Modeling Reconsolidation in Kernel Associative Memory

Nowicki, Dimitri; Verga, Patrick; Siegelmann, Hava T.

doi:10.1371/journal.pone.0068189

Cited by 8 publications

(5 citation statements)

References 41 publications

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“…Although cognitive and conceptual models have elucidated functional roles for reconsolidation (Blumenfeld et al, 2006 ; Siegelmann, 2008 ; Osan et al, 2011 ; Nowicki et al, 2013 ), and a simplified molecular model has been proposed to explain some aspects of reconsolidation (Zhang et al, 2010 ), synaptic models of reconsolidation have been lacking. Here we extend models of LTP to capture the results of Fonseca et al ( 2006a ), and simulate a process akin to reconsolidation in spiking leaky integrate and fire neurons.…”

Section: Introductionmentioning

confidence: 99%

A Model of Synaptic Reconsolidation

et al. 2016

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Reconsolidation of memories has mostly been studied at the behavioral and molecular level. Here, we put forward a simple extension of existing computational models of synaptic consolidation to capture hippocampal slice experiments that have been interpreted as reconsolidation at the synaptic level. The model implements reconsolidation through stabilization of consolidated synapses by stabilizing entities combined with an activity-dependent reservoir of stabilizing entities that are immune to protein synthesis inhibition (PSI). We derive a reduced version of our model to explore the conditions under which synaptic reconsolidation does or does not occur, often referred to as the boundary conditions of reconsolidation. We find that our computational model of synaptic reconsolidation displays complex boundary conditions. Our results suggest that a limited resource of hypothetical stabilizing molecules or complexes, which may be implemented by protein phosphorylation or different receptor subtypes, can underlie the phenomenon of synaptic reconsolidation.

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Section: Introductionmentioning

confidence: 99%

A Model of Synaptic Reconsolidation

et al. 2016

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“…In the case when y contains noise and the series of singular numbers of the matrix A smoothly drops to zero (with A having a high conditionality number), the problem of estimating x is called the discrete ill-posed problem (DIP) [29]. For DIP, the solution (estimate of signal x) obtained on the basis of a pseudoinversion as x* = A + y, where A + is a pseudoinverse [30], [31] is unstable and inaccurate. To overcome the instability and improve the accuracy of the solution, a regularization approach is used.…”

Section: Distributed Representations Based On Random Projections For mentioning

confidence: 99%

Neural Distributed Representations of Vector Data in Intelligent Information Technologies

Gritsenko¹,

Revunova²,

Rachkovskij³

2018

Kibern. vyčisl. teh.

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Introduction. Distributed representation (DR) of data is a form of a vector representation,where each object is represented by a set of vector components, and each vector component can belong to representations of many objects. In ordinary vector representations, the meaning of each component is defined, which cannot be said about DR. However, the similarity of RP vectors reflects the similarity of the objects they represent.DR is a neural network approach based on modeling the representation of information in the brain, resulted from ideas about a "distributed" or "holographic" representations. DRs have a large information capacity, allow the use of a rich arsenal of methods developed for vector data, scale well for processing large amounts of data, and have a number of other advantages. Methods for data transformation to DRs have been developed for data of various types -from scalar and vector to graphs.The purpose of the article is to provide an overview of a part of the work of the Department of Neural Information Processing Technologies (International Center) in the field of neural network distributed representations. The approach is a development of the ideas of Nikolai Mikhailovich Amosov and his scientific school of modeling the structure and functions of the brain.Scope. The formation of distributed representations from the original vector representations of objects using random projection is considered. With the help of the DR, it is possible to efficiently estimate the similarity of the original objects represented by numerical vectors. Gritsenko V.I., Rachkovskij D.A., Revunova E.G. ISSN 2519-2205 (Online), ISSN 0454-9910 (Print). Киб. и выч. техн. 2018. № 4 (194) 8The use of DR allows developing regularization methods for obtaining a stable solution of discrete ill-posed inverse problems, increasing the computational efficiency and accuracy of their solution, analyzing analytically the accuracy of the solution. Thus DRs allow for increasing the efficiency of information technologies applying them.Conclusions. DRs of various data types can be used to improve the efficiency and intelligence level of information technologies. DRs have been developed for both weakly structured data, such as vectors, and for complex structured representations of objects, such as sequences, graphs of knowledge-base situations (episodes), etc. Transformation of different types of data into the DR vector format allows unifying the basic information technologies of their processing and achieving good scalability with an increase in the amount of data processed.In future, distributed representations will naturally combine information on structure and semantics to create computationally efficient and qualitatively new information technologies in which the processing of relational structures from knowledge bases is performed by the similarity of their DRs. The neurobiological relevance of distributed representations opens up the possibility of creating intelligent information technologies based on them that function similarly to...

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“…Current models simulating contextual based object reconsolidation tackle this problem via conceptual modeling of memory dynamics, using the temporal evolution of context or set associations [49, 50]. In addition, simulations of sleep processes have focused mainly on memory reactivation [51, 52] and not consolidation during sleep.…”

Section: Introductionmentioning

confidence: 99%

“…The dorsoventral organization of the hippocampus and that of the proximodistal axis of the CA1 region have been largely absent in past computational models constructed from hippocampal anatomy [ 48 ], making it difficult for these models to separate the complex dynamics of object and context associations. Current models simulating contextual based object reconsolidation tackle this problem via conceptual modeling of memory dynamics, using the temporal evolution of context or set associations [ 49 , 50 ]. In addition, simulations of sleep processes have focused mainly on memory reactivation [ 51 , 52 ] and not consolidation during sleep.…”

Section: Introductionmentioning

confidence: 99%

Dorsoventral and Proximodistal Hippocampal Processing Account for the Influences of Sleep and Context on Memory (Re)consolidation: A Connectionist Model

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Fellous

2017

Computational Intelligence and Neuroscience

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The context in which learning occurs is sufficient to reconsolidate stored memories and neuronal reactivation may be crucial to memory consolidation during sleep. The mechanisms of context-dependent and sleep-dependent memory (re)consolidation are unknown but involve the hippocampus. We simulated memory (re)consolidation using a connectionist model of the hippocampus that explicitly accounted for its dorsoventral organization and for CA1 proximodistal processing. Replicating human and rodent (re)consolidation studies yielded the following results. (1) Semantic overlap between memory items and extraneous learning was necessary to explain experimental data and depended crucially on the recurrent networks of dorsal but not ventral CA3. (2) Stimulus-free, sleep-induced internal reactivations of memory patterns produced heterogeneous recruitment of memory items and protected memories from subsequent interference. These simulations further suggested that the decrease in memory resilience when subjects were not allowed to sleep following learning was primarily due to extraneous learning. (3) Partial exposure to the learning context during simulated sleep (i.e., targeted memory reactivation) uniformly increased memory item reactivation and enhanced subsequent recall. Altogether, these results show that the dorsoventral and proximodistal organization of the hippocampus may be important components of the neural mechanisms for context-based and sleep-based memory (re)consolidations.

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Modeling Reconsolidation in Kernel Associative Memory

Cited by 8 publications

References 41 publications

A Model of Synaptic Reconsolidation

A Model of Synaptic Reconsolidation

Neural Distributed Representations of Vector Data in Intelligent Information Technologies

Dorsoventral and Proximodistal Hippocampal Processing Account for the Influences of Sleep and Context on Memory (Re)consolidation: A Connectionist Model

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