Hydrogel-driven carbon nanotube microtransducers

We demonstrate the fabrication and integration of active microstructures based on composites of 3D carbon nanotube (CNT) frameworks and hydrogels. The alignment of the CNTs within the microstructures converts the isotropic expansion of the gel into a directed anisotropic motion. Actuation by a moisture-responsive gel is observed by changing the ambient humidity, and is predicted by a finite element model of the composite system. These shape changes are rapid and can be transduced electrically within a microfluidic channel, by measuring the resistance change across a CNT microstructure during expansion of the gel. Our results suggest that combinations of gels with aligned CNTs can be a platform for directing the actuation of gels and measuring their response to stimuli. © 2011 The Royal Society of Chemistry.

Theoretical and experimental analysis of pneumatic balloon microactuators

This paper presents a theoretical and experimental analysis of a biologically inspired balloon-type pneumatic microactuator. The operation principle of pneumatic balloon actuators (PBA's) is based on an asymmetric deflection of two PDMS layers with different thicknesses or different Young's moduli that are bonded together. A new analytical 2D model that describes the complex behavior of these actuators is presented and validated using both 3D FEM models and measurements. The actuators have dimensions ranging from 11 mm × 2 mm × 0.24 mm to 4 mm × 1 mm × 0.12 mm. Their fabrication is based on micromolding of PDMS, and can therefore easily be fabricated in high throughput. Measurements showed that the analytical model provides a qualitative description of the actuator behavior, and showed that the larger actuators are capable of delivering a 7 mm stroke at a supply pressure of 70 kPa and a force of max 22 mN at a supply pressure of 105 kPa. © 2011 Elsevier B.V. All rights reserved.

A hybrid surface tension seal for pneumatic and hydraulic microactuators

Recent research revealed that microactuators driven by pressurized fluids are able to generate high power and force densities at microscale. One of the main technological barriers in the development of these actuators is the fabrication low friction seals. This paper presents a novel scalable seal technology, which resists the actuation pressure relying on a combination of a clearance seal and a surface tension seal. This approach allows to seal pressures of more than 800 kPa without leakage. The seal is tested on an actuator with a bore of 0.8 mm2 and a length of 13 mm, which was able to generate forces up to 0.32 N. © 2008 Springer-Verlag.

A novel hydraulic microactuator sealed by surface tension

In order to improve the power density of microactuators, recent research focuses on the applicability of fluidic power at microscale. The main encountered difficulties in the development of small fluidic actuators are related to production tolerances and assembly requirements. Furthermore, these actuators tend to comprise highly three-dimensional parts, which are incompatible with traditional microproduction technologies. This paper presents accurate production and novel assembly techniques for the development of a hydraulic microactuator. In addition, a scalable low friction seal, relying on surface tension forces, is presented. A prototype piston-type microactuator with a bore of 1 mm and a length of 13 mm is developed. Using a gallium-based surface tension seal, pressures of more than 90 kPa have been sealed without leakage. © 2005 Elsevier B.V. All rights reserved.

Production and characterization of a hydraulic microactuator

In order to improve the power density of microactuators, recent research focuses on the applicability of fluidic actuation at the microscale. The main encountered difficulties in the development of small fluidic actuators are related to production tolerances and assembly requirements. In addition, these actuators tend to comprise highly three-dimensional parts, which are incompatible with traditional microproduction technologies. This paper presents accurate production and novel assembly techniques for the development of a hydraulic microactuator. Some of the presented techniques are widespread in precision mechanics, but have not yet been introduced in micromechanics. A prototype hydraulic microactuator with a bore of 1 mm and a length of 13 mm has been fabricated and tested. Measurements showed that this actuator is able to generate a force density of more than 0.23 N mm-2 and a work density of 0.18 mJ mm-3 at a driving pressure of 550 kPa, which is remarkable considering the small dimensions of the actuator. © 2005 IOP Publishing Ltd.

Neural activity associated with the passive prediction of ambiguity and risk for aversive events.

In economic decision making, outcomes are described in terms of risk (uncertain outcomes with certain probabilities) and ambiguity (uncertain outcomes with uncertain probabilities). Humans are more averse to ambiguity than to risk, with a distinct neural system suggested as mediating this effect. However, there has been no clear disambiguation of activity related to decisions themselves from perceptual processing of ambiguity. In a functional magnetic resonance imaging (fMRI) experiment, we contrasted ambiguity, defined as a lack of information about outcome probabilities, to risk, where outcome probabilities are known, or ignorance, where outcomes are completely unknown and unknowable. We modified previously learned pavlovian CS+ stimuli such that they became an ambiguous cue and contrasted evoked brain activity both with an unmodified predictive CS+ (risky cue), and a cue that conveyed no information about outcome probabilities (ignorance cue). Compared with risk, ambiguous cues elicited activity in posterior inferior frontal gyrus and posterior parietal cortex during outcome anticipation. Furthermore, a similar set of regions was activated when ambiguous cues were compared with ignorance cues. Thus, regions previously shown to be engaged by decisions about ambiguous rewarding outcomes are also engaged by ambiguous outcome prediction in the context of aversive outcomes. Moreover, activation in these regions was seen even when no actual decision is made. Our findings suggest that these regions subserve a general function of contextual analysis when search for hidden information during outcome anticipation is both necessary and meaningful.

