13 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…
Gabriel Barello, Adam S. Charles, Jonathan W. Pillow
The sparse coding model posits that the visual system has evolved to efficiently code natural stimuli using a sparse set of features from an overcomplete dictionary. The classic sparse coding model suffers from two key limitations, however: (1) computing the neural response to an image patch requires minimizing a…
Yanbo Lian, Anthony N. Burkitt
Experimental studies of grid cells in the Medial Entorhinal Cortex (MEC) have shown that they are selective to an array of spatial locations in the environment that form a hexagonal grid. However, place cells in the hippocampus are only selective to a single-location of the environment while granule cells in the…
Damien Drix, Verena V. Hafner, Michael Schmuker
Cortical neurons are silent most of the time. This sparse activity is energy efficient, and the resulting neural code has favourable properties for associative learning. Most neural models of sparse coding use some form of homeostasis to ensure that each neuron fires infrequently. But homeostatic plasticity acting on a…
Michael Beyeler, Emily Rounds, Kristofor D. Carlson, Nikil Dutt + 1 more
Supported by recent computational studies, sparse coding and dimensionality reduction are emerging as a ubiquitous coding strategy across brain regions and modalities, allowing neurons to achieve nonnegative sparse coding (NSC) by efficiently encoding high-dimensional stimulus spaces using a sparse and parts-based…
Yuwei Cui, Subutai Ahmad, Jeff Hawkins
Hierarchical temporal memory (HTM) provides a theoretical framework that models several key computational principles of the neocortex. In this paper we analyze an important component of HTM, the HTM spatial pooler (SP). The SP models how neurons learn feedforward connections and form efficient representations of the…
Rinaldo Betkiewicz, Benjamin Lindner, Martin P. Nawrot
Transformations between sensory representations are shaped by neural mechanisms at the cellular and the circuit level. In the insect olfactory system encoding of odour information undergoes a transition from a dense spatio-temporal population code in the antennal lobe to a sparse code in the mushroom body. However, the…
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…
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.…
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…
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…
Rishabh Raj, Dar Dahlen, Kyle Duyck, C. Ron Yu
The brain has a remarkable ability to recognize objects from noisy or corrupted sensory inputs. How this cognitive robustness is achieved computationally remains unknown. We present a coding paradigm, which encodes structural dependence among features of the input and transforms various forms of the same input into the…
Takashi Yoshida, Kenichi Ohki
Natural scenes sparsely activate neurons in the primary visual cortex (V1). However, whether sparsely active neurons sufficiently represent natural image contents and what proportion of neurons are involved in the natural image processing have not been revealed. We recorded the visual responses of mouse V1 neurons to…