

Embodied Choice considers action performance as a proper part of the decision making process rather than merely as a means to report the decision. The central statement of embodied choice is the existence of bidirectional influences between action and decisions. This implies that for a decision expressed by an action, the action dynamics and its constraints (e.g. current trajectory and kinematics) influence the decision making process. Here we use a perceptual decision making task to compare three types of model: a serial decision-then-action model, a parallel decision-and-action model, and an embodied choice model where the action feeds back into the decision making. The embodied model incorporates two key mechanisms that together are lacking in the other models: action preparation and commitment. First, action preparation strategies alleviate delays in enacting a choice but also modify decision termination. Second, action dynamics change the prospects and create a commitment effect to the initially preferred choice. Our results show that these two mechanisms make embodied choice models better suited to combine decision and action appropriately to achieve suitably fast and accurate responses, as usually required in ecologically valid situations. Moreover, embodied choice models with these mechanisms give a better account of trajectory tracking experiments during decision making. In conclusion, the embodied choice framework offers a combined theory of decision and action that gives a clear case that embodied phenomena such as the dynamics of actions can have a causal influence on central cognition.

Probabilistic Decision Making with Spikes: From ISI Distributions to Behaviour via Information Gain.

Computational theories of decision making in the brain usually assume that sensory 'evidence' is accumulated supporting a number of hypotheses, and that the first accumulator to reach threshold triggers a decision in favour of its associated hypothesis. However, the evidence is often assumed to occur as a continuous process whose origins are somewhat abstract, with no direct link to the neural signals - action potentials or 'spikes' - that must ultimately form the substrate for decision making in the brain. Here we introduce a new variant of the well-known multi-hypothesis sequential probability ratio test (MSPRT) for decision making whose evidence observations consist of the basic unit of neural signalling - the inter-spike interval (ISI) - and which is based on a new form of the likelihood function. We dub this mechanism s-MSPRT and show its precise form for a range of realistic ISI distributions with positive support. In this way we show that, at the level of spikes, the refractory period may actually facilitate shorter decision times, and that the mechanism is robust against poor choice of the hypothesized data distribution. We show that s-MSPRT performance is related to the Kullback-Leibler divergence (KLD) or information gain between ISI distributions, through which we are able to link neural signalling to psychophysical observation at the behavioural level. Thus, we find the mean information needed for a decision is constant, thereby offering an account of Hick's law (relating decision time to the number of choices). Further, the mean decision time of s-MSPRT shows a power law dependence on the KLD offering an account of Piéron's law (relating reaction time to stimulus intensity). These results show the foundations for a research programme in which spike train analysis can be made the basis for predictions about behavior in multi-alternative choice tasks.

The state of the art in biomimetics.

Biomimetics is a research field that is achieving particular prominence through an explosion of new discoveries in biology and engineering. The field concerns novel technologies developed through the transfer of function from biological systems. To analyze the impact of this field within engineering and related sciences, we compiled an extensive database of publications for study with network-based information analysis techniques. Criteria included publications by year and journal or conference, and subject areas judged by popular and common terms in titles. Our results reveal that this research area has expanded rapidly from less than 100 papers per year in the 1990s to several thousand papers per year in the first decade of this century. Moreover, this research is having impact across a variety of research themes, spanning robotics, computer science and bioengineering. In consequence, biomimetics is becoming a leading paradigm for the development of new technologies that will potentially lead to significant scientific, societal and economic impact in the near future.

The basal ganglia optimize decision making over general perceptual hypotheses.

The basal ganglia are a subcortical group of interconnected nuclei involved in mediating action selection within cortex. A recent proposal is that this selection leads to optimal decision making over multiple alternatives because the basal ganglia anatomy maps onto a network implementation of an optimal statistical method for hypothesis testing, assuming that cortical activity encodes evidence for constrained gaussian-distributed alternatives. This letter demonstrates that this model of the basal ganglia extends naturally to encompass general Bayesian sequential analysis over arbitrary probability distributions, which raises the proposal to a practically realizable theory over generic perceptual hypotheses. We also show that the evidence in this model can represent either log likelihoods, log-likelihood ratios, or log odds, all leading proposals for the cortical processing of sensory data. For these reasons, we claim that the basal ganglia optimize decision making over general perceptual hypotheses represented in cortex. The relation of this theory to cortical encoding, cortico-basal ganglia anatomy, and reinforcement learning is discussed.

