ICONS

Legged robots operating in real-world environments must possess the ability to rapidly adapt to unexpected conditions, such as changing terrains and varying payloads. This paper introduces the Synaptic Motor Adaptation (SMA) algorithm, a novel ...

Neuromorphic processing promises high energy efficiency and rapid response rates, making it an ideal candidate for achieving autonomous flight of resource-constrained robots. It can be especially beneficial for complex neural networks as are used for ...

This work describes the investigation of neuromorphic computing-based spiking neural network (SNN) models used to filter data from sensor electronics in high energy physics experiments conducted at the High Luminosity Large Hadron Collider (HL-LHC). ...

Superconducting Optoelectronic Networks (SOENs) combine photonics and superconductors to instantiate computing systems that approach the fundamental limits of information processing in terms of speed and scalability. Overcoming the engineering ...

Rhythmic tasks that biological beings perform such as breathing, walking, and swimming, use specialized neural networks called central pattern generators (CPG). Spiking CPGs have already been implemented to control robot locomotion. This paper aims to ...

As commercial event cameras are maturing, we present a wearable demonstrator for assessment of headborne augmented vision applications. The demonstrator is an untethered testbed that allows the user to: 1- perceive event-based stereopsis with a head-...

In recent work, we have shown that neuromorphic (event-based) cameras are highly efficient at detecting quadcopters. This is done by directly detecting the frequency of the rotating blades. This signal is highly characteristic of quadcopters, in that ...

We present a method for computing egomotion using event cameras with a pre-trained optical flow spiking neural network (SNN). To address the aperture problem encountered in the sparse and noisy normal flow of the initial SNN layers, our method ...

The ever-increasing demands of computationally expensive and high-dimensional problems require novel optimization methods to find near-optimal solutions in a reasonable amount of time. Bayesian Optimization (BO) stands as one of the best methodologies ...

Event-based cameras and classification datasets pair nicely with neuromorphic computing. Furthermore, it is attractive from a SWaP perspective to have a fully neuromorphic pipeline from event-based camera output to classification instead of having to ...

Event-based cameras are cameras with high dynamic range that measure changes in light intensity at each pixel instead of capturing frames like traditional cameras. There are several event-based camera simulators for generating event-based datasets, ...

Dynamic Vision Sensors (DVS) are fully asynchronous leading to excellent temporal resolution (≈1μs) and high throughput (>1 Giga-events per second) but are difficult to scale for higher resolutions (>1MegaPixels) due to the complex pixel design and ...

This work reports on new results and insights from the optimization of spiking neural networks developed for gamma-ray radiation anomaly detection. Our previous paper introduced the first known neuromorphic algorithm for this application, ...

Neurons that fire multiple spikes on activation are commonly observed in biological systems, but the impact of their inclusion in neuromorphic systems has not been thoroughly analyzed. In this preliminary work, we begin an initial evaluation of the ...

Neuromorphic processors have garnered considerable interest in recent years for their potential in energy-efficient and high-speed computing. The Locally Competitive Algorithm (LCA) has been utilized for power efficient sparse coding on neuromorphic ...

With the emergence of neuromorphic hardware as a promising low-power parallel computing platform, the need for tools that allow researchers and engineers to efficiently interact with such hardware is rapidly growing. Machine learning frameworks like ...

Memory storage and retrieval are context-sensitive in both humans and animals; memories are more accurately retrieved in the context where they were acquired, and similar stimuli can elicit different responses in different contexts. Researchers have ...

Incremental learning is a challenging task in the field of machine learning, and it is a key step towards autonomous learning and adaptation. With the increasing attention to neuromorphic computing, there is an urgent need to investigate incremental ...

Recent work in neuromorphic computing has proposed a range of new architectures for Spiking Neural Network (SNN)-based systems. However, neuromorphic design lacks a framework to facilitate exploration of different SNN-based architectures and aid with ...

The need for energy-efficient solutions in Deep Neural Network (DNN) applications has led to a growing interest in Spiking Neural Networks (SNNs) implemented in neuromorphic hardware. The Burstprop algorithm enables online and local learning in ...

Spiking Graph Convolutional Networks (SGCNs) are an emerging class of neural networks that operate on graph-structured data and leverage the advantages of both spiking neural networks and GCNs, making them ideal for graph representation learning ...

A Vector Symbolic Architecture (VSA) is a powerful framework for representing compositional reasoning. It can be used to create neural networks that perform cognitive functions, like spatial reasoning, arithmetic, symbol binding, and logic. But the ...

Biological evidence suggests that adaptation of synaptic delays on short to medium timescales plays an important role in learning in the brain. Inspired by biology, we explore the feasibility and power of using synaptic delays to solve challenging ...

Agent-based modeling (ABM) and simulation have emerged as important tools for studying emergent behaviors, especially in the context of swarming algorithms for robotic systems. Despite significant research in this area, there is a lack of standardized ...

Continual learning without catastrophic forgetting of previous experiences is an open general challenge for artificial neural networks, but is especially under-explored for artificial neural networks suitable to implement on neuromorphic platforms. An ...

It has been well-established that within conventional neural networks, many of the values produced at each layer are zero. In this work, it is demonstrated that spiking neural networks can prevent the transmission of spikes representing values close ...

Biological neurons can detect complex spatio-temporal features in spiking patterns via their synapses spread across their dendritic branches. This is achieved by modulating the efficacy of the individual synapses, and by exploiting the temporal delays ...

The Spiking Neural Network Computer Architecture (SpiNNaker) is a massively parallel computing system. As one of the most widespread platforms in the emerging field of neuromorphic engineering, SpiNNaker targets three main areas of research: ...

Analog memristive devices have the potential to merge computing and memory, support local learning, reach high densities, enable 3D stacking, and low energy consumption for neuromorphic computing applications. Yet, integration is challenged by the ...

Neuromorphic computing has emerged as a promising solution to the power and memory requirements of embedded systems. However, mapping pre-trained neural networks onto diverse neuromorphic hardware architectures presents significant challenges. In this ...