NICE

Synaptic plasticity poses itself as a powerful method of self-regulated unsupervised learning in neural networks. A recent resurgence of interest has developed in utilizing Artificial Neural Networks (ANNs) together with synaptic plasticity for intra-...

Taking inspiration from machine learning libraries – where techniques such as parallel batch training minimise latency and maximise GPU occupancy – as well as our previous research on efficiently simulating Spiking Neural Networks (SNNs) on GPUs for ...

Hearing can be considered as one of the most important senses since it plays a key role in the audiovisual learning process. While a lot of effort has been made for achieving good results from the traditional approach of auditory perception, new trends ...

The increasing amount of data to be processed on edge devices, such as cameras, has motivated Artificial Intelligence (AI) integration at the edge. Typical image processing methods performed at the edge, such as feature extraction or edge detection, ...

Spiking neural network (SNN) are emerging as a bio-plausible AI paradigm best suited for energy constrained edge use case. However the performance of SNNs largely depends upon the information content of the spike trains generated from real valued data ...

Online learning in neural networks has the potential to transform AI research. By enabling new information to be assimilated into existing systems, platforms can be adaptive to unseen data and can personalise performance to an individual. A common ...

Neuromorphic computing (NMC) is an exciting paradigm seeking to incorporate principles from biological brains to enable advanced computing capabilities. Not only does this encompass algorithms, such as neural networks, but also the consideration of how ...

The development of upper limb prosthesis that are able to relay information on their status back to the user is an important step towards making this assistive technology more intuitive. Applied within this context, neuromorphic hardware has the ...

Instabilities in neuromorphic machine learning can occur when synaptic updates meant to encode matrix transforms are not normalized. This phenomenon is encountered in Hebbian learning [5], where, as a synapse’s strength grows, post-synaptic activity ...

It has been demonstrated that grid cells are encoding physical locations using hexagonally spaced, periodic phase-space representations. Theories of how the brain is decoding this phase-space representation have been developed based on neuroscience ...

In this work we develop a model of predictive learning on neuromorphic hardware. Our model uses the on-chip plasticity capabilities of the Loihi chip to remember observed sequences of events and use this memory to generate predictions of future events ...

In this paper, we present an approach to integer factorization using distributed representations formed with Vector Symbolic Architectures. The approach formulates integer factorization in a manner such that it can be solved using neural networks and ...

Associative memory [4, 11, 15] is an important building block in neural computing, neuromorphic engineering, and, in general, collective-state computing. Based on phasor associative memories (PAM) [9], a type of phasor neural network (PNN), we present ...

The fundamental advantage of neuromorphic systems is their low power consumption, which emerges from their event-based computation implemented via spikes. However, we do not have a theory that explores the fundamental limits of the energy consumption ...

Neuromorphic hardware equipped with learning capabilities can adapt to new, real-time data. While models of Spiking Neural Networks (SNNs) can now be trained using gradient descent to reach an accuracy comparable to equivalent conventional neural ...

The BrainScaleS-2 (BSS-2) Neuromorphic Computing System currently consists of multiple single-chip setups, which are connected to a compute cluster via Gigabit-Ethernet network technology. This is convenient for small experiments, where the neural ...

We present a new data processing pipeline for automatically labelled stereo vision data. The method is based on 3D positioning of 3D printed props with active LED markers. It provides both RGB frame and event-based data, automatically annotated with ...

Emergent phenomena in condensed matter physics, such as superconductivity, are rooted in the interaction of many quantum particles. These phenomena remain poorly understood in part due to the computational demands of their simulation. In recent years ...

The Yin-Yang dataset was developed for research on biologically plausible error backpropagation and deep learning in spiking neural networks. It serves as an alternative to classic deep learning datasets, especially in early-stage prototyping scenarios ...

Vector space models for symbolic processing that encode symbols by random vectors have been proposed in cognitive science and connectionist communities under the names Vector Symbolic Architecture (VSA), and, synonymously, Hyperdimensional (HD) ...