Modulation of pain ratings by expectation and uncertainty: Behavioral characteristics and anticipatory neural correlates.

Expectations about the magnitude of impending pain exert a substantial effect on subsequent perception. However, the neural mechanisms that underlie the predictive processes that modulate pain are poorly understood. In a combined behavioral and high-density electrophysiological study we measured anticipatory neural responses to heat stimuli to determine how predictions of pain intensity, and certainty about those predictions, modulate brain activity and subjective pain ratings. Prior to receiving randomized laser heat stimuli at different intensities (low, medium or high) subjects (n=15) viewed cues that either accurately informed them of forthcoming intensity (certain expectation) or not (uncertain expectation). Pain ratings were biased towards prior expectations of either high or low intensity. Anticipatory neural responses increased with expectations of painful vs. non-painful heat intensity, suggesting the presence of neural responses that represent predicted heat stimulus intensity. These anticipatory responses also correlated with the amplitude of the Laser-Evoked Potential (LEP) response to painful stimuli when the intensity was predictable. Source analysis (LORETA) revealed that uncertainty about expected heat intensity involves an anticipatory cortical network commonly associated with attention (left dorsolateral prefrontal, posterior cingulate and bilateral inferior parietal cortices). Relative certainty, however, involves cortical areas previously associated with semantic and prospective memory (left inferior frontal and inferior temporal cortex, and right anterior prefrontal cortex). This suggests that biasing of pain reports and LEPs by expectation involves temporally precise activity in specific cortical networks.

Context-dependent human extinction memory is mediated by a ventromedial prefrontal and hippocampal network.

In fear extinction, an animal learns that a conditioned stimulus (CS) no longer predicts a noxious stimulus [unconditioned stimulus (UCS)] to which it had previously been associated, leading to inhibition of the conditioned response (CR). Extinction creates a new CS-noUCS memory trace, competing with the initial fear (CS-UCS) memory. Recall of extinction memory and, hence, CR inhibition at later CS encounters is facilitated by contextual stimuli present during extinction training. In line with theoretical predictions derived from animal studies, we show that, after extinction, a CS-evoked engagement of human ventromedial prefrontal cortex (VMPFC) and hippocampus is context dependent, being expressed in an extinction, but not a conditioning, context. Likewise, a positive correlation between VMPFC and hippocampal activity is extinction context dependent. Thus, a VMPFC-hippocampal network provides for context-dependent recall of human extinction memory, consistent with a view that hippocampus confers context dependence on VMPFC.

Predictive neural coding of reward preference involves dissociable responses in human ventral midbrain and ventral striatum.

Food preferences are acquired through experience and can exert strong influence on choice behavior. In order to choose which food to consume, it is necessary to maintain a predictive representation of the subjective value of the associated food stimulus. Here, we explore the neural mechanisms by which such predictive representations are learned through classical conditioning. Human subjects were scanned using fMRI while learning associations between arbitrary visual stimuli and subsequent delivery of one of five different food flavors. Using a temporal difference algorithm to model learning, we found predictive responses in the ventral midbrain and a part of ventral striatum (ventral putamen) that were related directly to subjects' actual behavioral preferences. These brain structures demonstrated divergent response profiles, with the ventral midbrain showing a linear response profile with preference, and the ventral striatum a bivalent response. These results provide insight into the neural mechanisms underlying human preference behavior.

Temporal difference models describe higher-order learning in humans.

The ability to use environmental stimuli to predict impending harm is critical for survival. Such predictions should be available as early as they are reliable. In pavlovian conditioning, chains of successively earlier predictors are studied in terms of higher-order relationships, and have inspired computational theories such as temporal difference learning. However, there is at present no adequate neurobiological account of how this learning occurs. Here, in a functional magnetic resonance imaging (fMRI) study of higher-order aversive conditioning, we describe a key computational strategy that humans use to learn predictions about pain. We show that neural activity in the ventral striatum and the anterior insula displays a marked correspondence to the signals for sequential learning predicted by temporal difference models. This result reveals a flexible aversive learning process ideally suited to the changing and uncertain nature of real-world environments. Taken with existing data on reward learning, our results suggest a critical role for the ventral striatum in integrating complex appetitive and aversive predictions to coordinate behaviour.

