15 papers · ranked by Valyu relevance
Yanbo Lian, Anthony N. Burkitt
Sparse coding, predictive coding and divisive normalization have each been found to be principles that underlie the function of neural circuits in many parts of the brain, supported by substantial experimental evidence. However, the connections between these related principles are still poorly understood. In this…
Yanbo Lian, Simon Williams, Andrew S. Alexander, Michael E. Hasselmo + 1 more
The use of spatial maps to navigate through the world requires a complex ongoing transformation of egocentric views of the environment into position within the allocentric map. Recent research has discovered neurons in retrosplenial cortex and other structures that could mediate the transformation from egocentric views…
Andrew Ligeralde, Michael R. DeWeese
It is well known that sparse coding models trained on natural images learn basis functions whose shapes resemble the receptive fields (RFs) of simple cells in the primary visual cortex (V1). However, few studies have considered how these basis functions develop during training. In particular, it is unclear whether…
Maliheh Miri, Mohammad Taghi Sadeghi, Vahid Abootalebi
Sparse representation of signals has achieved satisfactory results in classification applications compared to the conventional methods. Microarray data, which are obtained from monitoring the expression levels of thousands of genes simultaneously, have very high dimensions in relation to the small number of samples.…
Jan Homann, Hyewon Kim, David W. Tank, Michael J. Berry
A notable feature of neural activity is sparseness – namely, that only a small fraction of neurons in a local circuit have high activity at any moment. Not only is sparse neural activity observed experimentally in most areas of the brain, but sparseness has been proposed as an optimization or design principle for…
Shyam Srinivasan, Simon Daste, Mehrab Modi, Glenn Turner + 2 more
Sparse coding is thought to improve discrimination of sensory stimuli by reducing overlap between their representations. Two factors, however, can offset sparse coding’s advantages. Similar sensory stimuli have significant overlap, and responses vary across trials. To elucidate the effect of these two factors, we…
Mingyi Huang, Wei Lin, Anna Wang Roe, Yuguo Yu
Understanding how cortical neurons use dynamic firing patterns to represent sensory signals is a central challenge in neuroscience. Decades of research have shown that cortical neuronal activities exhibit high variance, typically quantified by the coefficient of variation (CV), suggesting intrinsic randomness.…
Marko A. Ruslim, Martin J. Spencer, Hinze Hogendoorn, Hamish Meffin + 2 more
Many computational studies attempt to address the question of information representation in biological neural networks using an explicit optimization based on an objective function. These approaches begin with principles of information representation that are expected to be found in the network and from which learning…
Naomi Auer, Lars Chen, Jakob Stubenrauch, Benjamin Lindner + 1 more
The brain can efficiently learn and form memories based on limited exposure to stimuli. One key factor believed to support this ability is sparse coding, which can reduce overlap between representations and minimize interference. It is well known that increased sparseness can enhance memory capacity, yet its impact on…
Oliver M. Gauld, Adam M. Packer, Lloyd E. Russell, Henry W.P. Dalgleish + 5 more
Which patterns of neural activity in sensory cortex are relevant for perceptual decision-making? To address this question, we used simultaneous two-photon calcium imaging and targeted two-photon optogenetics to probe barrel cortex activity during a perceptual discrimination task. Head-fixed mice discriminated bilateral…
Eliezyer Fermino de Oliveira, Soyoun Kim, Tian Season Qiu, Adrien Peyrache + 2 more
Low-dimensional neural manifolds are controversial in part because it is unclear how to reconcile them with high-dimensional representations observed in areas such as primary visual cortex (V1). We addressed this by recording neuronal activity in V1 during slow-wave sleep, enabling us to identify internally-generated…
S. Amin Moosavi, Antonia Pastor, Alfredo G. Ornelas, Elaine Tring + 1 more
Sparse coding enables cortical populations to represent sensory inputs efficiently, yet its temporal dynamics remain poorly understood. Consistent with theoretical predictions, we show that stimulus onset triggers broad cortical activation, initially reducing sparseness and increasing mutual information. Subsequently…
Danielle Roedel, Braden A. W. Brinkman
The sparse coding hypothesis has successfully predicted neural response properties of several sensory brain areas. For example, sparse basis representations of natural images match edge-detecting receptive fields observed in simple cells of primary visual cortex (V1), and sparse representations of natural sounds mimic…
Thach V. Bui
Neural coding is an important tool to discover the inner workings of mind. In this work, we propose and consider a simple but novel self-decoding model for neural coding based on the principle that the neuron body represents ongoing stimulus while dendrites are used to store that stimulus as a memory. In particular…
Tong Gou, Catherine A. Matulis, Damon A. Clark
Sensory systems adapt their response properties to the statistics of their inputs. For instance, visual systems adapt to low-order statistics like mean and variance to encode the stimulus efficiently or to facilitate specific downstream computations. However, it remains unclear how other statistical features affect…