76 research outputs found
The importance of space and time in neuromorphic cognitive agents
Artificial neural networks and computational neuroscience models have made
tremendous progress, allowing computers to achieve impressive results in
artificial intelligence (AI) applications, such as image recognition, natural
language processing, or autonomous driving. Despite this remarkable progress,
biological neural systems consume orders of magnitude less energy than today's
artificial neural networks and are much more agile and adaptive. This
efficiency and adaptivity gap is partially explained by the computing substrate
of biological neural processing systems that is fundamentally different from
the way today's computers are built. Biological systems use in-memory computing
elements operating in a massively parallel way rather than time-multiplexed
computing units that are reused in a sequential fashion. Moreover, activity of
biological neurons follows continuous-time dynamics in real, physical time,
instead of operating on discrete temporal cycles abstracted away from
real-time. Here, we present neuromorphic processing devices that emulate the
biological style of processing by using parallel instances of mixed-signal
analog/digital circuits that operate in real time. We argue that this approach
brings significant advantages in efficiency of computation. We show examples of
embodied neuromorphic agents that use such devices to interact with the
environment and exhibit autonomous learning
Autonomous Reinforcement of Behavioral Sequences in Neural Dynamics
We introduce a dynamic neural algorithm called Dynamic Neural (DN)
SARSA(\lambda) for learning a behavioral sequence from delayed reward.
DN-SARSA(\lambda) combines Dynamic Field Theory models of behavioral sequence
representation, classical reinforcement learning, and a computational
neuroscience model of working memory, called Item and Order working memory,
which serves as an eligibility trace. DN-SARSA(\lambda) is implemented on both
a simulated and real robot that must learn a specific rewarding sequence of
elementary behaviors from exploration. Results show DN-SARSA(\lambda) performs
on the level of the discrete SARSA(\lambda), validating the feasibility of
general reinforcement learning without compromising neural dynamics.Comment: Sohrob Kazerounian, Matthew Luciw are Joint first author
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
Event-driven Vision and Control for UAVs on a Neuromorphic Chip
Event-based vision sensors achieve up to three orders of magnitude better speed vs. power consumption trade off in high-speed control of UAVs compared to conventional image sensors. Event-based cameras produce a sparse stream of events that can be processed more efficiently and with a lower latency than images, enabling ultra-fast vision-driven control. Here, we explore how an event-based vision algorithm can be implemented as a spiking neuronal network on a neuromorphic chip and used in a drone controller. We show how seamless integration of event-based perception on chip leads to even faster control rates and lower latency. In addition, we demonstrate how online adaptation of the SNN controller can be realised using on-chip learning. Our spiking neuronal network on chip is the first example of a neuromorphic vision-based controller on chip solving a high-speed UAV control task. The excellent scalability of processing in neuromorphic hardware opens the possibility to solve more challenging visual tasks in the future and integrate visual perception in fast control loops
A neuromorphic controller for a robotic vehicle equipped with a dynamic vision sensor
Neuromorphic electronic systems exhibit advantageous characteristics, in terms of low energy consumption and low response latency, which can be useful in robotic applications that require compact and low power embedded computing resources. However, these neuromorphic circuits still face significant limitations that make their usage challenging: these include low precision, variability of components, sensitivity to noise and temperature drifts, as well as the currently limited number of neurons and synapses that are typically emulated on a single chip. In this paper, we show how it is possible to achieve functional robot control strategies using a mixed signal analog/digital neuromorphic processor interfaced to a mobile robotic platform equipped with an event-based dynamic vision sensor. We provide a proof of concept implementation of obstacle avoidance and target acquisition using biologically plausible spiking neural networks directly emulated by the neuromorphic hardware. To our knowledge, this is the first demonstration of a working spike-based neuromorphic robotic controller in this type of hardware which illustrates the feasibility, as well as limitations, of this approach
A neuromorphic controller for a robotic vehicle equipped with a dynamic vision sensor
Neuromorphic electronic systems exhibit advantageous characteristics, in terms of low energy consumption and low response latency, which can be useful in robotic applications that require compact and low power embedded computing resources. However, these neuromorphic circuits still face significant limitations that make their usage challenging: these include low precision, variability of components, sensitivity to noise and temperature drifts, as well as the currently limited number of neurons and synapses that are typically emulated on a single chip. In this paper, we show how it is possible to achieve functional robot control strategies using a mixed signal analog/digital neuromorphic processor interfaced to a mobile robotic platform equipped with an event-based dynamic vision sensor. We provide a proof of concept implementation of obstacle avoidance and target acquisition using biologically plausible spiking neural networks directly emulated by the neuromorphic hardware. To our knowledge, this is the first demonstration of a working spike-based neuromorphic robotic controller in this type of hardware which illustrates the feasibility, as well as limitations, of this approach
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