scholarly journals Continuous attractors for dynamic memories

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Davide Spalla ◽  
Isabel Maria Cornacchia ◽  
Alessandro Treves
Keyword(s):  
Storage Capacity ◽  
Temporal Dynamics ◽  
Temporal Structure ◽  
Memory Capacity ◽  
Synaptic Connectivity ◽  
Retrieval Process ◽  
Dynamic Nature ◽  
Dynamic Component ◽  
Asymmetric Component ◽  
Continuous Attractors

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.

scholarly journals Continuous attractors for dynamic memories

2020 ◽  
Author(s):  
Davide Spalla ◽  
Isabel M. Cornacchia ◽  
Alessandro Treves
Keyword(s):  
Storage Capacity ◽  
Temporal Dynamics ◽  
Temporal Structure ◽  
Memory Capacity ◽  
Synaptic Connectivity ◽  
Retrieval Process ◽  
Dynamic Nature ◽  
Dynamic Component ◽  
Asymmetric Component ◽  
Continuous Attractors

AbstractEpisodic 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, that 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 on 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.

The visual arrays task…visual storage capacity or attention control?

2019 ◽  
Author(s):  
Jessie Martin ◽  
Jason S. Tsukahara ◽  
Christopher Draheim ◽  
Zach Shipstead ◽  
Cody Mashburn ◽  
...  
Keyword(s):  
Working Memory ◽  
Storage Capacity ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
Attention Control ◽  
Data Sets ◽  
Independent Data ◽  
Visual Storage ◽  
Design Selection ◽  
The Relationship

**The uploaded manuscript is still in preparation** In this study, we tested the relationship between visual arrays tasks and working memory capacity and attention control. Specifically, we tested whether task design (selection or non-selection demands) impacted the relationship between visual arrays measures and constructs of working memory capacity and attention control. Using analyses from 4 independent data sets we showed that the degree to which visual arrays measures rely on selection influences the degree to which they reflect domain-general attention control.

scholarly journals Temporal distribution of fruit-feeding butterflies (Lepidoptera, Nymphalidae) in the eastern extreme of the Amazon region

2020 ◽  
Vol 50 (1) ◽  
pp. 12-23
Author(s):  
Elias da Costa ARAUJO ◽  
Lucas Pereira MARTINS ◽  
Marcelo DUARTE ◽  
Gisele Garcia AZEVEDO
Keyword(s):  
Temporal Dynamics ◽  
Abiotic Factors ◽  
Temporal Structure ◽  
Temporal Distribution ◽  
Amazon Region ◽  
Influential Factors ◽  
Northeastern Brazil ◽  
Amazon Forest ◽  
Dry Seasons ◽  
Species Richness And Abundance

ABSTRACT Rainfall is one of the most influential factors driving insect seasonality in the Amazon region. However, few studies have analyzed the temporal dynamics of fruit-feeding butterflies in the Brazilian Amazon, specially in its eastern portion. Here, we evaluated the diversity patterns and temporal distribution of fruit-feeding butterflies in a remnant of eastern Amazon forest in the Baixada Maranhense, northeastern Brazil. Specifically, we tested whether fruit-feeding butterflies are temporally structured and whether rainfall influences species richness and abundance. Butterflies were collected with baited traps in both the rainy and dry seasons for two consecutive years. In total, we captured 493 butterflies belonging to 28 species, 15 genera and eight tribes. Three species comprised about half of the overall abundance, and Satyrinae was the most representative subfamily. The fruit-feeding butterfly assemblage showed a strong temporal structure during the second year of sampling, but not during the first year. Species composition and richness did not differ between rainy and dry seasons, and neither abundance nor richness was influenced by rainfall. Our results indicate that seasonality is not a strong environmental filter in this region, and that other biotic and abiotic factors are probably driving the community structure. The predominance of palms in the Baixada Maranhense, which are used as host plants by larvae of several lepidopteran species (specially satyrines) and are available year-round, might have contributed to the observed patterns of temporal diversity.

