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The vertebrate hippocampus is believed to use recurrent connectivity in area CA3 to support episodic memory recall from partial cues. This brain area also contains place cells, whose location-selective firing fields implement maps…

Neurons and Cognition · Quantitative Biology 2025-07-11 Zhaoze Wang , Ronald W. Di Tullio , Spencer Rooke , Vijay Balasubramanian

Codifying memories is one of the fundamental problems of modern Neuroscience. The functional mechanisms behind this phenomenon remain largely unknown. Experimental evidence suggests that some of the memory functions are performed by…

Neurons and Cognition · Quantitative Biology 2022-05-17 Ivan Y. Tyukin , Alexander N. Gorban , Carlos Calvo , Julia Makarova , Valeri A. Makarov

Various neurophysiological and cognitive functions are based on transferring information between spiking neurons via a complex system of synaptic connections. In particular, the capacity of presynaptic inputs to influence the postsynaptic…

Neurons and Cognition · Quantitative Biology 2018-10-30 Y. Dabaghian

The spiking activity of the hippocampal place cells plays a key role in producing and sustaining an internalized representation of the ambient space---a cognitive map. These cells do not only exhibit location-specific spiking during…

Neurons and Cognition · Quantitative Biology 2018-11-05 Andrey Babichev , Dmitriy Morozov , Yuri Dabaghian

Hippocampal place and time cells encode spatial and temporal aspects of experience. Both have the same neural substrate, but have been modeled as having different functions and mechanistic origins, place cells as continuous attractors, and…

Neurons and Cognition · Quantitative Biology 2026-04-02 Qiaorong S. Yu , Zhaoze Wang , Vijay Balasubramanian

Sequential firing of hippocampal place cells is often attributed to sequential sensory drive along a trajectory, and has also been attributed to planning and other cognitive functions. Here, we propose a mechanistic and parsimonious…

Neurons and Cognition · Quantitative Biology 2026-03-03 Xiao-Xiong Lin , Yuk-Hoi Yiu , Christian Leibold

Self-sustained, elevated neuronal activity persisting on time scales of ten seconds or longer is thought to be vital for aspects of working memory, including brain representations of real space. Continuous-attractor neural networks, one of…

Neurons and Cognition · Quantitative Biology 2020-08-19 Joseph L. Natale , H. George E. Hentschel , Ilya Nemenman

Complementary Learning Systems theory holds that intelligent agents need two learning systems. Semantic memory is encoded in the neocortex with dense, overlapping representations and acquires structured knowledge. Episodic memory is encoded…

Machine Learning · Computer Science 2025-09-03 Lucie Fontaine , Frédéric Alexandre

In this paper we explain the strikingly regular activity of the 'grid' cells in rodent dorsal medial entorhinal cortex (dMEC) and the spatially localized activity of the hippocampal place cells in CA3 and CA1 by assuming that the…

Neurons and Cognition · Quantitative Biology 2008-04-22 Andras Lorincz , Melinda Kiszlinger , Gabor Szirtes

Place cells in the rat hippocampus play a key role in creating the animal's internal representation of the world. During active navigation, these cells spike only in discrete locations, together encoding a map of the environment.…

Neurons and Cognition · Quantitative Biology 2016-03-22 Yuri Dabaghian

Hippocampal cognitive map---a neuronal representation of the spatial environment---is broadly discussed in the computational neuroscience literature for decades. More recent studies point out that hippocampus plays a major role in producing…

Neurons and Cognition · Quantitative Biology 2017-10-18 Andrey Babichev , Yuri Dabaghian

Continuous attractor networks (CANs) are widely used to model how the brain temporarily retains continuous behavioural variables via persistent recurrent activity, such as an animal's position in an environment. However, this memory…

Neural and Evolutionary Computing · Computer Science 2025-07-02 Madison Cotteret , Christopher J. Kymn , Hugh Greatorex , Martin Ziegler , Elisabetta Chicca , Friedrich T. Sommer

The hippocampus encodes space through a striking gradient of place field sizes along its dorsal-ventral axis, yet the principles generating this continuous gradient from discrete grid cell inputs remain debated. We propose a unified…

Neurons and Cognition · Quantitative Biology 2025-06-06 Shujun Zhou , Guozhang Chen

Most self-supervised learning (SSL) methods learn continuous visual representations by aligning different views of the same input, offering limited control over how information is structured across representation dimensions. In this work,…

Computer Vision and Pattern Recognition · Computer Science 2026-02-11 Kawtar Zaher , Ilyass Moummad , Olivier Buisson , Alexis Joly

The spiking activity of principal cells in mammalian hippocampus encodes an internalized neuronal representation of the ambient space---a cognitive map. Once learned, such a map enables the animal to navigate a given environment for a long…

Neurons and Cognition · Quantitative Biology 2017-10-10 Andrey Babichev , Dmitriy Morozov , Yuri Dabaghian

The hippocampus is often attributed to episodic memory formation and storage in the mammalian brain; in particular, Alme et al. showed that hippocampal area CA3 forms statistically independent representations across a large number of…

Neurons and Cognition · Quantitative Biology 2021-01-01 Bhav Jain , Sean Elliott

Biological visual systems learn from limited experience, unlike deep learning models that rely on millions of training images. What learning principles make this possible? We tested whether efficient coding, the idea that neural…

Computer Vision and Pattern Recognition · Computer Science 2026-05-20 Ananya Passi , Brian S. Robinson , Michael F. Bonner

The hippocampus supports spatial navigation by encoding cognitive maps through collective place cell activity. We model the place cell population as non-negative spatial embeddings derived from the spectral decomposition of multi-step…

Neurons and Cognition · Quantitative Biology 2025-10-28 Minglu Zhao , Dehong Xu , Deqian Kong , Wen-Hao Zhang , Ying Nian Wu

This paper introduces an efficient and robust method for discovering interpretable circuits in large language models using discrete sparse autoencoders. Our approach addresses key limitations of existing techniques, namely computational…

Computation and Language · Computer Science 2024-05-22 Charles O'Neill , Thang Bui

Can neural networks learn goal-directed behaviour using similar strategies to the brain, by combining the relationships between the current state of the organism and the consequences of future actions? Recent work has shown that recurrent…

Neurons and Cognition · Quantitative Biology 2021-01-21 Justin Jude , Matthias H. Hennig
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