scholarly journals Memory consolidation and improvement by synaptic tagging and capture in recurrent neural networks

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Jannik Luboeinski ◽  
Christian Tetzlaff
Keyword(s):  
Synaptic Plasticity ◽  
Memory Consolidation ◽  
Recurrent Neural Networks ◽  
Neural Circuits ◽  
Time Dependent ◽  
Memory Recall ◽  
Spiking Neural Network ◽  
Biological Principle ◽  
Synaptic Tagging And Capture ◽  
First Time

AbstractThe synaptic-tagging-and-capture (STC) hypothesis formulates that at each synapse the concurrence of a tag with protein synthesis yields the maintenance of changes induced by synaptic plasticity. This hypothesis provides a biological principle underlying the synaptic consolidation of memories that is not verified for recurrent neural circuits. We developed a theoretical model integrating the mechanisms underlying the STC hypothesis with calcium-based synaptic plasticity in a recurrent spiking neural network. In the model, calcium-based synaptic plasticity yields the formation of strongly interconnected cell assemblies encoding memories, followed by consolidation through the STC mechanisms. Furthermore, we show for the first time that STC mechanisms modify the storage of memories such that after several hours memory recall is significantly improved. We identify two contributing processes: a merely time-dependent passive improvement, and an active improvement during recall. The described characteristics can provide a new principle for storing information in biological and artificial neural circuits.

scholarly journals Memory consolidation and improvement by synaptic tagging and capture in recurrent neural networks

2020 ◽  
Author(s):  
Jannik Luboeinski ◽  
Christian Tetzlaff
Keyword(s):  
Neural Network ◽  
Synaptic Plasticity ◽  
Memory Consolidation ◽  
Recurrent Neural Networks ◽  
Neural Circuits ◽  
Memory Recall ◽  
Spiking Neural Network ◽  
Memory Enhancement ◽  
Biological Principle ◽  
Synaptic Tagging And Capture

AbstractThe synaptic-tagging-and-capture (STC) hypothesis formulates that at each synapse the concurrence of a tag with protein synthesis yields the maintenance of changes induced by synaptic plasticity. This hypothesis provides a biological principle underlying the synaptic consolidation of memories that is not verified for recurrent neural circuits. We developed a theoretical model integrating the mechanisms underlying the STC hypothesis with calcium-based synaptic plasticity in a recurrent spiking neural network. In the model, calcium-based synaptic plasticity yields the formation of strongly interconnected cell assemblies encoding memories, followed by consolidation through the STC mechanisms. Furthermore, we find that the STC mechanisms have an up to now undiscovered effect on memories – with the passage of time they modify the storage of memories, such that after several hours memory recall is significantly improved. This kind of memory enhancement can provide a new principle for storing information in biological and artificial neural circuits.

scholarly journals How neural circuits achieve and use stable dynamics

2019 ◽  
Author(s):  
Leo Kozachkov ◽  
Mikael Lundqvist ◽  
Jean-Jacques Slotine ◽  
Earl K. Miller
Keyword(s):  
Neural Networks ◽  
Synaptic Plasticity ◽  
Recurrent Neural Networks ◽  
Neural Circuits ◽  
Stable State ◽  
Distributed Networks ◽  
Time Varying ◽  
Multiple Sources ◽  
Hebbian Plasticity ◽  
The Brain

1AbstractThe brain consists of many interconnected networks with time-varying activity. There are multiple sources of noise and variation yet activity has to eventually converge to a stable state for its computations to make sense. We approached this from a control-theory perspective by applying contraction analysis to recurrent neural networks. This allowed us to find mechanisms for achieving stability in multiple connected networks with biologically realistic dynamics, including synaptic plasticity and time-varying inputs. These mechanisms included anti-Hebbian plasticity, synaptic sparsity and excitatory-inhibitory balance. We leveraged these findings to construct networks that could perform functionally relevant computations in the presence of noise and disturbance. Our work provides a blueprint for how to construct stable plastic and distributed networks.

Class I HDAC inhibition improves object recognition memory consolidation through BDNF/TrkB pathway in a time-dependent manner

2021 ◽  
Vol 187 ◽  
pp. 108493
Author(s):  
Gerardo Ramirez-Mejia ◽  
Elvi Gil-Lievana ◽  
Oscar Urrego-Morales ◽  
Ernesto Soto-Reyes ◽  
Federico Bermúdez-Rattoni
Keyword(s):  
Object Recognition ◽  
Recognition Memory ◽  
Memory Consolidation ◽  
Time Dependent ◽  
Class I ◽  
Dependent Manner ◽  
Hdac Inhibition ◽  
Object Recognition Memory

scholarly journals Causal role for sleep-dependent reactivation of learning-activated sensory ensembles for fear memory consolidation

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Brittany C. Clawson ◽  
Emily J. Pickup ◽  
Amy Ensing ◽  
Laura Geneseo ◽  
James Shaver ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Memory Consolidation ◽  
Critical Role ◽  
Visual Presentation ◽  
Fear Memory ◽  
Memory Recall ◽  
Visual Cue ◽  
V1 Neurons ◽  
Targeted Recombination

AbstractLearning-activated engram neurons play a critical role in memory recall. An untested hypothesis is that these same neurons play an instructive role in offline memory consolidation. Here we show that a visually-cued fear memory is consolidated during post-conditioning sleep in mice. We then use TRAP (targeted recombination in active populations) to genetically label or optogenetically manipulate primary visual cortex (V1) neurons responsive to the visual cue. Following fear conditioning, mice respond to activation of this visual engram population in a manner similar to visual presentation of fear cues. Cue-responsive neurons are selectively reactivated in V1 during post-conditioning sleep. Mimicking visual engram reactivation optogenetically leads to increased representation of the visual cue in V1. Optogenetic inhibition of the engram population during post-conditioning sleep disrupts consolidation of fear memory. We conclude that selective sleep-associated reactivation of learning-activated sensory populations serves as a necessary instructive mechanism for memory consolidation.

