Zobrazeno 1 - 10
of 93
pro vyhledávání: '"A. Buice, Michael"'
Autor:
Ocker, Gabriel Koch, Buice, Michael A.
Biological synaptic plasticity exhibits nonlinearities that are not accounted for by classic Hebbian learning rules. Here, we introduce a simple family of generalized nonlinear Hebbian learning rules. We study the computations implemented by their dy
Externí odkaz:
http://arxiv.org/abs/2106.15685
Partially inspired by features of computation in visual cortex, deep neural networks compute hierarchical representations of their inputs. While these networks have been highly successful in machine learning, it remains unclear to what extent they ca
Externí odkaz:
http://arxiv.org/abs/1911.07986
Autor:
Ocker, Gabriel Koch, Hu, Yu, Buice, Michael A., Doiron, Brent, Josić, Krešimir, Rosenbaum, Robert, Shea-Brown, Eric
An essential step toward understanding neural circuits is linking their structure and their dynamics. In general, this relationship can be almost arbitrarily complex. Recent theoretical work has, however, begun to identify some broad principles under
Externí odkaz:
http://arxiv.org/abs/1703.03132
A major obstacle to understanding neural coding and computation is the fact that experimental recordings typically sample only a small fraction of the neurons in a circuit. Measured neural properties are skewed by interactions between recorded neuron
Externí odkaz:
http://arxiv.org/abs/1702.00865
Publikováno v:
PLoS Computational Biology 2017;13(6):e1005583
Recent experimental advances are producing an avalanche of data on both neural connectivity and neural activity. To take full advantage of these two emerging datasets we need a framework that links them, revealing how collective neural activity arise
Externí odkaz:
http://arxiv.org/abs/1610.03828
Autor:
Buice, Michael A., Chow, Carson C.
Publikováno v:
Front. Comput. Neurosci. 7:162 (2013)
Much progress has been made in uncovering the computational capabilities of spiking neural networks. However, spiking neurons will always be more expensive to simulate compared to rate neurons because of the inherent disparity in time scales - the sp
Externí odkaz:
http://arxiv.org/abs/1310.6934
Autor:
Chow, Carson C., Buice, Michael A.
We give a pedagogical review of the application of field theoretic and path integral methods to calculate moments of the probability density function of stochastic differential equations perturbatively.
Comment: revised version
Comment: revised version
Externí odkaz:
http://arxiv.org/abs/1009.5966
Population rate or activity equations are the foundation of a common approach to modeling for neural networks. These equations provide mean field dynamics for the firing rate or activity of neurons within a network given some connectivity. The shortc
Externí odkaz:
http://arxiv.org/abs/0902.3925
Autor:
Buice, Michael A., Chow, Carson C.
The incoherent state of the Kuramoto model of coupled oscillators exhibits marginal modes in mean field theory. We demonstrate that corrections due to finite size effects render these modes stable in the subcritical case, i.e. when the population is
Externí odkaz:
http://arxiv.org/abs/0704.1650
We present an approach for the description of fluctuations that are due to finite system size induced correlations in the Kuramoto model of coupled oscillators. We construct a hierarchy for the moments of the density of oscillators that is analogous
Externí odkaz:
http://arxiv.org/abs/nlin/0612029