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Context-modulation of hippocampal dynamics and deep convolutional networks [article]

James B. Aimone, William M. Severa
2017 arXiv   pre-print
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]

Tamizharasan Kanagamani, V. Srinivasa Chakaravarthy, Balaraman Ravindran
2021 bioRxiv   pre-print
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)  
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

Michael E Hasselmo, Bradley P Wyble
1997 Behavioural Brain Research  
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]

Noam Nitzan, Sam McKenzie, Prateep Beed, Daniel Fine English, Silvia Oldani, John Jan Tukker, György Buzsáki, Dietmar Schmitz
2020 bioRxiv   pre-print
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

Nikolas Karalis, Anton Sirota
2018 Social Science Research Network  
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]

Nikolaos Karalis, Anton Sirota
2018 bioRxiv   pre-print
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

Joao F. Henriques, Andrea Vedaldi
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  
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

Mustafa Khalid, Jun Wu, Taghreed M. Ali, Thaair Ameen, Ali Salem Altaher, Ahmed A. Moustafa, Qiuguo Zhu, Rong Xiong
2020 Frontiers in Computational Neuroscience  
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]

David McCaffary
2021 arXiv   pre-print
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]

German I. Parisi, Jun Tani, Cornelius Weber, Stefan Wermter
2018 Frontiers in Neurorobotics   accepted
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

Angela C.E. Onslow, Rafal Bogacz, Matthew W. Jones
2011 Progress in Biophysics and Molecular Biology  
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

Evren Daglarli
2020 IEEE Access  
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  
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

Dharshan Kumaran, Demis Hassabis, James L. McClelland
2016 Trends in Cognitive Sciences  
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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