Browse
Submit Data
Databases
API
Help

Dataset Information

45 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Continuous attractors for dynamic memories.

ABSTRACT: Episodic memory has a dynamic nature: when we recall past episodes, we retrieve not only their content, but also their temporal structure. The phenomenon of replay, in the hippocampus of mammals, offers a remarkable example of this temporal dynamics. However, most quantitative models of memory treat memories as static configurations, neglecting the temporal unfolding of the retrieval process. Here, we introduce a continuous attractor network model with a memory-dependent asymmetric component in the synaptic connectivity, which spontaneously breaks the equilibrium of the memory configurations and produces dynamic retrieval. The detailed analysis of the model with analytical calculations and numerical simulations shows that it can robustly retrieve multiple dynamical memories, and that this feature is largely independent of the details of its implementation. By calculating the storage capacity, we show that the dynamic component does not impair memory capacity, and can even enhance it in certain regimes.

SUBMITTER: Spalla D

PROVIDER: S-EPMC8439658 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Continuous attractors for dynamic memories.

Spalla Davide D Cornacchia Isabel Maria IM Treves Alessandro A

eLife 20210914

Episodic memory has a dynamic nature: when we recall past episodes, we retrieve not only their content, but also their temporal structure. The phenomenon of replay, in the hippocampus of mammals, offers a remarkable example of this temporal dynamics. However, most quantitative models of memory treat memories as static configurations, neglecting the temporal unfolding of the retrieval process. Here, we introduce a continuous attractor network model with a memory-dependent asymmetric component in ...[more]

PMID: 34520345

Similar Datasets

Short-term postsynaptic plasticity facilitates predictive tracking in continuous attractors

Project description: Not available

| S-EPMC10652417 | biostudies-literature

Continuous Quasi-Attractors dissolve with too much - or too little - variability.

Project description:Recent research involving bats flying in long tunnels has confirmed that hippocampal place cells can be active at multiple locations, with considerable variability in place field size and peak rate. With self-organizing recurrent networks, variability implies inhomogeneity in the synaptic weights, impeding the establishment of a continuous manifold of fixed points. Are continuous attractor neural networks still valid models for understanding spatial memory in the hippocampus, given such variability? Here, we ask what are the noise limits, in terms of an experimentally inspired parametrization of the irregularity of a single map, beyond which the notion of continuous attractor is no longer relevant. Through numerical simulations we show that (i) a continuous attractor can be approximated even when neural dynamics ultimately converge onto very few fixed points, since a quasi-attractive continuous manifold supports dynamically localized activity; (ii) excess irregularity in field size however disrupts the continuity of the manifold, while too little irregularity, with multiple fields, surprisingly prevents localized activity; and (iii) the boundaries in parameter space among these three regimes, extracted from simulations, are well matched by analytical estimates. These results lead to predict that there will be a maximum size of a 1D environment which can be retained in memory, and that the replay of spatial activity during sleep or quiet wakefulness will be for short segments of the environment.

| S-EPMC11635835 | biostudies-literature

Dynamic Organization of Hierarchical Memories.

Project description:In the brain, external objects are categorized in a hierarchical way. Although it is widely accepted that objects are represented as static attractors in neural state space, this view does not take account interaction between intrinsic neural dynamics and external input, which is essential to understand how neural system responds to inputs. Indeed, structured spontaneous neural activity without external inputs is known to exist, and its relationship with evoked activities is discussed. Then, how categorical representation is embedded into the spontaneous and evoked activities has to be uncovered. To address this question, we studied bifurcation process with increasing input after hierarchically clustered associative memories are learned. We found a "dynamic categorization"; neural activity without input wanders globally over the state space including all memories. Then with the increase of input strength, diffuse representation of higher category exhibits transitions to focused ones specific to each object. The hierarchy of memories is embedded in the transition probability from one memory to another during the spontaneous dynamics. With increased input strength, neural activity wanders over a narrower state space including a smaller set of memories, showing more specific category or memory corresponding to the applied input. Moreover, such coarse-to-fine transitions are also observed temporally during transient process under constant input, which agrees with experimental findings in the temporal cortex. These results suggest the hierarchy emerging through interaction with an external input underlies hierarchy during transient process, as well as in the spontaneous activity.

| S-EPMC5019405 | biostudies-literature

Core motifs predict dynamic attractors in combinatorial threshold-linear networks.

Project description: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 developed a detailed mathematical theory relating stable and unstable fixed points of CTLNs to graph-theoretic properties of the underlying network. Here we find that a special type of fixed points, corresponding to core motifs, are predictive of both static and dynamic attractors. Moreover, the attractors can be found by choosing initial conditions that are small perturbations of these fixed points. This motivates us to hypothesize that dynamic attractors of a network correspond to unstable fixed points supported on core motifs. We tested this hypothesis on a large family of directed graphs of size n = 5, and found remarkable agreement. Furthermore, we discovered that core motifs with similar embeddings give rise to nearly identical attractors. This allowed us to classify attractors based on structurally-defined graph families. Our results suggest that graphical properties of the connectivity can be used to predict a network's complex repertoire of nonlinear dynamics.

| S-EPMC8896682 | biostudies-literature

Mapping dynamic memories of gradually changing objects.

