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Context-modulation of hippocampal dynamics and deep convolutional networks
[article]
2017
arXiv
pre-print
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We implement this concept in a deep artificial neural network by enabling a context-sensitive bias. The motivation for this is to improve performance of a size-constrained network. ...
Here, we introduce a mechanism by which direct and indirect pathways from cortex to the CA3 region of the hippocampus can balance both contextual gating of memory formation and driving network activity ...
Sandia National Laboratories is a multiprogram laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC, for the U.S. ...
arXiv:1711.09876v1
fatcat:gaa5ey6qqfgndfai7x6gvo7ote
A deep network-based model of hippocampal memory functions under normal and Alzheimer's disease conditions
[article]
2021
bioRxiv
pre-print
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The proposed network architecture has two key modules: 1) an autoencoder module which represents the forward and backward projections of the cortico-hippocampal projections and 2) a module that computes ...
We present a deep network based model of the associative memory functions of the hippocampus. ...
In this paper, we present a deep network-based model of hippocampal memory functions. ...
doi:10.1101/2021.01.31.429076
fatcat:nhvzf5ojvfclvj33sh5uzby2rm
Authors List
2020
2020 National Conference on Communications (NCC)
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Evaluation
and Post-Challenge Improvements
Niladri Puhan
Twin Deep Convolutional Neural Network Based Cross-spectral
Periocular Recognition
Naveen Gupta
P
Palani Maheswaran
On Multi RF Chain ...
Dynamic Routing and Spectrum Allocation in Elastic Optical Networks
with Minimal Disruption
Sagar Deep Deb
Frontal Facial Expression Recognition Using Parallel CNN Model
Samarjeet Das
Synthesis ...
doi:10.1109/ncc48643.2020.9056032
fatcat:tsdhbqblujfwlgf4ojr6ftqdhe
Free recall and recognition in a network model of the hippocampus: simulating effects of scopolamine on human memory function
1997
Behavioural Brain Research
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Free recall and recognition are simulated in a network model of the hippocampal formation, incorporating simplified simulations of neurons, synaptic connections, and the effects of acetylcholine. ...
the subsequent reactivation of context attractor states by item input (spared recognition). ...
Acknowledgements Supported by an NIMH award R29 MH52732-01, an Office of Naval Research Young Investigator Award N00014-93-1-0595, and the Human Frontier Science Program. ...
doi:10.1016/s0166-4328(97)00048-x
pmid:9475612
fatcat:wfhnv2pgmjcf3kvr6jil6rark4
Propagation of Hippocampal Ripples to the Neocortex by Way of a Subiculum-Retrosplenial Pathway
[article]
2020
bioRxiv
pre-print
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subclasses of hippocampal SPW-Rs according to ensemble activity patterns in CA1. ...
Using silicon probe recordings in awake, head-fixed mice, we show the existence of SPW-R analogues in gRSC and demonstrate their coupling to hippocampal SPW-Rs. gRSC neurons reliably distinguished different ...
Network Homeostasis and State Dynamics of Neocortical Sleep. Neuron 90, 839-852 (2016). 59. Kim, Y. & Spruston, N. ...
doi:10.1101/2020.02.27.966770
fatcat:uvqgs3enznhnlkauxhdtvbyeva
Breathing Coordinates Limbic Network Dynamics Underlying Memory Consolidation
2018
Social Science Research Network
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Lu for assistance in the experiments and all the members of the Sirota laboratory for helpful discussions and comments on the manuscript. ...
Blanco Hernandez and E. Resnik for valuable input, R. Ahmed for technical assistance, J. ...
Hippocampal network dynamics are modulated by breathing From the results so far, it is clear that hippocampal neuronal activity is massively modulated by breathing, by means of entorhinal inputs to the ...
doi:10.2139/ssrn.3283711
fatcat:wngdkknmrfh5domu6a6i4kxhzi
Breathing coordinates limbic network dynamics underlying memory consolidation
[article]
2018
bioRxiv
pre-print
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as hippocampal ripples and cortical UP and DOWN states, involved in memory consolidation. ...
support the widespread synchronization of brain regions and the relationship of neuronal dynamics with other bodily rhythms, such as breathing. ...
Lu for assistance in the experiments and all the members of the Sirota laboratory for helpful discussions and comments on the manuscript. ...
doi:10.1101/392530
fatcat:wh3dyewlojh5fb6mtg4yilkey4
MapNet: An Allocentric Spatial Memory for Mapping Environments
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
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The map itself is a 2.5D representation of an environment storing information that a deep neural network module learns to distill from RGBD input. ...
In this paper, we develop a differentiable module that satisfies such requirements, while being robust, efficient, and suitable for integration in end-to-end deep networks. ...
The authors acknowledge the generous support of ERC 677195-IDIU. ...
doi:10.1109/cvpr.2018.00884
dblp:conf/cvpr/HenriquesV18
fatcat:ysaulzcqzrbqnornilseiofjoq
Cortico-Hippocampal Computational Modeling Using Quantum-Inspired Neural Networks
2020
Frontiers in Computational Neuroscience
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Many current computational models that aim to simulate cortical and hippocampal modules of the brain depend on artificial neural networks. ...
