Compressed sensing, sparsity, and dimensionality in neuronal information processing and data analysis.

By Surya Ganguli, Haim Sompolinsky. 2012

297 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computational Mechanics, Electrical and Electronic Engineering

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Citations in the corpus

Re-evaluating circuit mechanisms underlying pattern separation (2019, neutral): “8 is structured so as to demix specific sparse stimulus features that have been densely encoded in the input population (Ganguli and Sompolinsky, 2012).”
Compressive Sensing and Low-Rank Libraries for Classification of Bifurcation Regimes in Nonlinear Dynamical Systems (2013, methodology): “Including additional modes, such as the shift mode [32], to capture transients between qualitatively diﬀerent ﬂow regimes has resulted in additional methods such as double POD [42], and the GaussNewton with approximated tensors (GNAT) method [13] and trust-region POD [18, 6].”
Sequential and efficient neural-population coding of complex task information (2019, unknown): “In many experiments, ours included (Figure 1A), the observed neural states appeared confined to a lower-dimensional region of the neural state space, termed the ‘‘neural manifold’’ (Aksay et al., 2003; Gallego et al., 2017; Ganguli and Sompolinsky, 2012; Hopfield and Tank, 1986; Jazayeri and Afraz, ”
On simplicity and complexity in the brave new world of large-scale neuroscience. (2015, background): “They reveal the beautiful fact that we can recover a lot of information about large objects (vectors or matrices) using surprisingly few measurements when we have seemingly weak prior information, like sparsity, or low-rank structure (see [53, 54] for reviews in a neuroscience context).”
Reinforcement learning, efficient coding, and the statistics of natural tasks (2015, background): “, [17,18]), while other research has focused on the cognitive level (e.”
Statistical mechanics of complex neural systems and high dimensional data (2013, methodology): “For readers who are more interested in applications of random projections, compressed sensing and L1 minimization to neuronal information processing and data analysis, we refer them to [30].”
Statistical Mechanics of Optimal Convex Inference in High Dimensions (2016, background): “spanning the sciences, engineering, and humanities, including neuroscience [1,2], systems biology [3], health care [4], economics [5], social science [6], and history [7].”
Deep Supervised, but Not Unsupervised, Models May Explain IT Cortical Representation (2014, background): “The representational geometry would be conserved to high precision if the measured responses sampled random dimensions of the neuronal representational space [31,32].”