scholarly journals Characteristics of sequential activity in networks with temporally asymmetric Hebbian learning

2020 ◽  
Vol 117 (47) ◽  
pp. 29948-29958
Author(s):  
Maxwell Gillett ◽  
Ulises Pereira ◽  
Nicolas Brunel
Keyword(s):  
Storage Capacity ◽  
Synaptic Weight ◽  
Hebbian Learning ◽  
Mean Field Theory ◽  
Mean Field ◽  
Learning Rule ◽  
Network Activity ◽  
Synaptic Connectivity ◽  
Random Input ◽  
Low Dimensional

Sequential activity has been observed in multiple neuronal circuits across species, neural structures, and behaviors. It has been hypothesized that sequences could arise from learning processes. However, it is still unclear whether biologically plausible synaptic plasticity rules can organize neuronal activity to form sequences whose statistics match experimental observations. Here, we investigate temporally asymmetric Hebbian rules in sparsely connected recurrent rate networks and develop a theory of the transient sequential activity observed after learning. These rules transform a sequence of random input patterns into synaptic weight updates. After learning, recalled sequential activity is reflected in the transient correlation of network activity with each of the stored input patterns. Using mean-field theory, we derive a low-dimensional description of the network dynamics and compute the storage capacity of these networks. Multiple temporal characteristics of the recalled sequential activity are consistent with experimental observations. We find that the degree of sparseness of the recalled sequences can be controlled by nonlinearities in the learning rule. Furthermore, sequences maintain robust decoding, but display highly labile dynamics, when synaptic connectivity is continuously modified due to noise or storage of other patterns, similar to recent observations in hippocampus and parietal cortex. Finally, we demonstrate that our results also hold in recurrent networks of spiking neurons with separate excitatory and inhibitory populations.

FREEMAN'S K MODELS AS RESERVOIR COMPUTING ARCHITECTURES

2009 ◽  
Vol 05 (01) ◽  
pp. 265-286
Author(s):  
MUSTAFA C. OZTURK ◽  
JOSE C. PRINCIPE
Keyword(s):  
Computational Models ◽  
Temporal Dynamics ◽  
Temporal Structure ◽  
Reservoir Computing ◽  
Short Term ◽  
System Parameters ◽  
History Of ◽  
Spatio Temporal ◽  
Universal Approximators ◽  
Theoretical Results

Walter Freeman in his classic 1975 book "Mass Activation of the Nervous System" presented a hierarchy of dynamical computational models based on studies and measurements done in real brains, which has been known as the Freeman's K model (FKM). Much more recently, liquid state machine (LSM) and echo state network (ESN) have been proposed as universal approximators in the class of functionals with exponential decaying memory. In this paper, we briefly review these models and show that the restricted K set architecture of KI and KII networks share the same properties of LSM/ESNs and is therefore one more member of the reservoir computing family. In the reservoir computing perspective, the states of the FKM are a representation space that stores in its spatio-temporal dynamics a short-term history of the input patterns. Then at any time, with a simple instantaneous read-out made up of a KI, information related to the input history can be accessed and read out. This work provides two important contributions. First, it emphasizes the need for optimal readouts, and shows how to adaptively design them. Second, it shows that the Freeman model is able to process continuous signals with temporal structure. We will provide theoretical results for the conditions on the system parameters of FKM satisfying the echo state property. Experimental results are presented to illustrate the validity of the proposed approach.

scholarly journals Seasonal temporal dynamics of marine protists communities in tidally mixed coastal waters

2021 ◽  
Author(s):  
Mariarita Caracciolo ◽  
Fabienne Rigaut-Jalabert ◽  
Sarah Romac ◽  
Frédéric Mahé ◽  
Samuel Forsans ◽  
...  
Keyword(s):  
Community Dynamics ◽  
Temporal Dynamics ◽  
Seasonal Pattern ◽  
Temporal Structure ◽  
Species Abundance ◽  
Temporal Correlation ◽  
Tidal Mixing ◽  
Taxonomic Resolution ◽  
English Channel ◽  
Turnover Rates

