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Binary Willshaw learning yields high synaptic capacity for long-term familiarity memory

João Sacramento, Andreas Wichert
2012 Biological cybernetics  
We investigate from a computational perspective the efficiency of the Willshaw synaptic update rule in the context of familiarity discrimination, a binary-answer, memory-related task that has been linked  ...  We find that in terms of network capacity, Willshaw learning is strongly affected by the pattern coding rates, which have to be kept fixed and very low at any time to achieve a non-zero capacity in the  ...  Melo and Diogo Rendeiro for carefully reading a preliminary version of the manuscript.  ... 
doi:10.1007/s00422-012-0488-4 pmid:22481645 fatcat:qhj4gp5jevglvfo7muft5kxtfm

Memory Capacities for Synaptic and Structural Plasticity

Andreas Knoblauch, Günther Palm, Friedrich T. Sommer
2010 Neural Computation  
Further, the review contains novel technical material: a capacity analysis of the Willshaw model that rigorously controls for the level of retrieval quality, an analysis for memories with a nonconstant  ...  We introduce fair measures for information-theoretic capacity in associative memory that also provide a theoretical benchmark.  ...  Acknowledgments We thank Sen Cheng, Marc-Oliver Gewaltig, Edgar Körner, Ursula Körner, Bartlett Mel, and Xundong Wu for helpful discussions, as well as Pentti Kanerva for his comments to an earlier version  ... 
doi:10.1162/neco.2009.08-07-588 pmid:19925281 fatcat:e6ctksmaqngbjfowjidx2zvj7a

Inhomogeneities in Heteroassociative Memories with Linear Learning Rules

David C. Sterratt, David Willshaw
2008 Neural Computation  
For a class of local learning rules, we determine the memory capacity of the model by extending previous analysis.  ...  The inhomogeneities incorporated into the model are differential input attenuation, stochastic synaptic transmission, and memories learned with varying intensity.  ...  , and to the referees for their constructive reviews.  ... 
doi:10.1162/neco.2007.08-06-301 pmid:18047408 fatcat:tsglbkzlijfkvn57zwrt3eer6i

Structural Plasticity, Effectual Connectivity, and Memory in Cortex

Andreas Knoblauch, Friedrich T. Sommer
2016 Frontiers in Neuroanatomy  
Learning and memory is commonly attributed to the modification of synaptic strengths in neuronal networks.  ...  For this we define effectual connectivity as the fraction of synaptically linked neuron pairs within a cell assembly representing a memory.  ...  ACKNOWLEDGMENTS We thank Edgar Körner, Ursula Körner, Günther Palm, and Marc-Oliver Gewaltig for many fruitful discussions.  ... 
doi:10.3389/fnana.2016.00063 pmid:27378861 pmcid:PMC4909771 fatcat:mgeiwy2qtvakrj2h7gbbljfndy

How Dendrites Affect Online Recognition Memory

Xundong E. Wu, Gabriel C. Mel, D. J. Strouse, Bartlett W. Mel, Tatiana Engel
2019 PLoS Computational Biology  
Our model provides the first normative theory that explains how dendrites increase the brain's capacity for online learning; predicts which combinations of parameter settings we should expect to find in  ...  Based on increasing evidence that dendrites act as both signaling and learning units in the brain, we developed an analytical model that relates online recognition memory capacity to roughly a dozen dendritic  ...  Acknowledgments We would like to thank Fritz Sommer for helpful discussions in the course of this work. Author Contributions Conceptualization: Xundong Wu, Bartlett W. Mel.  ... 
doi:10.1371/journal.pcbi.1006892 pmid:31050662 pmcid:PMC6527246 fatcat:luq6uzvptvg2tkc4i4v23mk6mu

Dynamics and Robustness of Familiarity Memory

J. M. Cortes, A. Greve, A. B. Barrett, M. C. W. van Rossum
2010 Neural Computation  
We show how the familiarity signal decays rapidly after stimulus presentation. For both discriminators, we calculate the capacity using mean field analysis.  ...  This sense of recognition is called familiarity memory.  ...  Acknowledgments We acknowledge Rafal Bogacz (University of Bristol) and David Donaldson (University of Stirling) for helpful discussions, the anonymous reviewers, and financial support from EPSRC (project  ... 
doi:10.1162/neco.2009.12-08-921 pmid:19842985 fatcat:2opnsx6m3fdo5mv5gfavxmfzii

Learning internal representations in an attractor neural network with analogue neurons

Daniel J Amit, Nicolas Brunel
1995 Network  
Synaptic dynamics is an unsupervised, analogue Hebbian process, but long term memory in the absence of neural activity is maintained by a refresh mechanism which on long timescales discretizes the synaptic  ...  A leaming a m t o r neural network ( U " ) with a double dynamics of neual activities and synaptic efficacies, operating on two different timescales is studied by simulations in preparation for an electronic  ...  Acknowledgments We wish to thank Stefan0 Fusi for many discussions and comments on a previous version of the manuscript.  ... 
doi:10.1088/0954-898x_6_3_004 fatcat:rm5cwjxqsvf65kq53igfx7kaca

Impact of Active Dendrites and Structural Plasticity on the Memory Capacity of Neural Tissue

