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Search · four archives
23 papers · ranked by Valyu relevance
Yoram Burak, Ila R. Fiete, Olaf Sporns
Grid Cells Accurate Path Integration in the Grid-Cell System Authors: ['Yoram Burak' 'Ila R. Fiete' 'Olaf Sporns'] Grid cells in the rat entorhinal cortex display strikingly regular firing responses to the animal's position in 2-D space and have been hypothesized to form the neural substrate for dead-reckoning.…
Oliver Shipston‐Sharman, Lukas Solanka, Matthew F. Nolan
Neurons in the medial entorhinal cortex encode location through spatial firing fields that have a grid-like organisation. The challenge of identifying mechanisms for grid firing has been addressed through experimental and theoretical investigations of medial entorhinal circuits. Here, we discuss evidence for continuous…
Bryan C. Daniels, Marc W. Howard
Many cognitive models, including those for predicting the time of future events, can be mapped onto a particular form of neural representation in which activity across a population of neurons is restricted to manifolds that specify the Laplace transform of functions of continuous variables. These populations coding…
Yujun Li, Tianhao Chu, Si Wu
Attractor neural networks consider that neural information is stored as stationary states of a dynamical system formed by a large number of interconnected neurons. The attractor property empowers a neural system to encode information robustly, but it also incurs the difficulty of rapid update of network states, which…
Yi Ren, Zimei Chen, Hayden Deans, Ying Nian Wu + 1 more
Extensive studies suggest the brain performs Bayesian inference to infer the latent world states. It is a fundamental neuroscience question that how canonical recurrent neural circuits in the brain implement Bayesian inference. Many existing theoretical studies focused on how the recurrent circuits compute the…
Sandro Romani, Misha Tsodyks, Karl J. Friston
Continuous attractor networks are used to model the storage and representation of analog quantities, such as position of a visual stimulus. The storage of multiple continuous attractors in the same network has previously been studied in the context of self-position coding. Several uncorrelated maps of environments are…
Federico Claudi, Sarthak Chandra, Ila R. Fiete
Across the brain, circuits with continuous attractor dynamics underpin the representation and storage in memory of continuous variables for motor control, navigation, and mental computations. The represented variables have various dimensions and topologies (lines, rings, euclidean planes), and the circuits exhibit…
Mikail Khona, Ila Fiete
In this review, we describe the singular success of attractor neural network models in describing how the brain maintains persistent activity states for working memory, error-corrects, and integrates noisy cues. We consider the mechanisms by which simple and forgetful units can organize to collectively generate…
Tian, Shaoxin, Hongkai Liu, Yuying Yang + 6 more
a Yingcai Honors College, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, Sichuan, China b School of Mathematical Sciences, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, Sichuan, China…
Alexander Seeholzer, Moritz Deger, Wulfram Gerstner, Yoram Burak
Continuous attractor models of working-memory store continuous-valued information in continuous state-spaces, but are sensitive to noise processes that degrade memory retention. Short-term synaptic plasticity of recurrent synapses has previously been shown to affect continuous attractor systems: short-term facilitation…
Il Memming Park, Ábel Ságodi, Piotr Sokół
Neural dynamical systems with stable attractor structures, such as point attractors and continuous attractors, are hypothesized to underlie meaningful temporal behavior that requires short-term/working memory. However, working memory may not support useful learning signals necessary to adapt to changes in the temporal…
Ábel Ságodi, Guillermo Martín-Sánchez, Piotr Sokół, Il Memming Park
Continuous attractors offer a unique class of solutions for storing continuousvalued variables in recurrent system states for indefinitely long time intervals. Unfortunately, continuous attractors suffer from severe structural instability in general—they are destroyed by most infinitesimal changes of the dynamical law…
Amit Vinograd, Aditya Nair, Joseph H. Kim, Scott W. Linderman + 1 more
'David J. Anderson'] Continuous attractors are an emergent property of neural population dynamics that have been hypothesized to encode continuous variables such as head direction and eye position1-4. In mammals, direct evidence of neural implementation of a continuous attractor has been hindered by the challenge of…
Chen Beer, Omri Barak
Attractors play a key role in a wide range of processes including learning and memory. Due to recent innovations in recording methods, there is increasing evidence for the existence of attractor dynamics in the brain. Yet, our understanding of how these attractors emerge or disappear in a biological system is lacking.…
Mengsen Zhang, Samir Chowdhury, Manish Saggar
Characterizing large-scale dynamic organization of the brain relies on both descriptive and mechanistic modeling. The former often uses the language of statistical dependency, and the latter uses that of dynamical systems. The conceptual translation between the two is not straightforward. The present work aims to…
Joseph L. Natale, H. George E. Hentschel, Ilya Nemenman
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 the most well-known modeling frameworks for persistent activity, have been able…
Caitlyn Parmelee, Samantha Moore, Katherine Morrison, Carina Curto + 1 more
'Ivan Kryven'] Combinatorial threshold-linear networks (CTLNs) are a special class of inhibition-dominated TLNs defined from directed graphs. Like more general TLNs, they display a wide variety of nonlinear dynamics including multistability, limit cycles, quasiperiodic attractors, and chaos. In prior work, we have…
Mario Zarco, Tom Froese
A recent advance in complex adaptive systems has revealed a new unsupervised learning technique called self-modeling or self-optimization. Basically, a complex network that can form an associative memory of the state configurations of the attractors on which it converges will optimize its structure: it will…
Juliana Londono Alvarez, Katie Morrison, Carina Curto
Neural circuits in the brain perform a variety of essential functions, including input classification, pattern completion, and the generation of rhythms and oscillations that support functions such as breathing and locomotion. There is also substantial evidence that the brain encodes memories and processes information…
Eric Raman, Larry Shupe, Ryan Eaton, Eberhard Fetz
The activity and connectivity of neurons in the primate brain underlying behavior cannot yet be completely specified, but neural networks provide complete models of the connectivity and activity that performs specific tasks and provide insight into the neural computations performed by the primate brain (5). Studies of…
Amit Kahana, Lior Segev, Doron Lancet
The origin of life must have involved an unlikely transition from chaotic chemistry to reproducing supramolecular structures. Previous quantitative analyses of reproducing mutually catalytic networks made of simple molecules have led to increasing popularity of this pre-RNA scenario for life’s origin. Here, we…
Dominik Thalmeier, Marvin Uhlmann, Hilbert J. Kappen, Raoul-Martin Memmesheimer + 1 more
'Raoul-Martin Memmesheimer' 'Matthias Bethge'] Providing the neurobiological basis of information processing in higher animals, spiking neural networks must be able to learn a variety of complicated computations, including the generation of appropriate, possibly delayed reactions to inputs and the self-sustained…
Authors not listed
Real-world datasets in chemical engineering and bioengineering processes--such as those from catalytic reactors, multiphase flows, polymerization reactors, bioreactors, and clinical trials--can often be unlabelled or disorganized, rendering the training of existing supervised learning models ineffective at learning the…