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Over the past 40 years, neurobiology and computational neuroscience has proved that deeper understanding of visual processes in humans and non-human primates can lead to important advancements in computational perception theories and systems. One of the main difficulties that arises when designing automatic vision systems is developing a mechanism that can recognize - or simply find - an object when faced with all the possible variations that may occur in a natural scene, with the ease of the primate visual system. The area of the brain in primates that is dedicated at analyzing visual information is the visual cortex. The visual cortex performs a wide variety of complex tasks by means of simple operations. These seemingly simple operations are applied to several layers of neurons organized into a hierarchy, the layers representing increasingly complex, abstract intermediate processing stages. In this Research Topic we propose to bring together current efforts in neurophysiology and computer vision in order 1) To understand how the visual cortex encodes an object from a starting point where neurons respond to lines, bars or edges to the representation of an object at the top of the hierarchy that is invariant to illumination, size, location, viewpoint, rotation and robust to occlusions and clutter; and 2) How the design of automatic vision systems benefit from that knowledge to get closer to human accuracy, efficiency and robustness to variations.

object recognition --- Neuronal modeling --- shape --- Neuromorphic --- Computational neuroscence --- Attention --- Visual Cortex --- Computer Vision

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Once a day, every individual lay down and becomes unconscious. Isn’t sleep a strange thing to do? Despite the risks associated with it, our ancestors used to sleep too, suggesting that it should provide an evolutionary advantage. Thus, it raises a fundamental question: why do we sleep? Among all essential functions of sleep, research has proved its preponderant role in memory formation and consolidation. 
At the cellular level, memory is achieved through processes referred to as synaptic plasticity and translating the remarkable ability of the brain to constantly evolve due to various stimuli. Furthermore, differences in the neuronal firing patterns have been highlighted between wake and sleep: during sleep, neurons are bursting while during wake, neurons show a tonic firing pattern. 

Memory is an abstract concept, it is not a simple task to understand the processes behind it. As experimental evidence provides insights about how plasticity is induced, modeling techniques reproducing experimental data can give insights about memory mechanisms. Literature is broad concerning plasticity modeling. In this work, a concise review of phenomenological models is conducted.

Then, some of them are implemented in a conductance-based model able to switch from waking to sleep, i.e. from tonic to bursting activity. Compared to simplified spiking neuron model, this conductance-based model is a powerful tool to be able to faithfully replicate neuronal behavior in waking and sleeping period. Reproduction of experimental protocols is carried in tonic mode as well as the impact of variability in the firing pattern to mimic more in vivo situations. As the ultimate goal of this thesis is to see the impact of existing models on memory consolidation during sleep, their robustness and behaviour during a bursting period are investigated. It led to unsatisfactory results regarding memory consolidation, highlighting the limitations of those phenomenological models. The behaviour of the models implemented highly depends on the method used to bound the synaptic weight in-between extreme values. Finally, insights about neuromodulation are suggested as improvements.

synaptic plasticity --- computational neurosciences --- memory consolidation --- neuronal modeling --- Ingénierie, informatique & technologie > Multidisciplinaire, généralités & autres