This cortico-hippocampal computational quantum-inspired (CHCQI) model simulates cortical and hippocampal modules by using adaptively updated neural networks entangled with quantum circuits. ...
TMA, TA, and AA encouraged MK to investigate quantum theory and quantum computations. JW, QZ, and RX supervised the findings of this work. ...
doi:10.3389/fncom.2020.00080
pmid:33224031
pmcid:PMC7674175
fatcat:or6hvkhdavar5gbp6plvyyawhq
Towards continual task learning in artificial neural networks: current approaches and insights from neuroscience
[article]
2021
arXiv
pre-print
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In parallel, the ability of artificial neural networks (ANNs) to learn across a range of tasks and domains, combining and re-using learned representations where required, is a clear goal of artificial ...
The innate capacity of humans and other animals to learn a diverse, and often interfering, range of knowledge and skills throughout their lifespan is a hallmark of natural intelligence, with obvious evolutionary ...
Although
AdaNet has not been tested exhaustively in the context of continual learning, it represents an ap-
pealing method of dynamically reconfiguring the network to mitigate catastrophic forgetting with ...
arXiv:2112.14146v1
fatcat:xu3a3blkxrhkvmutosnwrlalum
Lifelong Learning of Spatiotemporal Representations with Dual-Memory Recurrent Self-Organization
[article]
2018
Frontiers in Neurorobotics
accepted
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The architecture comprises two growing recurrent networks with the complementary tasks of learning object instances (episodic memory) and categories (semantic memory). ...
The ability to learn from continuous streams of information is referred to as lifelong learning and represents a long-standing challenge for neural network models due to catastrophic forgetting. ...
The authors would like to thank Pablo Barros and Vadym Gryshchuk for technical support. ...
doi:10.3389/fnbot.2018.00078
pmid:30546302
pmcid:PMC6279894
arXiv:1805.10966v4
fatcat:ou5sjdf6gbfzrjx5vojuyrnuca
Quantifying phase–amplitude coupling in neuronal network oscillations
2011
Progress in Biophysics and Molecular Biology
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of prefrontal cortical theta phase modulating hippocampal gamma power. ...
Neuroscience time series data from a range of techniques and species reveal complex, non-linear interactions between different frequencies of neuronal network oscillations within and across brain regions ...
LFP data were kindly provided by Hannah Chandler (University of Bristol, Department of Physiology and Pharmacology); MWJ thanks the MRC, BBSRC and The Wellcome Trust for financial support of related experimental ...
doi:10.1016/j.pbiomolbio.2010.09.007
pmid:20869387
fatcat:d2lm7iyydbhf7er6lleuwdstf4
Computational Modeling of Prefrontal Cortex for Meta-Cognition of a Humanoid Robot
2020
IEEE Access
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The main components of the system are composed of several computational modules including dorsolateral, ventrolateral, anterior, and medial prefrontal regions. ...
Computational mechanisms are mainly placed on the bio-physical plausible neural structures embodied in different dynamics. ...
such as a convolutional neural network (CNN), recurrent neural network structures like long-short term memory (LSTM) and reinforcement deep learning model as deep Q-network (DQN). ...
doi:10.1109/access.2020.2998396
fatcat:p37fba6frbdbzkbrdepllwauwi
2020 Index IEEE/ACM Transactions on Computational Biology and Bioinformatics Vol. 17
2021
IEEE/ACM Transactions on Computational Biology & Bioinformatics
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P Pain Classification of Patients with Coronary Microvascular Dysfunction. ...
-Oct. 2020 1741-1750
Convolutional neural networks
Deep Learning for Automated Feature Discovery and Classification of Sleep
Stages. Sokolovsky, M., +, TCBB Nov. ...
., +, TCBB March-April 2020 599-607
Convolutional neural nets
Combining High Speed ELM Learning with a Deep Convolutional Neu-
ral Network Feature Encoding for Predicting Protein-RNA Interactions. ...
doi:10.1109/tcbb.2020.3047571
fatcat:x3kmrpexsve6bnjtd3dh6ntkyy
What Learning Systems do Intelligent Agents Need? Complementary Learning Systems Theory Updated
2016
Trends in Cognitive Sciences
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Hippocampal Replay A wealth of evidence demonstrates that replay of recent experiences occurs during offline periods (e.g., during sleep, rest) [2,3]. ...
Drawing on earlier ideas by David Marr [9] , it offered a synthesis of the computational functions and characteristics of the hippocampus and neocortex that not only accounted for a wealth of empirical ...
Acknowledgments We are very grateful to Adam Cain for help with creating the figures and Greg Wayne and Nikolaus Kriegeskorte for comments on an earlier version of the paper. ...
doi:10.1016/j.tics.2016.05.004
pmid:27315762
fatcat:322rl7plsjhsnkwjx7cclyafoe
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