Optimal decision-making in mammals: insights from a robot study of rodent texture discrimination.

Texture perception is studied here in a physical model of the rat whisker system consisting of a robot equipped with a biomimetic vibrissal sensor. Investigations of whisker motion in rodents have led to several explanations for texture discrimination, such as resonance or stick-slips. Meanwhile, electrophysiological studies of decision-making in monkeys have suggested a neural mechanism of evidence accumulation to threshold for competing percepts, described by a probabilistic model of Bayesian sequential analysis. For our robot whisker data, we find that variable reaction-time decision-making with sequential analysis performs better than the fixed response-time maximum-likelihood estimation. These probabilistic classifiers also use whatever available features of the whisker signals aid the discrimination, giving improved performance over a single-feature strategy, such as matching the peak power spectra of whisker vibrations. These results cast new light on how the various proposals for texture discrimination in rodents depend on the whisker contact mechanics and suggest the possibility of a common account of decision-making across mammalian species.

The effect of whisker movement on radial distance estimation: a case study in comparative robotics.

Whisker movement has been shown to be under active control in certain specialist animals such as rats and mice. Though this whisker movement is well characterized, the role and effect of this movement on subsequent sensing is poorly understood. One method for investigating this phenomena is to generate artificial whisker deflections with robotic hardware under different movement conditions. A limitation of this approach is that assumptions must be made in the design of any artificial whisker actuators, which will impose certain restrictions on the whisker-object interaction. In this paper we present three robotic whisker platforms, each with different mechanical whisker properties and actuation mechanisms. A feature-based classifier is used to simultaneously discriminate radial distance to contact and contact speed for the first time. We show that whisker-object contact speed predictably affects deflection magnitudes, invariant of whisker material or whisker movement trajectory. We propose that rodent whisker control allows the animal to improve sensing accuracy by regulating contact speed induced touch-to-touch variability.

Efficient fitting of conductance-based model neurons from somatic current clamp.

Estimating biologically realistic model neurons from electrophysiological data is a key issue in neuroscience that is central to understanding neuronal function and network behavior. However, directly fitting detailed Hodgkin-Huxley type model neurons to somatic membrane potential data is a notoriously difficult optimization problem that can require hours/days of supercomputing time. Here we extend an efficient technique that indirectly matches neuronal currents derived from somatic membrane potential data to two-compartment model neurons with passive dendrites. In consequence, this approach can fit semi-realistic detailed model neurons in a few minutes. For validation, fits are obtained to model-derived data for various thalamo-cortical neuron types, including fast/regular spiking and bursting neurons. A key aspect of the validation is sensitivity testing to perturbations arising in experimental data, including sampling rates, inadequately estimated membrane dynamics/channel kinetics and intrinsic noise. We find that maximal conductance estimates and the resulting membrane potential fits diverge smoothly and monotonically from near-perfect matches when unperturbed. Curiously, some perturbations have little effect on the error because they are compensated by the fitted maximal conductances. Therefore, the extended current-based technique applies well under moderately inaccurate model assumptions, as required for application to experimental data. Furthermore, the accompanying perturbation analysis gives insights into neuronal homeostasis, whereby tuning intrinsic neuronal properties can compensate changes from development or neurodegeneration.

A simple method for characterizing passive and active neuronal properties: application to striatal neurons.

