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pro vyhledávání: '"Pillow, Jonathan W"'
Animals adjust their behavioral response to sensory input adaptively depending on past experiences. The flexible brain computation is crucial for survival and is of great interest in neuroscience. The nematode C. elegans modulates its navigation beha
Externí odkaz:
http://arxiv.org/abs/2311.07117
Publikováno v:
Transactions on Machine Learning Research (2023)
Latent linear dynamical systems with Bernoulli observations provide a powerful modeling framework for identifying the temporal dynamics underlying binary time series data, which arise in a variety of contexts such as binary decision-making and discre
Externí odkaz:
http://arxiv.org/abs/2303.02060
Imaging neural activity in a behaving animal presents unique challenges in part because motion from an animal's movement creates artifacts in fluorescence intensity time-series that are difficult to distinguish from neural signals of interest. One ap
Externí odkaz:
http://arxiv.org/abs/2204.12595
Publikováno v:
Neural Computation (2024), 36 (3): 437-474
Active learning seeks to reduce the amount of data required to fit the parameters of a model, thus forming an important class of techniques in modern machine learning. However, past work on active learning has largely overlooked latent variable model
Externí odkaz:
http://arxiv.org/abs/2202.13426
Approximate Bayesian inference methods provide a powerful suite of tools for finding approximations to intractable posterior distributions. However, machine learning applications typically involve selecting actions, which -- in a Bayesian setting --
Externí odkaz:
http://arxiv.org/abs/2201.03128
A key challenge in understanding the sensory transformations of the visual system is to obtain a highly predictive model of responses from visual cortical neurons. Deep neural networks (DNNs) provide a promising candidate for such a model. However, D
Externí odkaz:
http://arxiv.org/abs/2006.11412
An open question in systems and computational neuroscience is how neural circuits accumulate evidence towards a decision. Fitting models of decision-making theory to neural activity helps answer this question, but current approaches limit the number
Externí odkaz:
http://arxiv.org/abs/2001.04571
Autor:
Keeley, Stephen L., Zoltowski, David M., Yu, Yiyi, Yates, Jacob L., Smith, Spencer L., Pillow, Jonathan W.
Gaussian Process Factor Analysis (GPFA) has been broadly applied to the problem of identifying smooth, low-dimensional temporal structure underlying large-scale neural recordings. However, spike trains are non-Gaussian, which motivates combining GPFA
Externí odkaz:
http://arxiv.org/abs/1906.03318
Autor:
Lu, Qihong, Chen, Po-Hsuan, Pillow, Jonathan W., Ramadge, Peter J., Norman, Kenneth A., Hasson, Uri
Different neural networks trained on the same dataset often learn similar input-output mappings with very different weights. Is there some correspondence between these neural network solutions? For linear networks, it has been shown that different in
Externí odkaz:
http://arxiv.org/abs/1811.11684
In many problem settings, parameter vectors are not merely sparse but dependent in such a way that non-zero coefficients tend to cluster together. We refer to this form of dependency as "region sparsity." Classical sparse regression methods, such as
Externí odkaz:
http://arxiv.org/abs/1711.10058