Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Park, Il Memming"'
Continuous attractors offer a unique class of solutions for storing continuous-valued variables in recurrent system states for indefinitely long time intervals. Unfortunately, continuous attractors suffer from severe structural instability in general
Externí odkaz:
http://arxiv.org/abs/2408.00109
Neural dynamical systems with stable attractor structures, such as point attractors and continuous attractors, are hypothesized to underlie meaningful temporal behavior that requires working memory. However, working memory may not support useful lear
Externí odkaz:
http://arxiv.org/abs/2308.12585
Latent Gaussian process (GP) models are widely used in neuroscience to uncover hidden state evolutions from sequential observations, mainly in neural activity recordings. While latent GP models provide a principled and powerful solution in theory, th
Externí odkaz:
http://arxiv.org/abs/2306.01802
Latent variable models have become instrumental in computational neuroscience for reasoning about neural computation. This has fostered the development of powerful offline algorithms for extracting latent neural trajectories from neural recordings. H
Externí odkaz:
http://arxiv.org/abs/2305.11278
Autor:
Brinkman, Braden A. W., Yan, Han, Maffei, Arianna, Park, Il Memming, Fontanini, Alfredo, Wang, Jin, La Camera, Giancarlo
Publikováno v:
Applied Physics Reviews (2022) 9(1), 011313
Cortical neurons emit seemingly erratic trains of action potentials or "spikes," and neural network dynamics emerge from the coordinated spiking activity within neural circuits. These rich dynamics manifest themselves in a variety of patterns, which
Externí odkaz:
http://arxiv.org/abs/2110.03025
Autor:
Pei, Felix, Ye, Joel, Zoltowski, David, Wu, Anqi, Chowdhury, Raeed H., Sohn, Hansem, O'Doherty, Joseph E., Shenoy, Krishna V., Kaufman, Matthew T., Churchland, Mark, Jazayeri, Mehrdad, Miller, Lee E., Pillow, Jonathan, Park, Il Memming, Dyer, Eva L., Pandarinath, Chethan
Advances in neural recording present increasing opportunities to study neural activity in unprecedented detail. Latent variable models (LVMs) are promising tools for analyzing this rich activity across diverse neural systems and behaviors, as LVMs do
Externí odkaz:
http://arxiv.org/abs/2109.04463
Autor:
Jordan, Ian D., Park, Il Memming
Brain dynamics can exhibit narrow-band nonlinear oscillations and multistability. For a subset of disorders of consciousness and motor control, we hypothesize that some symptoms originate from the inability to spontaneously transition from one attrac
Externí odkaz:
http://arxiv.org/abs/2004.10658
Autor:
Zhao, Yuan, Park, Il Memming
A central challenge in neuroscience is understanding how neural system implements computation through its dynamics. We propose a nonlinear time series model aimed at characterizing interpretable dynamics from neural trajectories. Our model assumes lo
Externí odkaz:
http://arxiv.org/abs/1608.06546
Autor:
Zhao, Yuan, Park, Il Memming
Publikováno v:
Neural Computation, May 2017, Vol. 29, No. 5 , Pages: 1293-1316
When governed by underlying low-dimensional dynamics, the interdependence of simultaneously recorded population of neurons can be explained by a small number of shared factors, or a low-dimensional trajectory. Recovering these latent trajectories, pa
Externí odkaz:
http://arxiv.org/abs/1604.03053
Publikováno v:
IEEE Signal Processing Magazine 30(4), 2013
Over the last decade several positive definite kernels have been proposed to treat spike trains as objects in Hilbert space. However, for the most part, such attempts still remain a mere curiosity for both computational neuroscientists and signal pro
Externí odkaz:
http://arxiv.org/abs/1302.5964