Panayiota Poirazi, Bartlett W. Mel
2001 Neuron  
synaptic weights of connectionist theory and the physical substrate for long-term learning and memory in the brain.  ...  tic inputs are summed across the entire dendritic physical substrate for long-term memory.  ...  In an ex-tural basis for long-term memory links a diverse set of neuroanatomical and neurophysiological findings and tension of this idea, our proposal assigns two distinct roles to long-term potentiation  ... 
doi:10.1016/s0896-6273(01)00252-5 pmid:11301036 fatcat:2yxleme5ujd27d5uv6nofm6eay

Learning internal representations in an attractor neural network with analogue neurons

Daniel Amit†, Nicolas Brunel
1995 Network  
Synaptic dynamics is an unsupervised, analogue Hebbian process, but long term memory in the absence of neural activity is maintained by a refresh mechanism which on long timescales discretizes the synaptic  ...  A leaming a m t o r neural network ( U " ) with a double dynamics of neual activities and synaptic efficacies, operating on two different timescales is studied by simulations in preparation for an electronic  ...  Acknowledgments We wish to thank Stefan0 Fusi for many discussions and comments on a previous version of the manuscript.  ... 
doi:10.1088/0954-898x/6/3/004 fatcat:phedzgm7ebcphno5pzhfmru3lu

Extended Sparse Distributed Memory and Sequence Storage

Javier Snaider, Stan Franklin
2012 Cognitive Computation  
Sparse distributed memory (SDM) is an auto-associative memory system that stores high dimensional Boolean vectors.  ...  SDM uses the same vector for the data (word) and the location where it is stored (address).  ...  Using associative memories for sequence storage has been long studied.  ... 
doi:10.1007/s12559-012-9125-8 fatcat:jucwhia2drbwjdqlqpadhkvkxi

A Radically New Theory of how the Brain Represents and Computes with Probabilities [article]

Gerard Rinkus
2018 arXiv   pre-print
thought; rather 7) noise is a resource generated/used to cause similar inputs to map to similar codes, controlling a tradeoff between storage capacity and embedding the input space statistics in the pattern  ...  We present a radically different theory that assumes: 1) binary units; 2) only a small subset of units, i.e., a sparse distributed representation (SDR) (cell assembly), comprises any individual code; 3  ...  I'd also like to thank my colleagues at Neurithmic Systems, Greg Lesher, Jasmin Leveille, Oliver Layton, Harald Ruda, and Nick Nowak for their help developing these ideas.  ... 
arXiv:1701.07879v4 fatcat:gx6wcsityrbjfluagpmhochtym

A Radically New Theory of how the Brain Represents and Computes with Probabilities [article]

Rod Rinkus
2017 bioRxiv   pre-print
Crucially, Sparsey's code selection algorithm (CSA), used for both learning and inference, achieves this with a single pass over the weights for each successive item of a sequence, thus performing spatiotemporal  ...  codes, indirectly yielding correlation patterns.  ...  I'd also like to thank my colleagues at Neurithmic Systems, Greg Lesher, Jasmin Leveille, Oliver Layton, Harald Ruda, and Nick Nowak for their help developing these ideas.  ... 
doi:10.1101/162941 fatcat:ih4shllngrfnphtmpqsqqmikb4

A Model for Navigation in Unknown Environments Based on a Reservoir of Hippocampal Sequences [article]

Christian Leibold
2019 bioRxiv   pre-print
The model is based on a new variant of temporal difference learning and implements a simultaneous localization and mapping algorithm.  ...  a result sequences during intermittent replay periods can be decoded as spatial trajectories and improve navigation performance, which supports the functional interpretation of replay to consolidate memories  ...  Acknowledgments The author is very grateful to George Dragoi for comments on the manuscript. The author declares no conflict of interest.  ... 
doi:10.1101/2019.12.18.880583 fatcat:2wojshn2v5g4vbb4drfluof2ci

A computational theory of hippocampal function, and empirical tests of the theory

Edmund T. Rolls, Raymond P. Kesner
2006 Progress in Neurobiology  
Based on the computational proposal that CA3-CA3 autoassociative networks are important for episodic memory, it has been shown behaviorally that the CA3 supports spatial rapid one-trial learning, learning  ...  of arbitrary associations where space is a component, pattern completion, spatial short-term memory, and sequence learning by associations formed between successive items.  ...  also incorporate hetero-synaptic long-term depression to make the learning self-limiting .  ... 
doi:10.1016/j.pneurobio.2006.04.005 pmid:16781044 fatcat:hhjiojuzhzhsnbmjazrqyyag34

Selective Delay Activity in the Cortex: Phenomena and Interpretation

D. J. Amit
2003 Cerebral Cortex  
The present article does not intend to present technical progress nor recent successes in accounting for experiments, as this issue of the journal presents a rich inventory.  ...  , and training serves for the monkeys to learn the stimuli; 3. that there is no long-term coding of the stimuli.  ...  objects (as stable selective delay activity) as well as the task (long-term memory); 2. that long-term coding exists, but is innate or learned in natural experience of the animal, prior to the experiments  ... 
doi:10.1093/cercor/bhg103 pmid:14576206 fatcat:gere6ifyf5hzhck6mmn5p63o5q
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