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of 37
pro vyhledávání: '"Mastrogiuseppe, Francesca"'
The relation between neural activity and behaviorally relevant variables is at the heart of neuroscience research. When strong, this relation is termed a neural representation. There is increasing evidence, however, for partial dissociations between
Externí odkaz:
http://arxiv.org/abs/2307.07654
Publikováno v:
Phys. Rev. Research 3, 013176 (2021)
A fundamental feature of complex biological systems is the ability to form feedback interactions with their environment. A prominent model for studying such interactions is reservoir computing, where learning acts on low-dimensional bottlenecks. Desp
Externí odkaz:
http://arxiv.org/abs/2011.06066
Autor:
Beiran, Manuel, Dubreuil, Alexis, Valente, Adrian, Mastrogiuseppe, Francesca, Ostojic, Srdjan
An emerging paradigm proposes that neural computations can be understood at the level of dynamical systems that govern low-dimensional trajectories of collective neural activity. How the connectivity structure of a network determines the emergent dyn
Externí odkaz:
http://arxiv.org/abs/2007.02062
Autor:
Schuessler, Friedrich, Mastrogiuseppe, Francesca, Dubreuil, Alexis, Ostojic, Srdjan, Barak, Omri
Recurrent neural networks (RNNs) trained on low-dimensional tasks have been widely used to model functional biological networks. However, the solutions found by learning and the effect of initial connectivity are not well understood. Here, we examine
Externí odkaz:
http://arxiv.org/abs/2006.11036
Autor:
Mastrogiuseppe, Francesca
Le cortex cérébral des mammifères est constitué de larges et complexes réseaux de neurones. La tâche de ces assemblées de cellules est d’encoder et de traiter, le plus précisément possible, l'information sensorielle issue de notre environn
Externí odkaz:
http://www.theses.fr/2017PSLEE048/document
Autor:
Schuessler, Friedrich, Dubreuil, Alexis, Mastrogiuseppe, Francesca, Ostojic, Srdjan, Barak, Omri
Publikováno v:
Phys. Rev. Research 2, 013111 (2020)
A given neural network in the brain is involved in many different tasks. This implies that, when considering a specific task, the network's connectivity contains a component which is related to the task and another component which can be considered r
Externí odkaz:
http://arxiv.org/abs/1909.04358
Recurrent neural networks have been extensively studied in the context of neuroscience and machine learning due to their ability to implement complex computations. While substantial progress in designing effective learning algorithms has been achieve
Externí odkaz:
http://arxiv.org/abs/1809.02386
Publikováno v:
Neuron Volume 99, Issue 3, 8 August 2018, Pages 609-623.e29
Large scale neural recordings have established that the transformation of sensory stimuli into motor outputs relies on low-dimensional dynamics at the population level, while individual neurons exhibit complex selectivity. Understanding how low-dimen
Externí odkaz:
http://arxiv.org/abs/1711.09672
Publikováno v:
PLOS Computational Biology 13(4): e1005498 (2017)
Recurrent networks of non-linear units display a variety of dynamical regimes depending on the structure of their synaptic connectivity. A particularly remarkable phenomenon is the appearance of strongly fluctuating, chaotic activity in networks of d
Externí odkaz:
http://arxiv.org/abs/1605.04221
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