scholarly journals Time Dependent Gene Expression Changes in the Liver of Mice Treated with Benzene

2008 ◽  
Vol 3 ◽  
pp. BMI.S590 ◽  
Author(s):  
Han-Jin Park ◽  
Jung Hwa Oh ◽  
Seokjoo Yoon ◽  
S.V.S. Rana
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
General Purpose ◽  
Time Dependent ◽  
Expression Data ◽  
Benzene Exposure ◽  
Microarray Analyses ◽  
First Time ◽  
Affymetrix Gene Chip

Benzene is used as a general purpose solvent. Benzene metabolism starts from phenol and ends with p-benzoquinone and o-benzoquinone. Liver injury inducted by benzene still remains a toxicologic problem. Tumor related genes and immune responsive genes have been studied in patients suffering from benzene exposure. However, gene expression profiles and pathways related to its hepatotoxicity are not known. This study reports the results obtained in the liver of BALB/C mice (SLC, Inc., Japan) administered 0.05 ml/100 g body weight of 2% benzene for six days. Serum, ALT, AST and ALP were determined using automated analyzer (Fuji., Japan). Histopathological observations were made to support gene expression data. c-DNA microarray analyses were performed using Affymetrix Gene-chip system. After six days of benzene exposure, twenty five genes were down regulated whereas nineteen genes were up-regulated. These gene expression changes were found to be related to pathways of biotransformation, detoxification, apoptosis, oxidative stress and cell cycle. It has been shown for the first time that genes corresponding to circadian rhythms are affected by benzene. Results suggest that gene expression profile might serve as potential biomarkers of hepatotoxicity during benzene exposure.

Experimental observation of viscoelastic fluid–structure interactions

2017 ◽  
Vol 813 ◽  
Author(s):  
Anita A. Dey ◽  
Yahya Modarres-Sadeghi ◽  
Jonathan P. Rothstein
Keyword(s):  
Flow Instability ◽  
Reynolds Numbers ◽  
Time Dependent ◽  
Flexible Structure ◽  
Fluid Inertia ◽  
Fluid Structure Interactions ◽  
Periodic Oscillations ◽  
Fluid Structure ◽  
Dependent Growth ◽  
First Time

It is well known that when a flexible or flexibly mounted structure is placed perpendicular to the flow of a Newtonian fluid, it can oscillate due to the shedding of separated vortices. Here, we show for the first time that fluid–structure interactions can also be observed when the fluid is viscoelastic. For viscoelastic fluids, a flexible structure can become unstable in the absence of fluid inertia, at infinitesimal Reynolds numbers, due to the onset of a purely elastic flow instability. Nonlinear periodic oscillations of the flexible structure are observed and found to be coupled to the time-dependent growth and decay of viscoelastic stresses in the wake of the structure.

Neuromorphic Functional Modules of Spiking Neural Network

2021 ◽  
Vol 23 (6) ◽  
pp. 317-326
Author(s):  
E.A. Ryndin ◽  
◽  
N.V. Andreeva ◽  
V.V. Luchinin ◽  
K.S. Goncharov ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Synaptic Plasticity ◽  
Data Processing ◽  
Circuit Design ◽  
Spiking Neural Networks ◽  
Spiking Neural Network ◽  
Wide Range ◽  
Artificial Neural

In the current era, design and development of artificial neural networks exploiting the architecture of the human brain have evolved rapidly. Artificial neural networks effectively solve a wide range of common for artificial intelligence tasks involving data classification and recognition, prediction, forecasting and adaptive control of object behavior. Biologically inspired underlying principles of ANN operation have certain advantages over the conventional von Neumann architecture including unsupervised learning, architectural flexibility and adaptability to environmental change and high performance under significantly reduced power consumption due to heavy parallel and asynchronous data processing. In this paper, we present the circuit design of main functional blocks (neurons and synapses) intended for hardware implementation of a perceptron-based feedforward spiking neural network. As the third generation of artificial neural networks, spiking neural networks perform data processing utilizing spikes, which are discrete events (or functions) that take place at points in time. Neurons in spiking neural networks initiate precisely timing spikes and communicate with each other via spikes transmitted through synaptic connections or synapses with adaptable scalable weight. One of the prospective approach to emulate the synaptic behavior in hardware implemented spiking neural networks is to use non-volatile memory devices with analog conduction modulation (or memristive structures). Here we propose a circuit design for functional analogues of memristive structure to mimic a synaptic plasticity, pre- and postsynaptic neurons which could be used for developing circuit design of spiking neural network architectures with different training algorithms including spike-timing dependent plasticity learning rule. Two different circuits of electronic synapse were developed. The first one is an analog synapse with photoresistive optocoupler used to ensure the tunable conductivity for synaptic plasticity emulation. While the second one is a digital synapse, in which the synaptic weight is stored in a digital code with its direct conversion into conductivity (without digital-to-analog converter andphotoresistive optocoupler). The results of the prototyping of developed circuits for electronic analogues of synapses, pre- and postsynaptic neurons and the study of transient processes are presented. The developed approach could provide a basis for ASIC design of spiking neural networks based on CMOS (complementary metal oxide semiconductor) design technology.

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