75 research outputs found
An AER Spike-Processing Filter Simulator and Automatic VHDL Generator Based on Cellular Automata
Spike-based systems are neuro-inspired circuits implementations
traditionally used for sensory systems or sensor signal processing. Address-Event-
Representation (AER) is a neuromorphic communication protocol for transferring
asynchronous events between VLSI spike-based chips. These neuro-inspired
implementations allow developing complex, multilayer, multichip neuromorphic
systems and have been used to design sensor chips, such as retinas and cochlea,
processing chips, e.g. filters, and learning chips. Furthermore, Cellular Automata
(CA) is a bio-inspired processing model for problem solving. This approach
divides the processing synchronous cells which change their states at the same time
in order to get the solution. This paper presents a software simulator able to gather
several spike-based elements into the same workspace in order to test a CA
architecture based on AER before a hardware implementation. Furthermore this
simulator produces VHDL for testing the AER-CA into the FPGA of the USBAER
AER-tool.Ministerio de Ciencia e Innovación TEC2009-10639-C04-0
Stochastic Receptive Fields in Deep Convolutional Networks
Deep convolutional neural networks (ConvNets) have rapidly grownin popularity due to their powerful capabilities in representing andmodelling the high-level abstraction of complex data. However,ConvNets require an abundance of data to adequately train networkparameters. To tackle this problem, we introduce the conceptof stochastic receptive fields, where the receptive fields arestochastic realizations of a random field that obey a learned distribution.We study the efficacy of incorporating layers of stochasticreceptive fields to a ConvNet to boost performance without theneed for additional training data. Preliminary results showing animprovement in accuracy ( 2% drop in test error) was achieved byadding a layer of stochastic receptive fields to a ConvNet comparedto adding a layer of fully-trained receptive fields, when training witha small training set consisting of 20% of the STL-10 dataset
Self-supervised learning: When is fusion of the primary and secondary sensor cue useful?
Self-supervised learning (SSL) is a reliable learning mechanism in which a
robot enhances its perceptual capabilities. Typically, in SSL a trusted,
primary sensor cue provides supervised training data to a secondary sensor cue.
In this article, a theoretical analysis is performed on the fusion of the
primary and secondary cue in a minimal model of SSL. A proof is provided that
determines the specific conditions under which it is favorable to perform
fusion. In short, it is favorable when (i) the prior on the target value is
strong or (ii) the secondary cue is sufficiently accurate. The theoretical
findings are validated with computational experiments. Subsequently, a
real-world case study is performed to investigate if fusion in SSL is also
beneficial when assumptions of the minimal model are not met. In particular, a
flying robot learns to map pressure measurements to sonar height measurements
and then fuses the two, resulting in better height estimation. Fusion is also
beneficial in the opposite case, when pressure is the primary cue. The analysis
and results are encouraging to study SSL fusion also for other robots and
sensors
Deep Predictive Models for Collision Risk Assessment in Autonomous Driving
In this paper, we investigate a predictive approach for collision risk
assessment in autonomous and assisted driving. A deep predictive model is
trained to anticipate imminent accidents from traditional video streams. In
particular, the model learns to identify cues in RGB images that are predictive
of hazardous upcoming situations. In contrast to previous work, our approach
incorporates (a) temporal information during decision making, (b) multi-modal
information about the environment, as well as the proprioceptive state and
steering actions of the controlled vehicle, and (c) information about the
uncertainty inherent to the task. To this end, we discuss Deep Predictive
Models and present an implementation using a Bayesian Convolutional LSTM.
Experiments in a simple simulation environment show that the approach can learn
to predict impending accidents with reasonable accuracy, especially when
multiple cameras are used as input sources.Comment: 8 pages, 4 figure
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