Adaptive motor control and learning in a spiking neural network realised on a mixed-signal neuromorphic processor

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 for building efficient neural network based architectures for control of fast and agile robots. In this paper, we present a spiking neural network architecture that uses sensory feedback to control rotational velocity of a robotic vehicle. When the velocity reaches the target value, the mapping from the target velocity of the vehicle to the correct motor command, both represented in the spiking neural network on the neuromorphic device, is autonomously stored on the device using on-chip plastic synaptic weights. We validate the controller using a wheel motor of a miniature mobile vehicle and inertia measurement unit as the sensory feedback and demonstrate online learning of a simple 'inverse model' in a two-layer spiking neural network on the neuromorphic chip. The prototype neuromorphic device that features 256 spiking neurons allows us to realise a simple proof of concept architecture for the purely neuromorphic motor control and learning. The architecture can be easily scaled-up if a larger neuromorphic device is available.Comment: 6+1 pages, 4 figures, will appear in one of the Robotics conference

Pose Estimation and Map Formation with Spiking Neural Networks: towards Neuromorphic SLAM

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 network simulations of the developed architecture, interfaced to a simulated robotic vehicle. We then port the neuronal map formation architecture on two connected neuromorphic devices, one of which features on-board plasticity, and demonstrate the feasibility of a neuromorphic realization of simultaneous localization and mapping (SLAM)

Organizing Sequential Memory in a Neuromorphic Device Using Dynamic Neural Fields

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 physically emulate the functionality of biological neurons and synapses enables faithful modeling of neural and synaptic dynamics at ultra low power consumption in real-time, and thus may serve as computational substrate for a new generation of efficient neural controllers for artificial intelligent systems. Although one of the main advantages of neural networks is their ability to perform on-line learning, only a small number of neuromorphic hardware devices implement this feature on-chip. In this work, we use a reconfigurable on-line learning spiking (ROLLS) neuromorphic processor chip to build a neuronal architecture for sequence learning. The proposed neuronal architecture uses the attractor properties of winner-takes-all (WTA) dynamics to cope with mismatch and noise in the ROLLS analog computing elements, and it uses its on-chip plasticity features to store sequences of states. We demonstrate, with a proof-of-concept feasibility study how this architecture can store, replay, and update sequences of states, induced by external inputs. Controlled by the attractor dynamics and an explicit destabilizing signal, the items in a sequence can last for varying amounts of time and thus reliable sequence learning and replay can be robustly implemented in a real sensorimotor system

An On-chip Spiking Neural Network for Estimation of the Head Pose of the iCub Robot

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 integrates the issued motor commands to estimate the iCub's head pose in a neuronal path-integration process. The neuromorphic vision system of the iCub is used to correct for drift in the pose estimation. Positions of objects in front of the robot are memorized using on-chip synaptic plasticity. We present real-time robotic experiments using 2 degrees of freedom (DoF) of the robot's head and show precise path integration, visual reset, and object position learning on-chip. We discuss the requirements for integrating the robotic system and neuromorphic hardware with current technologies

A Digital Multiplier-less Neuromorphic Model for Learning a Context-Dependent Task

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 challenging topic of current research. Reinforcement learning (RL) is a particularly promising learning paradigm, important for developing autonomous agents. In this paper, we propose a digital multiplier-less hardware implementation of an SNN with RL capability. The network is able to learn stimulus-response associations in a context-dependent learning task. Validated in a robotic experiment, the proposed model replicates the behavior in animal experiments and the respective computational model