Functional, but Not Anatomical, Separation of "what" and "when" in Prefrontal Cortex Gaussian-process Factor Analysis for Low-dimensional Single-trial Analysis of Neural Population Activity

By V. Shenoy, Maneesh Sahani, Byron M. Yu, John P. Cunningham, G. Santhanam, Stephen I. Ryu, Krishna, C. Machens, R. Romo, C. Brody and 7 more

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Citations in the corpus

Natural continual learning: success is a journey, not (just) a destination (2021, methodology): “To do this, we analyzed latent representations of the RNN activity trajectories, as is commonly done to study the collective dynamics of artificial and biological networks [15, 18, 19, 28, 51].”
Re-evaluating circuit mechanisms underlying pattern separation (2019, positive): “Moreover, recent theoretical advances may help estimate the dimensionality from noisy neural data (Machens et al., 2010; Stringer et al., 2018), or based on the local connectivity motifs of neural circuits (Recanatesi et al.”
Gaussian Process Regression Networks (2011, methodology): “In machine learning, the SLFM has also been developed as “Gaussian process factor analysis” (Yu et al., 2009), with an emphasis on time being the input (predictor) variable.”
Thalamic regulation of switching between cortical representations enables cognitive flexibility (2018, unknown): “To statistically validate these coupling filters, we randomized both neuronal labels and trial order, and used leave-one-neuron-out cross validation 60 .”
Motor cortex signals for each arm are mixed across hemispheres and neurons yet partitioned within the population response (2019, unknown): “Our results contribute to an increasingly broad set of studies reporting that neural activity related to different computations or task parameters is often separated across neural dimensions, instead of at the level of brain areas or individual neurons (Druckmann and Chklovskii, 2012; Machens et al.”
Complementary roles of dimensionality and population structure in neural computations (2020, unknown): “…often represent seemingly random mixtures of sensory and behavioral variables, especially in higher cortical areas [Churchland and Shenoy, 2007; Machens et al., 2010; Rigotti et al., 2013; Mante et al., 2013], where clear functional cell classes are often not clearly apparent [Raposo et al.,…”
Flexible sensorimotor computations through rapid reconfiguration of cortical dynamics (2018, unknown): “This strategy preserves a separable, unambiguous representation of gain and t s at the population level (Machens et al. 2010; Mante et al. 2013; Kobak et al. 2016) and provides the additional flexibility of parametric adjustments to the two parameters independently.”
Unsupervised Discovery of Demixed, Low-Dimensional Neural Dynamics across Multiple Timescales through Tensor Component Analysis (2017, methodology): “This approach, 101 which we call trial-concatenated PCA, is similar to Gaussian Process Factor Analysis (GPFA) [16], another specialized 102 technique for neural data analysis.”
How advances in neural recording affect data analysis (2011, methodology): “Considering interactions between neurons may offer some insight into the principles underlying neural activity, but there are also a number of recently developed methods that aim at providing simpler models of neural activity by assuming that the nervous system is inherently low dimensiona”