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

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Justin Lines ◽  
Kelsey Nation ◽  
Jean-Marc Fellous
Keyword(s):  
Experimental Data ◽  
Memory Consolidation ◽  
Memory Item ◽  
Neural Mechanisms ◽  
Connectionist Model ◽  
Recurrent Networks ◽  
Learning Context ◽  
Memory Reactivation ◽  
Subsequent Recall ◽  
Context Dependent

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.

scholarly journals Methods for Assessment of Memory Reactivation

2018 ◽  
Vol 30 (8) ◽  
pp. 2175-2209 ◽  
Author(s):  
Shizhao Liu ◽  
Andres D. Grosmark ◽  
Zhe Chen
Keyword(s):  
Statistical Methods ◽  
Memory Consolidation ◽  
Slow Wave Sleep ◽  
Brain Regions ◽  
Weighted Distance ◽  
Memory Reactivation ◽  
Statistical Dependency ◽  
Stored Information ◽  
Robust Statistical Methods ◽  
Population Decoding

It has been suggested that reactivation of previously acquired experiences or stored information in declarative memories in the hippocampus and neocortex contributes to memory consolidation and learning. Understanding memory consolidation depends crucially on the development of robust statistical methods for assessing memory reactivation. To date, several statistical methods have seen established for assessing memory reactivation based on bursts of ensemble neural spike activity during offline states. Using population-decoding methods, we propose a new statistical metric, the weighted distance correlation, to assess hippocampal memory reactivation (i.e., spatial memory replay) during quiet wakefulness and slow-wave sleep. The new metric can be combined with an unsupervised population decoding analysis, which is invariant to latent state labeling and allows us to detect statistical dependency beyond linearity in memory traces. We validate the new metric using two rat hippocampal recordings in spatial navigation tasks. Our proposed analysis framework may have a broader impact on assessing memory reactivations in other brain regions under different behavioral tasks.

Learning context-dependent word embeddings based on dependency parsing

2020 ◽  
Vol 19 (4) ◽  
pp. 334
Author(s):  
Ke Yan ◽  
Jie Chen ◽  
Wenhao Zhu ◽  
Xin Jin ◽  
Guannan Hu
Keyword(s):  
Word Embeddings ◽  
Dependency Parsing ◽  
Learning Context ◽  
Context Dependent

scholarly journals Neural circuits for learning context-dependent associations of stimuli

2018 ◽  
Vol 107 ◽  
pp. 48-60 ◽  
Author(s):  
Henghui Zhu ◽  
Ioannis Ch. Paschalidis ◽  
Michael E. Hasselmo
Keyword(s):  
Neural Circuits ◽  
Learning Context ◽  
Context Dependent

Interference in Learning Context‐dependent Words

1998 ◽  
Vol 18 (1) ◽  
pp. 5-25 ◽  
Author(s):  
Robert T. Elliott ◽  
Qingzong Zhang
Keyword(s):  
Learning Context ◽  
Context Dependent

scholarly journals Behavioral Tagging: A Translation of the Synaptic Tagging and Capture Hypothesis

2015 ◽  
Vol 2015 ◽  
pp. 1-21 ◽  
Author(s):  
Diego Moncada ◽  
Fabricio Ballarini ◽  
Haydée Viola
Keyword(s):  
Experimental Data ◽  
Memory Consolidation ◽  
Electrical Stimulus ◽  
Memory Formation ◽  
Synaptic Specificity ◽  
Molecular Machinery ◽  
Synaptic Changes ◽  
Plastic Changes ◽  
Synaptic Tagging And Capture ◽  
Related Proteins

