Raphaela Kreiser
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View article: Visual Pattern Recognition with on On-Chip Learning: Towards a Fully Neuromorphic Approach
Visual Pattern Recognition with on On-Chip Learning: Towards a Fully Neuromorphic Approach Open
We present a spiking neural network (SNN) for visual pattern recognition with on-chip learning on neuromorphichardware. We show how this network can learn simple visual patterns composed of horizontal and vertical bars sensed by a Dynamic …
View article: An On-chip Spiking Neural Network for Estimation of the Head Pose of the iCub Robot
An On-chip Spiking Neural Network for Estimation of the Head Pose of the iCub Robot Open
In this work, we present a neuromorphic architecture for head pose estimation and scene representation for the humanoid iCub robot. The spiking neuronal network is fully realized in Intel's neuromorphic research chip, Loihi, and precisely …
View article: A Digital Multiplier-less Neuromorphic Model for Learning a Context-Dependent Task
A Digital Multiplier-less Neuromorphic Model for Learning a Context-Dependent Task Open
Highly efficient performance-resources trade-off of the biological brain is a motivation for research on neuromorphic computing. Neuromorphic engineers develop event-based spiking neural networks (SNNs) in hardware. Learning in SNNs is a c…
View article: Neural State Machines for Robust Learning and Control of Neuromorphic Agents
Neural State Machines for Robust Learning and Control of Neuromorphic Agents Open
Mixed-signal analog/digital neuromorphic circuits are characterized by ultra-low power consumption, real-time processing abilities, and low-latency response times. These features make them promising for robotic applications that require fa…
View article: Digital Multiplier-less Event-Driven Spiking Neural Network Architecture for Learning a Context-Dependent Task
Digital Multiplier-less Event-Driven Spiking Neural Network Architecture for Learning a Context-Dependent Task Open
Neuromorphic engineers aim to develop event-based spiking neural networks (SNNs) in hardware. These SNNs closer resemble dynamics of biological neurons than todays' artificial neural networks and achieve higher efficiency thanks to the eve…
View article: Adaptive motor control and learning in a spiking neural network realised on a mixed-signal neuromorphic processor
Adaptive motor control and learning in a spiking neural network realised on a mixed-signal neuromorphic processor Open
Neuromorphic computing is a new paradigm for design of both the computing hardware and algorithms inspired by biological neural networks. The event-based nature and the inherent parallelism make neuromorphic computing a promising paradigm …
View article: Organizing Sequential Memory in a Neuromorphic Device Using Dynamic Neural Fields
Organizing Sequential Memory in a Neuromorphic Device Using Dynamic Neural Fields Open
Neuromorphic Very Large Scale Integration (VLSI) devices emulate the activation dynamics of biological neuronal networks using either mixed-signal analog/digital or purely digital electronic circuits. Using analog circuits in silicon to ph…
View article: Pose Estimation and Map Formation with Spiking Neural Networks: towards Neuromorphic SLAM
Pose Estimation and Map Formation with Spiking Neural Networks: towards Neuromorphic SLAM Open
In this paper, we investigate the use of ultra low-power, mixed signal analog/digital neuromorphic hardware for implementation of biologically inspired neuronal path integration and map formation for a mobile robot. We perform spiking netw…