02916 am 2200661 n 450 9910558699903321202203282-7535-8797-310.4000/books.pur.161356(CKB)4100000012873205(FrMaCLE)OB-pur-161356(oapen)https://directory.doabooks.org/handle/20.500.12854/86714(PPN)267767528(EXLCZ)99410000001287320520220415j|||||||| ||| 0freuu||||||m||||txtrdacontentcrdamediacrrdacarrierPromesses de Patagonie L’exploration française en Amérique australe et la patrimonialisation du « bout du monde » /Paz Núñez-RegueiroRennes Presses universitaires de Rennes20221 online resource (394-XXXII p.) Des Amériques2-7535-8384-6 Au xixe siècle, l’Amérique australe est encore perçue comme une région limitrophe entre les mondes connus et inconnus, un espace primordial situé à la dernière frontière qui se déploie entre « civilisation » et « barbarie ». Ce territoire resté hors de toute autorité étatique jusqu’à la décennie de 1880 fascine par sa nature majestueuse et les spécificités biologiques et culturelles de ses habitants : il apparaît comme un livre ouvert sur le temps, susceptible de dévoiler les secrets sur l’évolution de l’espèce humaine. Les disciplines naissantes de l’anthropologie et de l’ethnographie en font ainsi un terrain d’étude privilégié. En remontant la piste des aventuriers français en Patagonie, en Terre de Feu et en Araucanie, cet ouvrage retrace l’histoire des grandes missions d’exploration et de collecte menées dans le sud de l’Argentine et du Chili, et enquête sur la manière dont s’est faite l’appropriation intellectuelle et matérielle des populations du « bout du monde » à Paris, à l’heure de l’essor des musées d’ethnographie.AnthropologyanthropologieexplorationcoloniemuséeMapuchetehuelcheyahganeanthropologymuseumcolonialismanthropologyexplorationmuseumcolonialismAnthropologyanthropologieexplorationcoloniemuséeMapuchetehuelcheyahganeanthropologymuseumcolonialismNúñez-Regueiro Paz1324114FR-FrMaCLEBOOK9910558699903321Promesses de Patagonie3035915UNINA04544nam 2201045z- 450 9910404090703321202102113-03928-577-7(CKB)4100000011302231(oapen)https://directory.doabooks.org/handle/20.500.12854/53144(oapen)doab53144(EXLCZ)99410000001130223120202102d2020 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierMemristors for Neuromorphic Circuits and Artificial Intelligence ApplicationsMDPI - Multidisciplinary Digital Publishing Institute20201 online resource (244 p.)3-03928-576-9 Artificial Intelligence (AI) has found many applications in the past decade due to the ever increasing computing power. Artificial Neural Networks are inspired in the brain structure and consist in the interconnection of artificial neurons through artificial synapses. Training these systems requires huge amounts of data and, after the network is trained, it can recognize unforeseen data and provide useful information. The so-called Spiking Neural Networks behave similarly to how the brain functions and are very energy efficient. Up to this moment, both spiking and conventional neural networks have been implemented in software programs running on conventional computing units. However, this approach requires high computing power, a large physical space and is energy inefficient. Thus, there is an increasing interest in developing AI tools directly implemented in hardware. The first hardware demonstrations have been based on CMOS circuits for neurons and specific communication protocols for synapses. However, to further increase training speed and energy efficiency while decreasing system size, the combination of CMOS neurons with memristor synapses is being explored. The memristor is a resistor with memory which behaves similarly to biological synapses. This book explores the state-of-the-art of neuromorphic circuits implementing neural networks with memristors for AI applications.History of engineering and technologybicsscAIartificial intelligenceartificial neural networkartificial synapseautocovarianceboost-factor adjustmentcharacter recognitioncircuit designcompact modelcortical neuronscrossbar arraydeep learning networksdefect-tolerant spatial poolingelectronic synapsesemulatorFlash memoriesgraphene oxidehardware-based deep learning ICshierarchical temporal memorylasermemristive devicesmemristormemristor crossbarmemristor-CMOS hybrid circuitmemristorsmultiscale modelingneocortexneural network hardwareneural networksneuromorphicneuromorphic computingneuromorphic engineeringneuromorphic hardwareneuromorphic systemsneuromorphicsoptimizationOxRAMpattern recognitionpavlovreinforcement learningresistive switchingRRAMself-organization mapssensory and hippocampal responsessimulationspike-timing-dependent plasticityspiking neural networkspiking neural networksSTDPstrongly correlated oxidessynapsesynaptic devicesynaptic plasticitysynaptic weighttemporal poolingtime series modelingtransistor-like devicesvariabilityvertical RRAMwire resistanceHistory of engineering and technologySuñé Jordiauth1328958BOOK9910404090703321Memristors for Neuromorphic Circuits and Artificial Intelligence Applications3039226UNINA