Similar molecular machinery is activated in neurons following an electrical stimulus that induces synaptic changes and after learning sessions that trigger memory formation. Then, to achieve perdurability of these processes protein synthesis is required for the reinforcement of the changes induced in the network. The synaptic tagging and capture theory provided a strong framework to explain synaptic specificity and persistence of electrophysiological induced plastic changes. Ten years later, the behavioral tagging hypothesis (BT) made use of the same argument, applying it to learning and memory models. The hypothesis postulates that the formation of lasting memories relies on at least two processes: the setting of a learning tag and the synthesis of plasticity related proteins, which once captured at tagged sites allow memory consolidation. BT explains how weak events, only capable of inducing transient forms of memories, can result in lasting memories when occurring close in time with other behaviorally relevant experiences that provide proteins. In this review, we detail the findings supporting the existence of BT process in rodents, leading to the consolidation, persistence, and interference of a memory. We focus on the molecular machinery taking place in these processes and describe the experimental data supporting the BT in humans.

scholarly journals Characterizing the whiteness dependence of the Hermann and Scintillating Grid visual illusions

2019 ◽  
Author(s):  
Eric Sun
Keyword(s):  
Experimental Data ◽  
Illusion Magnitude ◽  
Visual Illusions ◽  
Neural Mechanisms ◽  
Brightness Contrast ◽  
Contrast Illusions ◽  
Significant Dependence ◽  
Wide Range ◽  
Visual Size

The Hermann Grid and the Scintillating Grid are among the most prominent brightness-contrast illusions. Perception of these illusions is sensitive to a wide range of image parameters including color, linearity of the edges, and visual size of the images. Here we characterize the influence of three prominent parameters that influence grid illusion perception--dot whiteness, line whiteness, and background whiteness. Experimental data was obtained from several volunteer groups that were tasked with scoring the magnitude of the illusion for images exhibiting different whiteness levels of these three grid elements. Analysis of the data revealed a significant dependence of illusion perception on the whiteness of grid elements. Surprisingly, illusion perception effectively disappeared after an intermediate threshold of whiteness for the dot, line, and background elements in both the Scintillating Grid and Hermann Grid. Moreover, increasing the size of elements decreased the illusion magnitude. The results of this study quantify the whiteness-dependence of brightness-contrast grid illusions and may motivate new experiments to understand the neural mechanisms that are responsible for their perception.

scholarly journals A Neural Model of Schemas and Memory Consolidation

2018 ◽  
Author(s):  
Tiffany Hwu ◽  
Jeffrey L. Krichmar
Keyword(s):  
Neural Network ◽  
Memory Consolidation ◽  
Hebbian Learning ◽  
Dynamic Environment ◽  
Neural Model ◽  
Vital Role ◽  
Learning To Learn ◽  
New Information ◽  
Context Dependent

AbstractThe ability to behave differently according to the situation is essential for survival in a dynamic environment. This requires past experiences to be encoded and retrieved alongside the contextual schemas in which they occurred. The complementary learning systems theory suggests that these schemas are acquired through gradual learning via the neocortex and rapid learning via the hippocampus. However, it has also been shown that new information matching a preexisting schema can bypass the gradual learning process and be acquired rapidly, suggesting that the separation of memories into schemas is useful for flexible learning. While there are theories of the role of schemas in memory consolidation, we lack a full understanding of the mechanisms underlying this function. For this reason, we created a biologically plausible neural network model of schema consolidation that studies several brain areas and their interactions. The model uses a rate-coded multilayer neural network with contrastive Hebbian learning to learn context-specific tasks. Our model suggests that the medial prefrontal cortex supports context-dependent behaviors by learning representations of schemas. Additionally, sparse random connections in the model from the ventral hippocampus to the hidden layers of the network gate neuronal activity depending on their involvement within the current schema, thus separating the representations of new and prior schemas. Contrastive Hebbian learning may function similarly to oscillations in the hippocampus, alternating between clamping and unclamping the output layer of the network to drive learning. Lastly, the model shows the vital role of neuromodulation, as a neuromodulatory area detects the certainty of whether new information is consistent with prior schemas and modulates the speed of memory encoding accordingly. Along with the insights that this model brings to the neurobiology of memory, it further provides a basis for creating context-dependent memories while preventing catastrophic forgetting in artificial neural networks.

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