AbstractMajor seasonal community reorganizations and associated biomass variations are landmarks of plankton ecology. However, the processes determining marine species and community turnover rates have not been fully elucidated so far. Here, we analyse patterns of planktonic protist community succession in temperate latitudes, based on quantitative taxonomic data from both microscopy counts and ribosomal DNA metabarcoding from plankton samples collected biweekly over 8 years (2009-2016) at the SOMLIT-Astan station (Roscoff, Western English Channel). Considering the temporal structure of community dynamics (creating temporal correlation), we elucidated the recurrent seasonal pattern of the dominant species and OTUs (rDNA-derived taxa) that drive annual plankton successions. The use of morphological and molecular analyses in combination allowed us to assess absolute species abundance while improving taxonomic resolution, and revealed a greater diversity. Overall, our results underpinned a protist community characterised by a seasonal structure, which is supported by the dominant OTUs. We detected that some were partly benthic as a result of the intense tidal mixing typical of the French coasts in the English Channel. While the occurrence of these microorganisms is driven by the physical and biogeochemical conditions of the environment, internal community processes, such as the complex network of biotic interactions, also play a key role in shaping protist communities.

scholarly journals Precision validation of MIPAS-Envisat products

2007 ◽  
Vol 7 (1) ◽  
pp. 911-929 ◽  
Author(s):  
C. Piccolo ◽  
A. Dudhia
Keyword(s):  
Standard Deviation ◽  
Temporal Structure ◽  
Michelson Interferometer ◽  
Atmospheric Temperature ◽  
Retrieval Process ◽  
Full Resolution ◽  
Atmospheric Sounding ◽  
Horizontal Inhomogeneity ◽  
Temperature Retrieval ◽  
Level 2

Abstract. This paper discusses the variation and validation of the precision, or estimated random error, associated with the ESA Level 2 products from the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS). This quantity represents the propagation of the radiometric noise from the spectra through the retrieval process into the Level 2 profile values. The noise itself varies with time, steadily rising between decontamination events, but the Level 2 precision has a greater variation due to the atmospheric temperature which controls the total radiance received. Hence, for all species, the precision varies latitudinally/seasonally with temperature, with a small superimposed temporal structure determined by the degree of ice contamination on the detectors. The precision validation involves comparing two MIPAS retrievals at the intersections of ascending/descending orbits. For 5 days per month of full resolution MIPAS operation, the standard deviation of the matching profile pairs is computed and compared with the precision given in the MIPAS Level 2 data. Even taking into account the propagation of the pressure-temperature retrieval errors into the VMR retrieval, the standard deviation of the matching pairs is usually a factor 1–2 larger than the precision. This is thought to be due to effects such as horizontal inhomogeneity of the atmosphere and instability of the retrieval.

scholarly journals The separation of auditory experience and the temporal structure of MEG recorded brain activity

2017 ◽  
Author(s):  
Chris Allen
Keyword(s):  
High Frequency ◽  
Temporal Dynamics ◽  
Brain Activity ◽  
Temporal Structure ◽  
Oscillatory Activity ◽  
Auditory Stimuli ◽  
Brain Oscillations ◽  
Successful Separation ◽  
Auditory Experience ◽  
Oscillatory Brain Activity

AbstractDo brain oscillations limit the temporal dynamics of experience? This pre-registered study used the separation of auditory stimuli to track perceptual experience and related this to oscillatory activity using magnetoencephalography. The rates at which auditory stimuli could be individuated matched the rates of oscillatory brain activity. Stimuli also entrained brain activity at the frequencies at which they were presented and a progression of high frequency gamma band events appeared to predict successful separation. These findings support a generalised function for brain oscillations, across frequency bands, in the alignment of activity to delineate representations.

