The brain is the perfect place to look for inspiration to develop more efficient neural networks. The inner workings of our synapses and neurons provide a glimpse at what the future of deep learning might look like. This article serves as a tutorial and perspective showing how to apply the lessons learned from several decades of research in deep learning, gradient descent, backpropagation, and neuroscience to biologically plausible spiking neural networks (SNNs). We also explore the delicate interplay between encoding data as spikes and the learning process; the challenges and solutions of applying gradient-based learning to SNNs; the subtle link between temporal backpropagation and spike timing-dependent plasticity; and how deep learning might move toward biologically plausible online learning. Some ideas are well accepted and commonly used among the neuromorphic engineering community, while others are presented or justified for the first time here. A series of companion interactive tutorials complementary to this article using our Python package,snnTorch, are also made available: https://snntorch.readthedocs.io/en/latest/tutorials/index.html.

We propose a simple and efficient clustering method for high-dimensional data with a large number of clusters. Our algorithm achieves high-performance by evaluating distances of datapoints with a subset of the cluster centres. Our contribution is substantially more efficient than k-means as it does not require an all to all comparison of data points and clusters. We show that the optimal solutions of our approximation are the same as in the exact solution. However, our approach is considerably more efficient at extracting these clusters compared to the state-of-the-art. We compare our approximation with the exact k-means and alternative approximation approaches on a series of standardised clustering tasks. For the evaluation, we consider the algorithmic complexity, including number of operations to convergence, and the stability of the results. An efficient implementation of the algorithm is available online.

Neuromorphic systems provide an alternative to conventional computing hardware, promising low-power operation suitable for sensory-processing and edge computing. In this paper, we present a mixed-signal processing system designed to provide on-sensor classification of signals obtained from multi-electrode array neural recordings. The designed circuits implement a real-time spike sorting algorithm, and operate on signals represented by asynchronous event streams. We combine analog circuits computation primitives (temporal surface generation, distance computation, winner-take-all) to implement a spatio-temporal clustering algorithm, classifying signals acquired by neighbouring electrodes. The prototype chip has been submitted for fabrication in a 180nm CMOS technology. The circuits are designed to fit, alongside signal conditioning and conversion circuits, in the area under the recording electrodes (below 80×80um per electrode). Circuit implementation details and simulation results are presented. The expected neural spike recognition rates of 75% in a single-layer network and 88% in a 2-layer network are comparable with a software implementation, while the system is designed to provide a low-power embedded real-time solution. This work provides a foundation towards the design of a large scale neuromorphic processing system, to be embedded in brain-machine interfaces.

Gesture recognition is an area of research that is expanding as it would allow facilitated interaction with modern handheld devices for the naive user. Currently, smart-phones and tablets are not commonly used by some groups such as the visually-impaired and the elderly, because they require fine motor control and a good visual accuracy. Gesture recognition plays a key role in having these user categories to interact with IT devices.