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pro vyhledávání: '"Sharpee, Tatyana O."'
Akademický článek
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Using the diagrammatic method, we derive a set of self-consistent equations that describe eigenvalue distributions of large correlated asymmetric random matrices. The matrix elements can have different variances and be correlated with each other. The
Externí odkaz:
http://arxiv.org/abs/1610.09353
Publikováno v:
Phys. Rev. E 93, 022302 (2016)
Using a generalized random recurrent neural network model, and by extending our recently developed mean-field approach [J. Aljadeff, M. Stern, T. Sharpee, Phys. Rev. Lett. 114, 088101 (2015)], we study the relationship between the network connectivit
Externí odkaz:
http://arxiv.org/abs/1509.02546
Akademický článek
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Computation in the brain involves multiple types of neurons, yet the organizing principles for how these neurons work together remain unclear. Information theory has offered explanations for how different types of neurons can optimize the encoding of
Externí odkaz:
http://arxiv.org/abs/1409.2604
Publikováno v:
Phys. Rev. Lett. 114, 088101 (2015)
In neural circuits, statistical connectivity rules strongly depend on neuronal type. Here we study dynamics of neural networks with cell-type specific connectivity by extending the dynamic mean field method, and find that these networks exhibit a pha
Externí odkaz:
http://arxiv.org/abs/1407.2297
Several types of biological networks have recently been shown to be accurately described by a maximum entropy model with pairwise interactions, also known as the Ising model. Here we present an approach for finding the optimal mappings between input
Externí odkaz:
http://arxiv.org/abs/0909.0700
Autor:
Sharpee, Tatyana O.
This paper compares a family of methods for characterizing neural feature selectivity with natural stimuli in the framework of the linear-nonlinear model. In this model, the neural firing rate is a nonlinear function of a small number of relevant sti
Externí odkaz:
http://arxiv.org/abs/0801.0311