scholarly journals Chunking as a rational strategy for lossy data compression in visual working memory

2017 ◽  
Author(s):  
Matthew R. Nassar ◽  
Julie C. Helmers ◽  
Michael J. Frank
Keyword(s):  
Working Memory ◽  
Visual Working Memory ◽  
Performance Optimization ◽  
Human Performance ◽  
Storage Capacity ◽  
Working Memory Capacity ◽  
Quantitative Description ◽  
Memory Task ◽  
Memory Capacity ◽  
Capacity Limitations

AbstractThe nature of capacity limits for visual working memory has been the subject of an intense debate that has relied on models that assume items are encoded independently. Here we propose that instead, similar features are jointly encoded through a “chunking” process to optimize performance on visual working memory tasks. We show that such chunking can: 1) facilitate performance improvements for abstract capacity-limited systems, 2) be optimized through reinforcement, 3) be implemented by center-surround dynamics, and 4) increase effective storage capacity at the expense of recall precision. Human performance on a variant of a canonical working memory task demonstrated performance advantages, precision detriments, inter-item dependencies, and trial-to-trial behavioral adjustments diagnostic of performance optimization through center-surround chunking. Models incorporating center-surround chunking provided a better quantitative description of human performance in our study as well as in a meta-analytic dataset, and apparent differences in working memory capacity across individuals were attributable to individual differences in the implementation of chunking. Our results reveal a normative rationale for center-surround connectivity in working memory circuitry, call for re-evaluation of memory performance differences that have previously been attributed to differences in capacity, and support a more nuanced view of visual working memory capacity limitations: strategic tradeoff between storage capacity and memory precision through chunking contribute to flexible capacity limitations that include both discrete and continuous aspects.

scholarly journals Automated control of odor dynamics for neurophysiology and behavior

2021 ◽  
Author(s):  
Luis Alonso Hernandez-Nunez ◽  
Aravinthan Samuel
Keyword(s):  
Temporal Dynamics ◽  
Temporal Structure ◽  
Olfactory Receptor Neuron ◽  
Neural Response ◽  
Olfactory Stimulus ◽  
Response Dynamics ◽  
Automated Control ◽  
Olfactory Stimuli ◽  
Olfactory Systems ◽  
Photoreceptor Neurons

Animals use their olfactory systems to avoid predators, forage for food, and identify mates. Olfactory systems detect and distinguish odors by responding to the concentration, temporal dynamics, and identities of odorant molecules. Studying the temporal neural processing of odors carried in air has been difficult because of the inherent challenge in precisely controlling odorized airflows over time. Odorized airflows interact with surfaces and other air currents, leading to a complex transformation from the odorized airflow that is desired to the olfactory stimulus that is delivered. Here, we present a method that achieves precise and automated control of the amplitude, baseline, and temporal structure of olfactory stimuli. We use this technique to analyze the temporal processing of olfactory stimuli in the early olfactory circuits and navigational behavior of larval Drosophila. Precise odor control and calcium measurements in the axon terminal of an Olfactory Receptor Neuron (ORN-Or42b) revealed dynamic adaptation properties: as in vertebrate photoreceptor neurons, Or42b-ORNs display simultaneous gain-suppression and speedup of their neural response. Furthermore, we found that ORN sensitivity to changes in odor concentration decreases with odor background, but the sensitivity to odor contrast is invariant -- this causes odor-evoked ORN activity to follow the Weber-Fechner Law. Using precise olfactory stimulus control with freely-moving animals, we uncovered correlations between the temporal dynamics of larval navigation motor programs and the neural response dynamics of second-order olfactory neurons. The correspondence between neural and behavioral dynamics highlights the potential of precise odor temporal dynamics control in dissecting the sensorimotor circuits for olfactory behaviors.

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