Hand gestures are a form of non-verbal communication that is used in social interaction and it is therefore required for more natural human-robot interaction. Neuromorphic (brain-inspired) computing offers a low-power solution for Spiking neural networks (SNNs) that can be used for the classification and recognition of gestures. This article introduces the preliminary results of a novel methodology for training spiking convolutional neural networks for hand-gesture recognition so that a humanoid robot with integrated neuromorphic hardware will be able to personalise the interaction with a user according to the shown hand gesture. It also describes other approaches that could improve the overall performance of the model.

The human brain is the most powerful and efficient machine in existence today, surpassing in many ways the capabilities of modern computers. Currently, lines of research in neuromorphic engineering are trying to develop hardware that mimics the functioning of the brain to acquire these superior capabilities. One of the areas still under development is the design of bio-inspired memories, where the hippocampus plays an important role. This region of the brain acts as a short-term memory with the ability to store associations of information from different sensory streams in the brain and recall them later. This is possible thanks to the recurrent collateral network architecture that constitutes CA3, the main sub-region of the hippocampus. In this work, we developed two spike-based computational models of fully functional hippocampal bio-inspired memories for the storage and recall of complex patterns implemented with spiking neural networks on the SpiNNaker hardware platform. These models present different levels of biological abstraction, with the first model having a constant oscillatory activity closer to the biological model, and the second one having an energy-efficient regulated activity, which, although it is still bio-inspired, opts for a more functional approach. Different experiments were performed for each of the models, in order to test their learning/recalling capabilities. A comprehensive comparison between the functionality and the biological plausibility of the presented models was carried out, showing their strengths and weaknesses. The two models, which are publicly available for researchers, could pave the way for future spike-based implementations and applications.

The increasing rise in machine learning and deep learning applications is requiring ever more computational resources to successfully meet the growing demands of an always-connected, automated world. Neuromorphic technologies based on Spiking Neural Network algorithms hold the promise to implement advanced artificial intelligence using a fraction of the computations and power requirements by modeling the functioning, and spiking, of the human brain. With the proliferation of tools and platforms aiding data scientists and machine learning engineers to develop the latest innovations in artificial and deep neural networks, a transition to a new paradigm will require building from the current well-established foundations. This paper explains the theoretical workings of neuromorphic technologies based on spikes, and overviews the state-of-art in hardware processors, software platforms and neuromorphic sensing devices. A progression path is paved for current machine learning specialists to update their skillset, as well as classification or predictive models from the current generation of deep neural networks to SNNs. This can be achieved by leveraging existing, specialized hardware in the form of SpiNNaker and the Nengo migration toolkit. First-hand, experimental results of converting a VGG-16 neural network to an SNN are shared. A forward gaze into industrial, medical and commercial applications that can readily benefit from SNNs wraps up this investigation into the neuromorphic computing future.

The open source Spinnaker is a continuous delivery tool originally developed by Netflix and Google, one that could be used to run a development pipeline for multiple cloud deployments. The software has found a home with the OpenStack community. Like OpenStack, Spinnaker streamlines and automates an inherently complex process of packaging resources in a heterogeneous environment. "In an ideal world, Spinnaker should live inside the OpenStack Foundation, because the approach that OpenStack has been solving problems in the infrastructure space is very similar to what Spinnaker does in the application delivery space" Boris Renski, co-founder of Mirantis, recently explained to us. Mirantis uses Spinnaker as a component of its recently launched-in-Beta commercially supported Mirantis Application Platform.

This year has seen some notable advancements in computer-based brain mimicry, not just on the artificial intelligence (AI) front, but also related to in silico brain simulations. Watson's vanquishing of Jeopardy champions Brad Rutter and Ken Jennings in February set the stage for the year. The now world-famous IBM super exhibited a sophisticated understanding of language semantics along with the ability to integrate that understanding into a complex analytics engine. Since the Jeopardy match, IBM has been looking to take the technology into the commercial realm, most notably in the health care arena. Meanwhile projects like FACETS (Fast Analog Computing with Emergent Transient States) and SpiNNaker are working to uncover the nature of the brain at the level of the neuron.

Neuromorphic computing – brain inspired computing – has long been a tantalizing goal. The human brain does with around 20 watts what supercomputers do with megawatts. While neuromorphic computing progress has been intriguing, it has still not proven very practical. This week neuromorphic computing takes another step forward with a workshop being offered to users from academia, industry and education interested in using two European neuromorphic systems that have been years in development and are coming online for broader use – the BrainScaleS system launching at the Kirchhoff Institute for Physics of Heidelberg University and SpiNNaker, a complementary approach and similarly sized system at the University of Manchester. Ramping up BrainScaleS and SpiNNaker is an important milestone, strengthening Europe's position in hardware development for alternative computing. Both projects are part of the European Human Brain Project, originally funded by the European Commission's Future Emerging Technologies program (2005-2015).