1,721,222 research outputs found
Firing proprieties of an adaptive analog VLSI neuron
Full text linkBen Dayan Rubin D, Chicca E, Indiveri G. Firing proprieties of an adaptive analog VLSI neuron. Presented at the Proceedings of Bio-ADIT, Lausanne, Switzerland.We describe the response properties of a compact, low power, analog circuit that implements a model of a leaky I&F neuron, with spike-frequency adaptation, refractory period and voltage threshold modulation properties. We investigate the statistics of the circuit's output response by modulation its operating parameters, like refractory period and adaptation level and by changing the statistics of the input current. The results show a clear match with theoretical and neurophysiological data in a given range of the parameter space. This analysis defines the chip's parameter working range and predicts its behavior in case of integration into large massively parallel VLSI networks
A systematic method for configuring VLSI networks of spiking neurons
Full text linkNeftci E, Chicca E, Indiveri G, Douglas RJ. A systematic method for configuring VLSI networks of spiking neurons. Neural Computation. 2011;23(10):2457-2497.An increasing number of research groups are developing custom hybrid analog/digital very large scale integration (VLSI) chips and systems that implement hundreds to thousands of spiking neurons with biophysically realistic dynamics, with the intention of emulating brainlike real-world behavior in hardware and robotic systems rather than simply simulating their performance on general-purpose digital computers. Although the electronic engineering aspects of these emulation systems is proceeding well, progress toward the actual emulation of brainlike tasks is restricted by the lack of suitable high-level configuration methods of the kind that have already been developed over many decades for simulations on general-purpose computers. The key difficulty is that the dynamics of the CMOS electronic analogs are determined by transistor biases that do not map simply to the parameter types and values used in typical abstract mathematical models of neurons and their networks. Here we provide a general method for resolving this difficulty. We describe a parameter mapping technique that permits an automatic configuration of VLSI neural networks so that their electronic emulation conforms to a higher-level neuronal simulation. We show that the neurons configured by our method exhibit spike timing statistics and temporal dynamics that are the same as those observed in the software simulated neurons and, in particular, that the key parameters of recurrent VLSI neural networks (e. g., implementing soft winner-take-all) can be precisely tuned. The proposed method permits a seamless integration between software simulations with hardware emulations and intertranslatability between the parameters of abstract neuronal models and their emulation counterparts. Most important, our method offers a route toward a high-level task configuration language for neuromorphic VLSI systems
Characterizing the firing properties of an adaptive analog VLSI neuron
Full text linkBen Dayan Rubin D, Chicca E, Indiveri G. Characterizing the firing properties of an adaptive analog VLSI neuron. Biologically Inspired Approaches to Advanced Information Technology. 2004;3141:189-200.We describe the response properties of a compact, low power, analog circuit that implements a model of a leaky-Integrate & Fire (I&F) neuron, with spike-frequency adaptation, refractory period and voltage threshold modulation properties. We investigate the statistics of the circuit's output response by modulating its operating parameters, like refractory period and adaptation level and by changing the statistics of the input current. The results show a clear match with theoretical prediction and neurophysiological data in a given range of the parameter space. This analysis defines the chip's parameter working range and predicts its behavior in case of integration into large massively parallel very-large-scale-integration (VLSI) networks
A recipe for creating ideal hybrid memristive-CMOS neuromorphic processing systems
Full text linkChicca E, Indiveri G. A recipe for creating ideal hybrid memristive-CMOS neuromorphic processing systems. Applied Physics Letters. 2020;116(12): 120501
Geotechnical Surveys with Cooperative Autonomous Marine Vehicles: The EC WiMUST project
No full textThe H2020 WiMUST (Widely scalable Mobile Underwater Sonar Technology) project aimed at expanding and improving the functionalities of current cooperative marine robotic systems, effectively enabling distributed acoustic array technologies for geophysical surveying with a view to ocean exploration and geotechnical applications. The paper includes a brief overview of the WiMUST project and a short description of the final experimental results at sea obtained with a group of 7 autonomous marine vehicles equipped with two acoustic sparkers and streamers with hydrophone arrays
Control oriented modeling of a twin thruster autonomous surface vehicle
No full textThis work investigates and identifies a first principles maneuvering model for a small size robotic twin thruster autonomous surface vessel (ASV) that includes and explains the sources of nonlinearity and asymmetry of this class of robots. With respect to state of the art ASV models, the proposed one accounts for the effects generating a transverse thrust, explaining the asymmetric turning radii. The model also accounts for the need to adapt the hydrodynamic derivatives when the ASV performs large or tight turns. An experimental dataset has been acquired using the ULISSE ASV and it is used to support the proposed model in comparison to the “baseline” one often used in the literature. The improved precision of the proposed model in fitting experimental data is a necessary prerequisite to design model-based motion controller and navigation systems with enhanced performance
Experimental Results and Design of an Integrated Low-Noise Read-out System for DNA Capacitive Sensor
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Robust global stabilization of an underactuated marine vehicle on a linear course by smooth time invariant feedback
No full textWiMUST: A cooperative marine robotic system for autonomous geotechnical surveys
No full textThis paper presents the main results of the European H2020 WiMUST project, whose aim was the development of a system of cooperative autonomous underwater vehicles and autonomous surface vehicles for geotechnical surveying. In particular, insights on the overall robotic technologies and methodologies employed, ranging from the communications and navigation framework to the cooperative and coordinated control solutions are given. The software architecture and the lessons learnt from the preliminary field test are also discussed. Finally, field results of the final survey campaign carried out in the Atlantic Ocean are presented, demonstrating how a team of seven robots could autonomously conduct a geotechnical survey, producing seismic images without artifacts
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