756 research outputs found
Neuro-inspired system for real-time vision sensor tilt correction
Neuromorphic engineering tries to mimic biological
information processing. Address-Event-Representation (AER)
is an asynchronous protocol for transferring the information of
spiking neuro-inspired systems. Currently AER systems are able
sense visual and auditory stimulus, to process information, to
learn, to control robots, etc. In this paper we present an AER
based layer able to correct in real time the tilt of an AER vision
sensor, using a high speed algorithmic mapping layer. A codesign
platform (the AER-Robot platform), with a Xilinx
Spartan 3 FPGA and an 8051 USB microcontroller, has been
used to implement the system. Testing it with the help of the
USBAERmini2 board and the jAER software.Junta de Andalucía P06-TIC-01417Ministerio de Educación y Ciencia TEC2006-11730-C03-02Ministerio de Ciencia e Innovación TEC2009-10639-C04-0
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
Building Blocks for Spikes Signals Processing
Neuromorphic engineers study models and
implementations of systems that mimic neurons behavior in the
brain. Neuro-inspired systems commonly use spikes to
represent information. This representation has several
advantages: its robustness to noise thanks to repetition, its
continuous and analog information representation using digital
pulses, its capacity of pre-processing during transmission time,
... , Furthermore, spikes is an efficient way, found by nature, to
codify, transmit and process information. In this paper we
propose, design, and analyze neuro-inspired building blocks
that can perform spike-based analog filters used in signal
processing. We present a VHDL implementation for FPGA.
Presented building blocks take advantages of the spike rate
coded representation to perform a massively parallel processing
without complex hardware units, like floating point arithmetic
units, or a large memory. Those low requirements of hardware
allow the integration of a high number of blocks inside a FPGA,
allowing to process fully in parallel several spikes coded signals.Junta de Andalucía P06-TIC-O1417Ministerio de Ciencia e Innovación TEC2009-10639-C04-02Ministerio de Ciencia e Innovación TEC2006-11730-C03-0
A Survey of Brain Inspired Technologies for Engineering
Cognitive engineering is a multi-disciplinary field and hence it is difficult
to find a review article consolidating the leading developments in the field.
The in-credible pace at which technology is advancing pushes the boundaries of
what is achievable in cognitive engineering. There are also differing
approaches to cognitive engineering brought about from the multi-disciplinary
nature of the field and the vastness of possible applications. Thus research
communities require more frequent reviews to keep up to date with the latest
trends. In this paper we shall dis-cuss some of the approaches to cognitive
engineering holistically to clarify the reasoning behind the different
approaches and to highlight their strengths and weaknesses. We shall then show
how developments from seemingly disjointed views could be integrated to achieve
the same goal of creating cognitive machines. By reviewing the major
contributions in the different fields and showing the potential for a combined
approach, this work intends to assist the research community in devising more
unified methods and techniques for developing cognitive machines
Event-based Vision: A Survey
Event cameras are bio-inspired sensors that differ from conventional frame
cameras: Instead of capturing images at a fixed rate, they asynchronously
measure per-pixel brightness changes, and output a stream of events that encode
the time, location and sign of the brightness changes. Event cameras offer
attractive properties compared to traditional cameras: high temporal resolution
(in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low
power consumption, and high pixel bandwidth (on the order of kHz) resulting in
reduced motion blur. Hence, event cameras have a large potential for robotics
and computer vision in challenging scenarios for traditional cameras, such as
low-latency, high speed, and high dynamic range. However, novel methods are
required to process the unconventional output of these sensors in order to
unlock their potential. This paper provides a comprehensive overview of the
emerging field of event-based vision, with a focus on the applications and the
algorithms developed to unlock the outstanding properties of event cameras. We
present event cameras from their working principle, the actual sensors that are
available and the tasks that they have been used for, from low-level vision
(feature detection and tracking, optic flow, etc.) to high-level vision
(reconstruction, segmentation, recognition). We also discuss the techniques
developed to process events, including learning-based techniques, as well as
specialized processors for these novel sensors, such as spiking neural
networks. Additionally, we highlight the challenges that remain to be tackled
and the opportunities that lie ahead in the search for a more efficient,
bio-inspired way for machines to perceive and interact with the world
Memory and information processing in neuromorphic systems
A striking difference between brain-inspired neuromorphic processors and
current von Neumann processors architectures is the way in which memory and
processing is organized. As Information and Communication Technologies continue
to address the need for increased computational power through the increase of
cores within a digital processor, neuromorphic engineers and scientists can
complement this need by building processor architectures where memory is
distributed with the processing. In this paper we present a survey of
brain-inspired processor architectures that support models of cortical networks
and deep neural networks. These architectures range from serial clocked
implementations of multi-neuron systems to massively parallel asynchronous ones
and from purely digital systems to mixed analog/digital systems which implement
more biological-like models of neurons and synapses together with a suite of
adaptation and learning mechanisms analogous to the ones found in biological
nervous systems. We describe the advantages of the different approaches being
pursued and present the challenges that need to be addressed for building
artificial neural processing systems that can display the richness of behaviors
seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed
neuromorphic computing platforms and system
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