Spiking Neural Networks (SNN) for Versatile Applications (SNAVA) simulation platform is a scalable and programmable parallel architecture that supports real-time, large-scale, multi-model SNN computation. This parallel architecture is implemented in modern Field-Programmable Gate Arrays (FPGAs) devices to provide high performance execution and flexibility to support large-scale SNN models. Flexibility is defined in terms of programmability, which allows easy synapse and neuron implementation. This has been achieved by using a special-purpose Processing Elements (PEs) for computing SNNs, and analyzing and customizing the instruction set according to the processing needs to achieve maximum performance with minimum resources. The parallel architecture is interfaced with customized Graphical User Interfaces (GUIs) to configure the SNN's connectivity, to compile the neuron-synapse model and to monitor SNN's activity. Our contribution intends to provide a tool that allows to prototype SNNs faster than on CPU/GPU architectures but significantly cheaper than fabricating a customized neuromorphic chip. This could be potentially valuable to the computational neuroscience and neuromorphic engineering communities.

Tactile rendering has been implemented in digital musical instruments (DMIs) to offer the musician haptic feedback that enhances his/her music playing experience. Recently, this implementation has expanded to the development of sensory substitution systems known as haptic music players (HMPs) to give the opportunity of experiencing music through touch to the hearing impaired. These devices may also be conceived as vibrotactile music players to enrich music listening activities. In this review, technology and methods to render musical information by means of vibrotactile stimuli are systematically studied. The methodology used to find out relevant literature is first outlined, and a preliminary classification of musical haptics is proposed. A comparison between different technologies and methods for vibrotactile rendering is performed to later organize the information according to the type of HMP. Limitations and advantages are highlighted to find out opportunities for future research. Likewise, methods for music audio-tactile rendering (ATR) are analyzed and, finally, strategies to compose for the sense of touch are summarized. This review is intended for researchers in the fields of haptics, assistive technologies, music, psychology, and human–computer interaction as well as artists that may make use of it as a reference to develop upcoming research on HMPs and ATR.

Given the massive number of interconnects in Spiking Neural Networks (SNNs), distributing spikes effciently becomes a critical issue for the efficient hardware emulation of large-scale SNNs. In this work, the AER-SRT (Address Event Representation over Synchronous Serial Ring Topology) architecture for spike transmission is proposed. AER-SRT is a light, easily scalable, packet-based solution implemented with high-speed serial link for multi-chip SNN communication. The channel uses a unidirectional, point-to-point connection between nodes, which provides a high transmission speed. Events (spikes) are distributed among all the nodes in a ring-topology pipeline fashion and the synchronous AER guarantees a collision-free scheme. The fast speed and efficient channel usage limits the spike distribution time to values that allow real-time operation for network sizes that can be calculated with simple design equations. Also, in the proposed communication protocol there is no specific or master node, so new nodes can be added to the ring by simply modifying two configuration parameters. As a proof of concept, a prototype of the architecture has been implemented and tested on FPGA development boards.

Interest in chatbot development is on the rise. As a usability evaluation is an essential step in chatbot development, the number of experimental studies on chatbot usability has grown as well. As a result, we think a systematic mapping study is opportune. We analyzed more than 700 sources and retrieved 28 primary studies. By aggregating the research questions and examining the characteristics and metrics used to evaluate the usability of chatbots in experiments, it is possible to identify the state of the art in chatbot usability experimentation. We conducted a systematic mapping study to identify the research questions, characteristics, and metrics used to evaluate the usability of chatbots in experiments. Most experiments adopted a within-subjects design. On the other hand, few experiments provided raw data, and only one of the identified papers was part of a family of experiments. Effectiveness, efficiency, and satisfaction are usability characteristics used to identify how well users can learn and use chatbots to achieve their goals and how satisfied users are during the interaction. Generally, the experimental results revealed that chatbots have several advantages (e.g., they provide a real-time response and they improve ease of use) and some shortcomings (e.g., natural language processing, which is rated as the weakness most in need of improvement). This research offers an overview of chatbot usability experimentation. The increasing interest in this area is very recent, as works did not start to be published until 2018. Chatbot usability experiments should be more replicable to improve the reliability and transparency of the experimental results.INDEX TERMS Usability, chatbots, experiments, family of experiments, systematic mapping study

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