The study of active and passive neuronal dynamics usually relies on a sophisticated array of electrophysiological, staining and pharmacological techniques. We describe here a simple complementary method that recovers many findings of these more complex methods but relies only on a basic patch-clamp recording approach. Somatic short and long current pulses were applied in vitro to striatal medium spiny (MS) and fast spiking (FS) neurons from juvenile rats. The passive dynamics were quantified by fitting two-compartment models to the short current pulse data. Lumped conductances for the active dynamics were then found by compensating this fitted passive dynamics within the current-voltage relationship from the long current pulse data. These estimated passive and active properties were consistent with previous more complex estimations of the neuron properties, supporting the approach. Relationships within the MS and FS neuron types were also evident, including a graduation of MS neuron properties consistent with recent findings about D1 and D2 dopamine receptor expression. Application of the method to simulated neuron data supported the hypothesis that it gives reasonable estimates of membrane properties and gross morphology. Therefore detailed information about the biophysics can be gained from this simple approach, which is useful for both classification of neuron type and biophysical modelling. Furthermore, because these methods rely upon no manipulations to the cell other than patch clamping, they are ideally suited to in vivo electrophysiology.

Sensory prediction or motor control? Application of marr-albus type models of cerebellar function to classical conditioning.

Marr-Albus adaptive filter models of the cerebellum have been applied successfully to a range of sensory and motor control problems. Here we analyze their properties when applied to classical conditioning of the nictitating membrane response in rabbits. We consider a system-level model of eyeblink conditioning based on the anatomy of the eyeblink circuitry, comprising an adaptive filter model of the cerebellum, a comparator model of the inferior olive and a linear dynamic model of the nictitating membrane plant. To our knowledge, this is the first model that explicitly includes all these principal components, in particular the motor plant that is vital for shaping and timing the behavioral response. Model assumptions and parameters were systematically investigated to disambiguate basic computational capacities of the model from features requiring tuning of properties and parameter values. Without such tuning, the model robustly reproduced a range of behaviors related to sensory prediction, by displaying appropriate trial-level associative learning effects for both single and multiple stimuli, including blocking and conditioned inhibition. In contrast, successful reproduction of the real-time motor behavior depended on appropriate specification of the plant, cerebellum and comparator models. Although some of these properties appear consistent with the system biology, fundamental questions remain about how the biological parameters are chosen if the cerebellar microcircuit applies a common computation to many distinct behavioral tasks. It is possible that the response profiles in classical conditioning of the eyeblink depend upon operant contingencies that have previously prevailed, for example in naturally occurring avoidance movements.

Nonlinear dynamic modeling of isometric force production in primate eye muscle.

Although the oculomotor plant is usually modeled as a linear system, recent studies of ocular motoneuron behavior have drawn attention to the presence of significant nonlinearities. One source of these is the development of muscle force in response to changes in motoneuron firing rate. Here, we attempt to simulate the production of isometric force by the primate lateral rectus muscle in response to electrical stimulation [A. Fuchs and E. Luschei, "Development of isometric tension in simian extraocular muscle," J. Physiol., vol. 219, no. 1, pp. 155-166, 1971] by comparing four different modeling approaches. The data could be well fitted either by parameter estimation for physically based models of force production [J. Bobet, E. R. Gossen, and R. B. Stein, "A comparison of models of force production during stimulated isometric ankle dorsiflexion in humans," IEEE Trans. Neural Syst. Rehabil. Eng., vol. 13, no. 4, pp. 444-451, Dec. 2005; E. Mavritsaki, N. Lepora, J. Porrill, C. H. Yeo, and P. Dean, "Response linearity determined by recruitment strategy in detailed model of nictitating membrane control," Biol. Cybern., vol. 96, no. 1, pp. 39-57, 2007], or by the application of a generic method for nonlinear system identification (the nonlinear autoregressive with exogenous input (NARX) model). These results suggest that nonlinear system identification may be a useful method for modeling more general aspects of muscle function, and provide a basis for distributed models of motor units in extraocular muscle for understanding dynamic oculomotor control. The success of previous linear models points to the potential importance of motor unit recruitment in overcoming nonlinearities in the oculomotor plant.