Project description:Our brain is able to maintain a continuously updated memory representation of objects despite changes in their appearance over time (aging faces or objects, growing trees, etc.). Although this ability is crucial for cognition and behavior, it was barely explored. Here, we investigate this memory characteristic using a protocol emulating face transformation. Observers were presented with a sequence of faces that gradually transformed over many days, from a known face (source) to a new face (target), in presentations separated by other stimuli. This practice resulted in a drastic change in the memory and recognition of the faces. Although identification of the source and older face instances was reduced, recent face instances were increasingly identified as the source and rated as highly similar to the memory of the source. Using an object perturbation method, we estimated the corresponding memory shift, showing that memory patterns shifted from the source neighborhood toward the target. Our findings suggest that memory is updated to account for object changes over time while still keeping associations with past appearances. These experimental results are broadly compatible with a recently developed model of associative memory that assumes attractor dynamics with a learning rule facilitated by novelty, shown to hold when objects change gradually over short timescales.

| S-EPMC2663989 | biostudies-literature

Cortical computations via transient attractors.

Project description:The ability of sensory networks to transiently store information on the scale of seconds can confer many advantages in processing time-varying stimuli. How a network could store information on such intermediate time scales, between typical neurophysiological time scales and those of long-term memory, is typically attributed to persistent neural activity. An alternative mechanism which might allow for such information storage is through temporary modifications to the neural connectivity which decay on the same second-long time scale as the underlying memories. Earlier work that has explored this method has done so by emphasizing one attractor from a limited, pre-defined set. Here, we describe an alternative, a Transient Attractor network, which can learn any pattern presented to it, store several simultaneously, and robustly recall them on demand using targeted probes in a manner reminiscent of Hopfield networks. We hypothesize that such functionality could be usefully embedded within sensory cortex, and allow for a flexibly-gated short-term memory, as well as conferring the ability of the network to perform automatic de-noising, and separation of input signals into distinct perceptual objects. We demonstrate that the stored information can be refreshed to extend storage time, is not sensitive to noise in the system, and can be turned on or off by simple neuromodulation. The diverse capabilities of transient attractors, as well as their resemblance to many features observed in sensory cortex, suggest the possibility that their actions might underlie neural processing in many sensory areas.

| S-EPMC5720801 | biostudies-literature

Transients and attractors in epidemics.

Project description:Historical records of childhood disease incidence reveal complex dynamics. For measles, a simple model has indicated that epidemic patterns represent attractors of a nonlinear dynamic system and that transitions between different attractors are driven by slow changes in birth rates and vaccination levels. The same analysis can explain the main features of chickenpox dynamics, but fails for rubella and whooping cough. We show that an additional (perturbative) analysis of the model, together with knowledge of the population size in question, can account for all the observed incidence patterns by predicting how stochastically sustained transient dynamics should be manifested in these systems.

| S-EPMC1691412 | biostudies-literature

Balancing robustness against the dangers of multiple attractors in a Hopfield-type model of biological attractors.

Project description:BackgroundMany chronic human diseases are of unclear origin, and persist long beyond any known insult or instigating factor. These diseases may represent a structurally normal biologic network that has become trapped within the basin of an abnormal attractor.Methodology/principal findingsWe used the Hopfield net as the archetypical example of a dynamic biological network. By progressively removing the links of fully connected Hopfield nets, we found that a designated attractor of the nets could still be supported until only slightly more than 1 link per node remained. As the number of links approached this minimum value, the rate of convergence to this attractor from an arbitrary starting state increased dramatically. Furthermore, with more than about twice the minimum of links, the net became increasingly able to support a second attractor.Conclusions/significanceWe speculate that homeostatic biological networks may have evolved to assume a degree of connectivity that balances robustness and agility against the dangers of becoming trapped in an abnormal attractor.

| S-EPMC3008716 | biostudies-literature

Dynamic metastable polymersomes enable continuous flow manufacturing.

Project description:Polymersomes are polymeric analogues of liposomes with exceptional physical and chemical properties. Despite being dubbed as next-generation vesicles since their inception nearly three decades ago, polymersomes have yet to experience translation into the clinical or industrial settings. This is due to a lack of reliable methods to upscale production without compromising control over polymersome properties. Herein we report a continuous flow methodology capable of producing near-monodisperse polymersomes at scale (≥3 g/h) with the possibility of performing downstream polymersome manipulation. Unlike conventional polymersomes, our polymersomes exhibit metastability under ambient conditions, persisting for a lifetime of ca. 7 days, during which polymersome growth occurs until a dynamic equilibrium state is reached. We demonstrate how this metastable state is key to the implementation of downstream processes to manipulate polymersome size and/or shape in the same continuous stream. The methodology operates in a plug-and-play fashion and is applicable to various block copolymers.

| S-EPMC10558441 | biostudies-literature

Continuous measurement of dynamic classroom social interactions.

Project description:Human observations can only capture a portion of ongoing classroom social activity, and are not ideal for understanding how children's interactions are spatially structured. Here we demonstrate how social interaction can be investigated by modeling automated continuous measurements of children's location and movement using a commercial system based on radio frequency identification. Continuous location data were obtained from 16 five-year-olds observed during three 1-h classroom free play observations. Illustrative coordinate mapping indicated that boys and girls tended to cluster in different physical locations in the classroom, but there was no suggestion of gender differences in children's velocity (i.e., speed of movement). To detect social interaction, we present the radial distribution function, an index of when children were in social contact at greater than chance levels. Rank-order plots indicated that children were in social contact tens to hundreds of times more with some peers than others. We illustrate the use of social ties (higher than average levels of social contact) to visualize the classroom network. Analysis of the network suggests that transitivity is a potential lens through which to examine male, female, and mixed-sex cliques. The illustrative findings suggest the validity of the new measurement approach by re-examining well-established gender segregation findings from a new perspective.

| S-EPMC11178315 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data