Smartglass-guided exposure for anxiety disorders: A proof-of-concept study

Exposure is a highly effective treatment for pathological fear and anxiety, but rarely used in routine care. Issues of practicability and lack of therapists in rural areas are main barriers for the dissemination of exposure. Smartglass devices may enable therapists to guide exposure from their own office via real-time feedback and may thereby increase practicability. The present study explored the technological usability and clinical feasibility of Smartglass-guided exposure in a behavioral approach test in spider fearful individuals (N=40). Fearful individuals were asked to start the Smartglass themselves and established a connection to a therapist, who guided the exposure test from afar. Clinical severity of spider fear was assessed via questionnaire. Technological usability was assessed with established measures of usability, user experience, and user acceptance. Exploratory, individual characteristics of exposure were collected (e.g., within-session fear reduction, duration, safety behavior). Overall, fearful individuals (94.9%) and therapists (100%) were able to establish a connection. Usability of Smartglass-guided exposure was evaluated as positive. Within-session fear reduction was large (d=1.91). Adverse events were minimal. There were, however, some associations between exposure characteristics and usability evaluation (e.g., lower userfriendliness and longer exposure duration). Two case examples further highlight chances and risks of Smartglass-guided exposure. These findings provide first evidence that Smartglass-guided exposure could be useful in exposure therapy. Smartglass-guided exposure may ultimately help to increase practicability of exposure and increase dissemination, also in rural areas. These findings are promising for future research on the long-term outcome of evidence-based exposure in treatment seeking patients

Configurable analog-digital conversion using the neural engineering framework

Efficient Analog-Digital Converters (ADC) are one of the mainstays of mixed-signal integrated circuit design. Besides the conventional ADCs used in mainstream ICs, there have been various attempts in the past to utilize neuromorphic networks to accomplish an efficient crossing between analog and digital domains, i.e., to build neurally inspired ADCs. Generally, these have suffered from the same problems as conventional ADCs, that is they require high-precision, handcrafted analog circuits and are thus not technology portable. In this paper, we present an ADC based on the Neural Engineering Framework (NEF). It carries out a large fraction of the overall ADC process in the digital domain, i.e., it is easily portable across technologies. The analog-digital conversion takes full advantage of the high degree of parallelism inherent in neuromorphic networks, making for a very scalable ADC. In addition, it has a number of features not commonly found in conventional ADCs, such as a runtime reconfigurability of the ADC sampling rate, resolution and transfer characteristic.ISSN:1662-453XISSN:1662-454

Complete Conceptual Schema Algebras

A schema algebra comprises operations on database schemata for a given data model. Such algebras are useful in database design as well as in schema integration. In this article we address the necessary theoretical underpinnings by introducing a novel not

Switched-capacitor realization of presynaptic short-term-plasticity and stop-learning synapses in 28 nm CMOS

Synaptic dynamics, such as long- and short-term plasticity, play an important role in the complexity and biological realism achievable when running neural networks on a neuromorphic IC. For example, they endow the IC with an ability to adapt and learn from its environment. In order to achieve the millisecond to second time constants required for these synaptic dynamics, analog subthreshold circuits are usually employed. However, due to process variation and leakage problems, it is almost impossible to port these types of circuits to modern sub-100nm technologies. In contrast, we present a neuromorphic system in a 28 nm CMOS process that employs switched capacitor (SC) circuits to implement 128 short term plasticity presynapses as well as 8192 stop-learning synapses. The neuromorphic system consumes an area of 0.36 mm(2) and runs at a power consumption of 1.9 mW. The circuit makes use of a technique for minimizing leakage effects allowing for real-time operation with time constants up to several seconds. Since we rely on SC techniques for all calculations, the system is composed of only generic mixed-signal building blocks. These generic building blocks make the system easy to port between technologies and the large digital circuit part inherent in an SC system benefits fully from technology scaling