Composite electrospun gelatin fiber-alginate gel scaffolds for mechanically robust tissue engineered cornea

A severe shortage of good quality donor cornea is now an international crisis in public health. Alternatives for donor tissue need to be urgently developed to meet the increasing demand for corneal transplantation. Hydrogels have been widely used as scaffolds for corneal tissue regeneration due to their large water content, similar to that of native tissue. However, these hydrogel scaffolds lack the fibrous structure that functions as a load-bearing component in the native tissue, resulting in poor mechanical performance. This work shows that mechanical properties of compliant hydrogels can be substantially enhanced with electrospun nanofiber reinforcement. Electrospun gelatin nanofibers were infiltrated with alginate hydrogels, yielding transparent fiber-reinforced hydrogels. Without prior crosslinking, electrospun gelatin nanofibers improved the tensile elastic modulus of the hydrogels from 78±19. kPa to 450±100. kPa. Stiffer hydrogels, with elastic modulus of 820±210. kPa, were obtained by crosslinking the gelatin fibers with carbodiimide hydrochloride in ethanol before the infiltration process, but at the expense of transparency. The developed fiber-reinforced hydrogels show great promise as mechanically robust scaffolds for corneal tissue engineering applications. © 2013 Elsevier Ltd.

Inhibitory plasticity balances excitation and inhibition in sensory pathways and memory networks

Cortical neurons receive balanced excitatory and inhibitory synaptic currents. Such a balance could be established and maintained in an experience-dependent manner by synaptic plasticity at inhibitory synapses. We show that this mechanism provides an explanation for the sparse firing patterns observed in response to natural stimuli and fits well with a recently observed interaction of excitatory and inhibitory receptive field plasticity. The introduction of inhibitory plasticity in suitable recurrent networks provides a homeostatic mechanism that leads to asynchronous irregular network states. Further, it can accommodate synaptic memories with activity patterns that become indiscernible from the background state but can be reactivated by external stimuli. Our results suggest an essential role of inhibitory plasticity in the formation and maintenance of functional cortical circuitry.

A theory of slow feature analysis for transformation-based input signals with an application to complex cells

We develop a group-theoretical analysis of slow feature analysis for the case where the input data are generated by applying a set of continuous transformations to static templates. As an application of the theory, we analytically derive nonlinear visual receptive fields and show that their optimal stimuli, as well as the orientation and frequency tuning, are in good agreement with previous simulations of complex cells in primary visual cortex (Berkes and Wiskott, 2005). The theory suggests that side and end stopping can be interpreted as a weak breaking of translation invariance. Direction selectivity is also discussed. © 2011 Massachusetts Institute of Technology.

Functional requirements for reward-modulated spike-timing-dependent plasticity.

Recent experiments have shown that spike-timing-dependent plasticity is influenced by neuromodulation. We derive theoretical conditions for successful learning of reward-related behavior for a large class of learning rules where Hebbian synaptic plasticity is conditioned on a global modulatory factor signaling reward. We show that all learning rules in this class can be separated into a term that captures the covariance of neuronal firing and reward and a second term that presents the influence of unsupervised learning. The unsupervised term, which is, in general, detrimental for reward-based learning, can be suppressed if the neuromodulatory signal encodes the difference between the reward and the expected reward-but only if the expected reward is calculated for each task and stimulus separately. If several tasks are to be learned simultaneously, the nervous system needs an internal critic that is able to predict the expected reward for arbitrary stimuli. We show that, with a critic, reward-modulated spike-timing-dependent plasticity is capable of learning motor trajectories with a temporal resolution of tens of milliseconds. The relation to temporal difference learning, the relevance of block-based learning paradigms, and the limitations of learning with a critic are discussed.

Slowness and sparseness lead to place, head-direction, and spatial-view cells.

We present a model for the self-organized formation of place cells, head-direction cells, and spatial-view cells in the hippocampal formation based on unsupervised learning on quasi-natural visual stimuli. The model comprises a hierarchy of Slow Feature Analysis (SFA) nodes, which were recently shown to reproduce many properties of complex cells in the early visual system []. The system extracts a distributed grid-like representation of position and orientation, which is transcoded into a localized place-field, head-direction, or view representation, by sparse coding. The type of cells that develops depends solely on the relevant input statistics, i.e., the movement pattern of the simulated animal. The numerical simulations are complemented by a mathematical analysis that allows us to accurately predict the output of the top SFA layer.