We fabricate and characterize asymmetric memristors which show a very strong single-sided hysteresis. When biased in one direction there is hysteresis and in the opposite direction there is a lack of hysteresis. We demonstrate that this apparent lack is actually hysteresis on a much faster time-scale. We further demonstrate that this form of asymmetric behavior correlates very well to the asymmetric structure and function of an actual synapse. The asymmetric memristor device presented here is necessary to correctly implement spike-timing-dependent-plasticity STDP in mixed memristor/neuron hybrid systems as an artificial synapse. These devices show the required characteristics for implementing the asymmetric form of long-term potentiation (LTP) and long-term depression (LTD) of a synapse between two neurons, where symmetric memristor devices do not. Signals from a presynaptic neuron are sent via its axon across the synapse to the dendrite of a postsynaptic neuron. Postsynaptic neuron signals sent to subsequent neurons have an influence on the strength of any further presynaptic neuron signals received by the postsynaptic neuron across the synapse. These signals are grouped into spike triplets within the framework of STDP and, as we experimentally show here, can be implemented with asymmetric memristors, not standard symmetric memristors.
Pitch is an essential category for musical sensations. Models of pitch perception are vividly discussed up to date. Most of them rely on definitions of mathematical methods in the spectral or temporal domain. Our proposed pitch perception model is composed of an active auditory model extended by octopus cells. The active auditory model is the same as used in the Stimulation based on Auditory Modeling (SAM), a successful cochlear implant sound processing strategy extended here by modeling the functional behavior of the octopus cells in the ventral cochlear nucleus and by modeling their connections to the auditory nerve fibers (ANFs). The neurophysiological parameterization of the extended model is fully described in the time domain. The model is based on latency-phase en- and decoding as octopus cells are latency-phase rectifiers in their local receptive fields. Pitch is ubiquitously represented by cascaded firing sweeps of octopus cells. Based on the firing patterns of octopus cells, inter-spike interval histograms can be aggregated, in which the place of the global maximum is assumed to encode the pitch.
The general scheme for the fast, pipelined rst level trigger on high pt muons in the CMS detector at LHC is presented. The prototype PACT system was tested in the high momentum muon beams in the RD5 experiment during 1993/94 runs. The obtained eciency curves are shown.
This study presents a computational model to reproduce the biological dynamics of "listening to music." A biologically plausible model of periodicity pitch detection is proposed and simulated. Periodicity pitch is computed across a range of the auditory spectrum. Periodicity pitch is detected from subsets of activated auditory nerve fibers (ANFs). These activate connected model octopus cells, which trigger model neurons detecting onsets and offsets; thence model interval-tuned neurons are innervated at the right interval times; and finally, a set of common interval-detecting neurons indicate pitch. Octopus cells rhythmically spike with the pitch periodicity of the sound. Batteries of interval-tuned neurons stopwatch-like measure the inter-spike intervals of the octopus cells by coding interval durations as first spike latencies (FSLs). The FSL-triggered spikes synchronously coincide through a monolayer spiking neural network at the corresponding receiver pitch neurons.
The Driving Assistance Systems (DAS) aim to help the vehicle drivers to proceed through different road situations. However, their main task is not only to safe one particular driver, but also to increase the safety for all traffic members. The problem domain here is huge and might be divided into the subtopics, like driver's fatigue detection, pedestrian tracking, obstacle collision avoidance, lane departure warnings and traffic signs detection and recognition. Advanced computer vision techniques are widely used in order to develop sufficient and robust systems for driving assistance. In this paper we discuss the video-based Hough-Transform driven objects detection algorithms and their applications for lane departure warnings, as well as for traffic signs detection. Furthermore, a high-speed hardware implementation of these algorithms on the FPGA/ASIC is also presented
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