21,218 research outputs found
Locust-inspired vision system on chip architecture for collision detection in automotive applications
This paper describes a programmable digital computing architecture dedicated to process information in accordance to the organization and operating principles of the four-layer neuron structure encountered at the visual system of Locusts. This architecture takes advantage of the natural collision detection skills of locusts and is capable of processing images and ascertaining collision threats in real-time automotive scenarios. In addition to the Locust features, the architecture embeds a Topological Feature Estimator module to identify and classify objects in collision course.European Commission IST2001 - 38097Ministerio de Ciencia y Tecnología TIC2003 - 09817- C02 - 0
A modified model for the Lobula Giant Movement Detector and its FPGA implementation
The Lobula Giant Movement Detector (LGMD) is a wide-field visual neuron located in the Lobula layer of the Locust nervous system. The LGMD increases its firing rate in response to both the velocity of an approaching object and the proximity of this object. It has been found that it can respond to looming stimuli very quickly and trigger avoidance reactions. It has been successfully applied in
visual collision avoidance systems for vehicles and robots. This paper introduces a modified neural model for LGMD that provides additional depth direction information for the movement. The proposed model retains the simplicity of the previous model by adding only a few new cells. It has been
simplified and implemented on a Field Programmable Gate Array (FPGA), taking advantage of the inherent parallelism exhibited by the LGMD, and tested on real-time video streams. Experimental results demonstrate the effectiveness as a fast motion detector
Evolving Gene Regulatory Networks with Mobile DNA Mechanisms
This paper uses a recently presented abstract, tuneable Boolean regulatory
network model extended to consider aspects of mobile DNA, such as transposons.
The significant role of mobile DNA in the evolution of natural systems is
becoming increasingly clear. This paper shows how dynamically controlling
network node connectivity and function via transposon-inspired mechanisms can
be selected for in computational intelligence tasks to give improved
performance. The designs of dynamical networks intended for implementation
within the slime mould Physarum polycephalum and for the distributed control of
a smart surface are considered.Comment: 7 pages, 8 figures. arXiv admin note: substantial text overlap with
arXiv:1303.722
On The Robustness of a Neural Network
With the development of neural networks based machine learning and their
usage in mission critical applications, voices are rising against the
\textit{black box} aspect of neural networks as it becomes crucial to
understand their limits and capabilities. With the rise of neuromorphic
hardware, it is even more critical to understand how a neural network, as a
distributed system, tolerates the failures of its computing nodes, neurons, and
its communication channels, synapses. Experimentally assessing the robustness
of neural networks involves the quixotic venture of testing all the possible
failures, on all the possible inputs, which ultimately hits a combinatorial
explosion for the first, and the impossibility to gather all the possible
inputs for the second.
In this paper, we prove an upper bound on the expected error of the output
when a subset of neurons crashes. This bound involves dependencies on the
network parameters that can be seen as being too pessimistic in the average
case. It involves a polynomial dependency on the Lipschitz coefficient of the
neurons activation function, and an exponential dependency on the depth of the
layer where a failure occurs. We back up our theoretical results with
experiments illustrating the extent to which our prediction matches the
dependencies between the network parameters and robustness. Our results show
that the robustness of neural networks to the average crash can be estimated
without the need to neither test the network on all failure configurations, nor
access the training set used to train the network, both of which are
practically impossible requirements.Comment: 36th IEEE International Symposium on Reliable Distributed Systems 26
- 29 September 2017. Hong Kong, Chin
Approximating Mexican highways with slime mould
Plasmodium of Physarum polycephalum is a single cell visible by unaided eye.
During its foraging behavior the cell spans spatially distributed sources of
nutrients with a protoplasmic network. Geometrical structure of the
protoplasmic networks allows the plasmodium to optimize transport of nutrients
between remote parts of its body. Assuming major Mexican cities are sources of
nutrients how much structure of Physarum protoplasmic network correspond to
structure of Mexican Federal highway network? To find an answer undertook a
series of laboratory experiments with living Physarum polycephalum. We
represent geographical locations of major cities by oat flakes, place a piece
of plasmodium in Mexico city area, record the plasmodium's foraging behavior
and extract topology of nutrient transport networks. Results of our experiments
show that the protoplasmic network formed by Physarum is isomorphic, subject to
limitations imposed, to a network of principle highways. Ideas and results of
the paper may contribute towards future developments in bio-inspired road
planning
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