Zobrazeno 1 - 10
of 225
pro vyhledávání: '"Ostojic, Srdjan."'
Networks of excitatory and inhibitory (EI) neurons form a canonical circuit in the brain. Seminal theoretical results on dynamics of such networks are based on the assumption that synaptic strengths depend on the type of neurons they connect, but are
Externí odkaz:
http://arxiv.org/abs/2411.06802
Linearization of the dynamics of recurrent neural networks (RNNs) is often used to study their properties. The same RNN dynamics can be written in terms of the ``activations" (the net inputs to each unit, before its pointwise nonlinearity) or in term
Externí odkaz:
http://arxiv.org/abs/2309.04030
Autor:
Ostojic, Srdjan, Fusi, Stefano
One major challenge of neuroscience is finding interesting structures in a seemingly disorganized neural activity. Often these structures have computational implications that help to understand the functional role of a particular brain area. Here we
Externí odkaz:
http://arxiv.org/abs/2308.16772
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
A large body of work has suggested that neural populations exhibit low-dimensional dynamics during behavior. However, there are a variety of different approaches for modeling low-dimensional neural population activity. One approach involves latent li
Externí odkaz:
http://arxiv.org/abs/2110.09804
Autor:
Ostojic, Srdjan.
Proefschrift Universiteit van Amsterdam.
Met bibliogr., lit. opg. - Met samenvatting in het Nederlands.
Met bibliogr., lit. opg. - Met samenvatting in het Nederlands.
Externí odkaz:
http://dare.uva.nl/document/30843
Autor:
Ostojic, Srdjan, Fusi, Stefano
Publikováno v:
In Trends in Cognitive Sciences July 2024 28(7):677-690
Autor:
Jazayeri, Mehrdad, Ostojic, Srdjan
The ongoing exponential rise in recording capacity calls for new approaches for analysing and interpreting neural data. Effective dimensionality has emerged as an important property of neural activity across populations of neurons, yet different stud
Externí odkaz:
http://arxiv.org/abs/2107.04084
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