Letteratura scientifica selezionata sul tema "Dispositifs neuromorphiques"
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Tesi sul tema "Dispositifs neuromorphiques":
Janzakova, Kamila. "Développement de dendrites polymères organiques en 3D comme dispositif neuromorphique". Electronic Thesis or Diss., Université de Lille (2022-....), 2023. http://www.theses.fr/2023ULILN017.
Neuromorphic technologies is a promising direction for development of more advanced and energy-efficient computing. They aim to replicate attractive brain features such as high computational efficiency at low power consumption on a software and hardware level. At the moment, brain-inspired software implementations (such as ANN and SNN) have already shown their successful application for different types of tasks (image and speech recognition). However, to benefit more from the brain-like algorithms, one may combine them with appropriate hardware that would also rely on brain-like architecture and processes and thus complement them. Neuromorphic engineering has already shown the utilization of solid-state electronics (CMOS circuits, memristor) for the development of brain-inspired devices. Nevertheless, these implementations are fabricated through top-down methods. In contrast, brain computing relies on bottom-up processes such as interconnectivity between cells and the formation of neural communication pathways.In the light of mentioned above, this work reports on the development of programmable 3D organic neuromorphic devices, which, unlike most current neuromorphic technologies, can be created in a bottom-up manner. This allows bringing neuromorphic technologies closer to the level of brain programming, where necessary neural paths are established only on the need.First, we found out that PEDOT:PSS based 3D interconnections can be formed by means of AC-bipolar electropolymerization and that they are capable of mimicking the growth of neural cells. By tuning individually the parameters of the waveform (peak amplitude voltage -VP, frequency - f, duty cycle - dc and offset voltage - Voff), a wide range of dendrite-like structures was observed with various branching degrees, volumes, surface areas, asymmetry of formation, and even growth dynamics.Next, it was discovered that dendritic morphologies obtained at various frequencies are conductive. Moreover, each structure exhibits an individual conductance value that can be interpreted as synaptic weight. More importantly, the ability of dendrites to function as OECT was revealed. Different dendrites exhibited different performances as OECT. Further, the ability of PEDOT:PSS dendrites to change their conductivity in response to gate voltage was used to mimic brain memory functions (short-term plasticity -STP and long-term plasticity -LTP). STP responses varied depending on the dendritic structure. Moreover, emulation of LTP was demonstrated not only by means of an Ag/AgCl gate wire but as well by means of a self-developed polymer dendritic gate.Finally, structural plasticity was demonstrated through dendritic growth, where the weight of the final connection is governed according to Hebbian learning rules (spike-timing-dependent plasticity - STDP and spike-rate-dependent plasticity - SRDP). Using both approaches, a variety of dendritic topologies with programmable conductance states (i.e., synaptic weight) and various dynamics of growth have been observed. Eventually, using the same dendritic structural plasticity, more complex brain features such as associative learning and classification tasks were emulated.Additionally, future perspectives of such technologies based on self-propagating polymer dendritic objects were discussed
Bennett, Christopher H. "Apprentissage local avec des dispositifs de mémoire hautement analogiques". Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS037/document.
In the next era of distributed computing, brain-based computers that perform operations locally rather than in remote servers would be a major benefit in reducing global energy costs. A new generation of emerging nonvolatile memory devices is a leading candidate for achieving this neuromorphic vision. Using theoretical and experimental work, we have explored critical issues that arise when physically realizing modern artificial neural network (ANN) architectures using emerging memory devices (“memristors”). In our experimental work, we showed organic nanosynapses adapting automatically as logic gates via a companion digital neuron and programmable logic cell (FGPA). In our theoretical work, we also considered multilayer memristive ANNs. We have developed and simulated random projection (NoProp) and backpropagation (Multilayer Perceptron) variants that use two crossbars. These local learning systems showed critical dependencies on the physical constraints of nanodevices. Finally, we examined how feed-forward ANN designs can be modified to exploit temporal effects. We focused in particular on improving bio-inspiration and performance of the NoProp system, for example, we improved the performance with plasticity effects in the first layer. These effects were obtained using a silver ionic nanodevice with intrinsic plasticity transition behavior
Bichler, Olivier. "Contribution à la conception d'architecture de calcul auto-adaptative intégrant des nanocomposants neuromorphiques et applications potentielles". Phd thesis, Université Paris Sud - Paris XI, 2012. http://tel.archives-ouvertes.fr/tel-00781811.
