Classes de dynamiques neuronales et correlations structurées par l'experience dans le cortex visuel.

Neuronal activity is often considered in cognitive neuroscience by the evoked response but most the energy used by the brain is devoted to the sustaining of ongoing dynamics in cortical networks. A combination of classification algorithms (K means, Hierarchical tree, SOM) is used on intracellular recordings of the primary visual cortex of the cat to define classes of neuronal dynamics and to compare it with the activity evoked by a visual stimulus. Those dynamics can be studied with simplified models (FitzHugh Nagumo, hybrid dynamical systems, Wilson Cowan) for which an analysis is presented. Finally, with simulations of networks composed of columns of spiking neurons, we study the ongoing dynamics in a model of the primary visual cortex and their effect on the response evoked by a stimulus. After a learning period during which visual stimuli are presented, waves of depolarization propagate through the network. The study of correlations in this network shows that the ongoing dynamics reflect the functional properties acquired during the learning period.L'activité neuronale est souvent considérée en neuroscience cognitive par la réponse évoquée mais l'essentiel de l'énergie consommée par le cerveau permet d'entretenir les dynamiques spontanées des réseaux corticaux. L'utilisation combinée d'algorithmes de classification (K means, arbre hirarchique, SOM) sur des enregistrements intracellulaires du cortex visuel primaire du chat nous permet de définir des classes de dynamiques neuronales et de les comparer l'activité évoquée par un stimulus visuel. Ces dynamiques peuvent être étudiées sur des systèmes simplifiés (FitzHugh-Nagumo, systèmes dynamiques hybrides, Wilson-Cowan) dont nous présentons l'analyse. Enfin, par des simulations de réseaux composés de colonnes de neurones, un modèle du cortex visuel primaire nous permet d'étudier les dynamiques spontanées et leur effet sur la réponse à un stimulus. Après une période d'apprentissage pendant laquelle des stimuli visuels sont presentés, des vagues de dépolarisation se propagent dans le réseau. L'étude des correlations dans ce réseau montre que les dynamiques spontanées reflètent les propriétés fonctionnelles acquises au cours de l'apprentissage

Robust sequence storage in bistable oscillators

International audienceNanoscale devices, like magnetic tunnel junction, have rich dynamics, including oscillatory ones. Such components may relieve us of the burden of integrating nonlinear equations. We propose a phenomenological model of bistable oscillators and we show that in a network, it achieves robust storage of sequences

Continual Learning with Deep Streaming Regularized Discriminant Analysis

Continual learning is increasingly sought after in real world machine learning applications, as it enables learning in a more human-like manner. Conventional machine learning approaches fail to achieve this, as incrementally updating the model with non-identically distributed data leads to catastrophic forgetting, where existing representations are overwritten. Although traditional continual learning methods have mostly focused on batch learning, which involves learning from large collections of labeled data sequentially, this approach is not well-suited for real-world applications where we would like new data to be integrated directly. This necessitates a paradigm shift towards streaming learning. In this paper, we propose a streaming version of regularized discriminant analysis as a solution to this challenge. We combine our algorithm with a convolutional neural network and demonstrate that it outperforms both batch learning and existing streaming learning algorithms on the ImageNet ILSVRC-2012 dataset

Intrinsic motivation and episodic memories for robot exploration of high-dimensional sensory spaces

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer überregionalen Konsortiallizenz frei zugänglich.This work presents an architecture that generates curiosity-driven goal-directed exploration behaviours for an image sensor of a microfarming robot. A combination of deep neural networks for offline unsupervised learning of low-dimensional features from images and of online learning of shallow neural networks representing the inverse and forward kinematics of the system have been used. The artificial curiosity system assigns interest values to a set of pre-defined goals and drives the exploration towards those that are expected to maximise the learning progress. We propose the integration of an episodic memory in intrinsic motivation systems to face catastrophic forgetting issues, typically experienced when performing online updates of artificial neural networks. Our results show that adopting an episodic memory system not only prevents the computational models from quickly forgetting knowledge that has been previously acquired but also provides new avenues for modulating the balance between plasticity and stability of the models.H2020 Marie Skłodowska-Curie Actionshttps://doi.org/10.13039/100010665Horizon 2020 Framework Programmehttps://doi.org/10.13039/100010661Peer Reviewe

Working memory dynamics and spontaneous activity in a flip-flop oscillations network model with a Milnor attractor

Many cognitive tasks require the ability to maintain and manipulate simultaneously several chunks of information. Numerous neurobiological observations have reported that this ability, known as the working memory, is associated with both a slow oscillation (leading to the up and down states) and the presence of the theta rhythm. Furthermore, during resting state, the spontaneous activity of the cortex exhibits exquisite spatiotemporal patterns sharing similar features with the ones observed during specific memory tasks. Here to enlighten neural implication of working memory under these complicated dynamics, we propose a phenomenological network model with biologically plausible neural dynamics and recurrent connections. Each unit embeds an internal oscillation at the theta rhythm which can be triggered during up-state of the membrane potential. As a result, the resting state of a single unit is no longer a classical fixed point attractor but rather the Milnor attractor, and multiple oscillations appear in the dynamics of a coupled system. In conclusion, the interplay between the up and down states and theta rhythm endows high potential in working memory operation associated with complexity in spontaneous activities

Annotated dataset for the semantic segmentation of radishes

This folder contains pictures of radishes collected on the PMF experimental field during Spring 2017. There are two kinds of labeled images in the following folders: - human annotations: human annotators draw polygons around each plant and those were then refined using an active contours algorithm. - machine annotations: An SVM trained on the human annotations was used to produce labeled images. Images with bad segmentation were manually discarded. Each of these folder contains an images folder containing original pictures and a labels folder containing binary segmentation masks

Bringing phenotyping to the farm: an evaluation of 3d reconstruction of plants in outdoor environement.

Recent reviews have pointed out that data collection and trait extraction is the major bottleneck in plant phenotyping. Several platforms, like PhenoTiki [3], provides Open Source components for an affordable acquisition of plant images. Recent years also emphasized the importance of 3d geometric features to characterize the morphology of plants. We describe the hardware and software components for the acquisition of 3d images and a precise analysis of the plant morphology. The platform distinguishes itself from existing work in that it is designed as an affordable and modular solution that can be used both in the lab and in the field, in either static or mobile configuration. In addition, the plant images are captured with an off-the-shelf camera that combines color from an RGB sensor with depth information from a time-of-flight (TOF) sensor (DepthSense by SoftKinetic)

Classes de dynamiques neuronales et correlations structurées par l'experience dans le cortex visuel

PALAISEAU-Polytechnique (914772301) / SudocSudocFranceF