620 research outputs found
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
Hardware design of LIF with Latency neuron model with memristive STDP synapses
In this paper, the hardware implementation of a neuromorphic system is
presented. This system is composed of a Leaky Integrate-and-Fire with Latency
(LIFL) neuron and a Spike-Timing Dependent Plasticity (STDP) synapse. LIFL
neuron model allows to encode more information than the common
Integrate-and-Fire models, typically considered for neuromorphic
implementations. In our system LIFL neuron is implemented using CMOS circuits
while memristor is used for the implementation of the STDP synapse. A
description of the entire circuit is provided. Finally, the capabilities of the
proposed architecture have been evaluated by simulating a motif composed of
three neurons and two synapses. The simulation results confirm the validity of
the proposed system and its suitability for the design of more complex spiking
neural network
The Brain on Low Power Architectures - Efficient Simulation of Cortical Slow Waves and Asynchronous States
Efficient brain simulation is a scientific grand challenge, a
parallel/distributed coding challenge and a source of requirements and
suggestions for future computing architectures. Indeed, the human brain
includes about 10^15 synapses and 10^11 neurons activated at a mean rate of
several Hz. Full brain simulation poses Exascale challenges even if simulated
at the highest abstraction level. The WaveScalES experiment in the Human Brain
Project (HBP) has the goal of matching experimental measures and simulations of
slow waves during deep-sleep and anesthesia and the transition to other brain
states. The focus is the development of dedicated large-scale
parallel/distributed simulation technologies. The ExaNeSt project designs an
ARM-based, low-power HPC architecture scalable to million of cores, developing
a dedicated scalable interconnect system, and SWA/AW simulations are included
among the driving benchmarks. At the joint between both projects is the INFN
proprietary Distributed and Plastic Spiking Neural Networks (DPSNN) simulation
engine. DPSNN can be configured to stress either the networking or the
computation features available on the execution platforms. The simulation
stresses the networking component when the neural net - composed by a
relatively low number of neurons, each one projecting thousands of synapses -
is distributed over a large number of hardware cores. When growing the number
of neurons per core, the computation starts to be the dominating component for
short range connections. This paper reports about preliminary performance
results obtained on an ARM-based HPC prototype developed in the framework of
the ExaNeSt project. Furthermore, a comparison is given of instantaneous power,
total energy consumption, execution time and energetic cost per synaptic event
of SWA/AW DPSNN simulations when executed on either ARM- or Intel-based server
platforms
A neuro-inspired system for online learning and recognition of parallel spike trains, based on spike latency and heterosynaptic STDP
Humans perform remarkably well in many cognitive tasks including pattern
recognition. However, the neuronal mechanisms underlying this process are not
well understood. Nevertheless, artificial neural networks, inspired in brain
circuits, have been designed and used to tackle spatio-temporal pattern
recognition tasks. In this paper we present a multineuronal spike pattern
detection structure able to autonomously implement online learning and
recognition of parallel spike sequences (i.e., sequences of pulses belonging to
different neurons/neural ensembles). The operating principle of this structure
is based on two spiking/synaptic neurocomputational characteristics: spike
latency, that enables neurons to fire spikes with a certain delay and
heterosynaptic plasticity, that allows the own regulation of synaptic weights.
From the perspective of the information representation, the structure allows
mapping a spatio-temporal stimulus into a multidimensional, temporal, feature
space. In this space, the parameter coordinate and the time at which a neuron
fires represent one specific feature. In this sense, each feature can be
considered to span a single temporal axis. We applied our proposed scheme to
experimental data obtained from a motor inhibitory cognitive task. The test
exhibits good classification performance, indicating the adequateness of our
approach. In addition to its effectiveness, its simplicity and low
computational cost suggest a large scale implementation for real time
recognition applications in several areas, such as brain computer interface,
personal biometrics authentication or early detection of diseases.Comment: Submitted to Frontiers in Neuroscienc
A Bio-Inspired Vision Sensor With Dual Operation and Readout Modes
This paper presents a novel event-based vision sensor with two operation modes: intensity mode and spatial contrast detection. They can be combined with two different readout approaches: pulse density modulation and time-to-first spike. The sensor is conceived to be a node of an smart camera network made up of several independent an autonomous nodes that send information to a central one. The user can toggle the operation and the readout modes with two control bits. The sensor has low latency (below 1 ms under average illumination conditions), low power consumption (19 mA), and reduced data flow, when detecting spatial contrast. A new approach to compute the spatial contrast based on inter-pixel event communication less prone to mismatch effects than diffusive networks is proposed. The sensor was fabricated in the standard AMS4M2P 0.35-um process. A detailed system-level description and experimental results are provided.Office of Naval Research (USA) N00014-14-1-0355Ministerio de Economía y Competitividad TEC2012- 38921-C02-02, P12-TIC-2338, IPT-2011-1625-43000
SIMPEL: Circuit model for photonic spike processing laser neurons
We propose an equivalent circuit model for photonic spike processing laser
neurons with an embedded saturable absorber---a simulation model for photonic
excitable lasers (SIMPEL). We show that by mapping the laser neuron rate
equations into a circuit model, SPICE analysis can be used as an efficient and
accurate engine for numerical calculations, capable of generalization to a
variety of different laser neuron types found in literature. The development of
this model parallels the Hodgkin--Huxley model of neuron biophysics, a circuit
framework which brought efficiency, modularity, and generalizability to the
study of neural dynamics. We employ the model to study various
signal-processing effects such as excitability with excitatory and inhibitory
pulses, binary all-or-nothing response, and bistable dynamics.Comment: 16 pages, 7 figure
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