La, Barbera Selina. "Development of filamentary Memristive devices for synaptic plasticity implementation". Thesis, Lille 1, 2015. http://www.theses.fr/2015LIL10163/document.
Replicating the computational functionalities of the brain remains one of the biggest challenges for the future of information and communication technologies. In this context, neuromorphic engineering appears a very promising direction. In this context memristive devices have been recently proposed for the implementation of synaptic functions, offering the required features and integration potentiality in a single component. In this dissertation, we present how advanced synaptic features can be implemented in memristive nanodevices. By exploiting the physical properties of filamentary switching, we successfully implemented a non-Hebbian plasticity form corresponding to the synaptic adaptation. We demonstrate that complex filament shape, such as dendritic paths of variable density and width, can reproduce short- and long- term processes observed in biological synapses and can be conveniently controlled by achieving a flexible way to program the device memory state and the relative state volatility. Then, we show that filamentary switching can be additionally controlled to reproduce a Hebbian plasticity form that corresponds to an increase of the synaptic weight when time correlation between pre- and post-neuron firing is experienced at the synaptic connection. We interpreted our results in the framework of a phenomenological model developed for biological synapses. Finally, we exploit this model to investigate how spike-based systems can be realized for memory and computing applications. These results pave the way for future engineering of neuromorphic computing systems, where complex behaviors of memristive physics can be exploited
Roclin, David. "Utilisation des nano-composants électroniques dans les architectures de traitement associées aux imageurs". Thesis, Paris 11, 2014. http://www.theses.fr/2014PA112408/document.
By using learning mechanisms extracted from recent discoveries in neuroscience, spiking neural networks have demonstrated their ability to efficiently analyze the large amount of data from our environment. The implementation of such circuits on conventional processors does not allow the efficient exploitation of their parallelism. The use of digital memory to implement the synaptic weight does not allow the parallel reading or the parallel programming of the synapses and it is limited by the bandwidth of the connection between the memory and the processing unit. Emergent memristive memory technologies could allow implementing this parallelism directly in the heart of the memory.In this thesis, we consider the development of an embedded spiking neural network based on emerging memory devices. First, we analyze a spiking network to optimize its different components: the neuron, the synapse and the STDP learning mechanism for digital implementation. Then, we consider implementing the synaptic memory with emergent memristive devices. Finally, we present the development of a neuromorphic chip co-integrating CMOS neurons with CBRAM synapses
Arth, Kevin. "Neuromorphic sensory substitution with an asynchronous tactile belt for unsighted people : from design to clinical trials". Thesis, Sorbonne université, 2018. http://www.theses.fr/2018SORUS218.
This document presents the conception of the first neuromorphic tactile sensory substitution device, merging the domains of neuroprosthetics and sensory substitution.After a presentation of the state of art of the domains at the core of this work, we will introduce the device and present its chronological evolution and technical choices. We will then in a second stage introduce the validation studies that have been carried out to test the tactile neuromorphic device on blind and healthy control patients. The first study relies on psychophysical tests carried out to evaluate the link between spatial and temporal resolution of the developed device. The test relied on the ability of subjects to detect the direction of motion of a point sent on the tactile belt contacting the back of the subject. In the second study, the neuromorphic tactile system is coupled with an artificial silicon retina. A clinical trial is performed to study the performances of the developed device in a more complex environments using an incremental learning method. This study also evaluates the subjects’ feedback on the ergonomics of such an equipment. Ten visually impaired and five well-sighted subjects were selected. Subjects were able to detect objects in motion, discriminate the spacing between shapes, find a target in a scene with variable brightness, follow a signaled path on the ground and even avoid potential obstacles