Neural correlates of sparse coding and dimensionality reduction

WARP-LCA: Efficient Convolutional Sparse Coding with Locally ...

Neural correlates of sparse coding and dimensionality reduction. PLoS computational biology, 15(6):e1006908, 2019. Davies et al. [2021] Mike ...

Bridging neuronal correlations and dimensionality reduction

Although both ap- proaches have been used to study trial-to-trial neuronal variability correlated among neurons, they are often used in ...

Sequential and efficient neural-population coding of complex task ...

Neural correlates of sparse coding and dimensionality reduction. PLoS · Comput. Biol. 15, e1006908. Bouchacourt, F., and Buschman, T.J. (2019) ...

VS265: Reading - RedwoodCenter

Sparse Coding via Thresholding and Local Competition in Neural Circuits ... A Global Geometric Framework for Nonlinear Dimensionality Reduction , Tenenbaum et al.

Inference via sparse coding in a hierarchical vision model | JOV

The work in Hosoya and Hyvärinen (2015) includes a model of V1 complex cell responses and a dimensionality reduction stage, followed by a version of independent ...

Deep Sparse Coding for Invariant Multimodal Halle Berry Neurons

rons do exist, suggesting a sparse and explicit neural code. ... We use the t-Distributed Stochastic Neighbor Embed- ding (t-SNE) [23] technique to reduce the ...

Sparse coding vs. sparse PCA, are they the same thing?

For example, are both of them for dimensionality reduction? machine-learning · pca · neuroscience · Share.

A Framework For Sparsity Promoting Dimensionality Reduction

Our algorithm performs sparse coding in the reduced space leading to a huge computational advantage, yet, is capable of taking into account the non-linearity as ...

Neural Coding and Adaptation (Lecture 1) by Adrienne Fairhall

... Neural coding" is a quest to reduce dimensionality 0:15:18 Dimensionality reduction 0:19:00 Coding 0:21:10 The weights f are a linear filter ...

SPARSE CODING FOR NEURONAL ACTIVITY

LOW-DIMENSIONAL STRUCTURES: SPARSE CODING FOR NEURONAL ACTIVITY ... reduce the dimension of the neuronal activity and express neural coding.

Sparse coding in striate and extrastriate visual cortex

Theoretical studies of mammalian cortex argue that efficient neural codes should be sparse. However, theoretical and experimental studies have used ...

A Sparse Coding Neural Network ASIC With On-Chip Learning for ...

Sparse feature encoding helps reduce the search space of the classifiers by modeling high-dimensional data as a combination of only a few active features ...

Compressed Sensing, Sparsity, and Dimensionality in Neuronal ...

reduction of sparse neural representations, and the recoding of ... Dimensionality Reduction: CS versus Efficient Coding. Efficient ...

Discrete Sparse Coding | Neural Computation - MIT Press Direct

In the definition of a DSC algorithm, we encounter the choice of latent space dimensionality, as in many other feature learning algorithms. In ...

Sparse Coding, Artificial Neural Networks, and the Brain - Demba Ba

Plenary talk of Demba Ba at Online-ICCHA2021 (https://www.univie.ac.at/projektservice-mathematik/e/index.php?event=ICCHA2021online)

Sparse coding of sensory inputs

of lower dimensional manifolds. 27. Roweis ST, Saul LK: Nonlinear dimensionality reduction by locally linear embedding. Science 2000, 290 ...

What is the best algorithm in dimension reduction for sparse binary ...

So you'd probably store a list of the row/column pairs where the 1's are. Whether that helps are not (you can really pack a full representation ...

Sparse coding with memristor networks

important feature of the sparse-coding algorithm: lateral neuron a b ... vertical bars, the input dimensionality is reduced to 9. With ...

3.14 Sparse Coding in the Neocortex

In his influential single neuron doctrine,. Barlow (1972) suggested sparseness as one of the principles important to sensory representation. However, sparse ...

The far-reaching influence of sparse coding in V1 - xcorr: AI & neuro

Introduction Olshausen and Field (1996) made a big splash in visual neurophysiology and machine learning by offering an answer to a ...