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Neuron-Less Neural-Like Networks with Exponential Association Capacity at Tabula Rasa [chapter]

Demian Battaglia
2009 Lecture Notes in Computer Science  
Artificial neural networks have been used as models of associative memory but their storage capacity is severely limited.  ...  The potential for such networks with exponential capacity to handle inputs with a combinatorially complex structure is finally explored with a toy-example.  ...  "neuron-less neural-like" up to "truly neural".  ... 
doi:10.1007/978-3-642-02264-7_20 fatcat:vsawvn7lg5hixcipsydyfaxgz4

Encoding innate ability through a genomic bottleneck [article]

Alexei Koulakov, Sergey Shuvaev, Anthony Zador
2021 bioRxiv   pre-print
Here we formulate the problem of innate behavioral capacity in the context of artificial neural networks in terms of lossy compression of the weight matrix.  ...  Animals are born with extensive innate behavioral capabilities, which arise from neural circuits encoded in the genome.  ...  We would like to acknowledge support from Deep Valley Labs.  ... 
doi:10.1101/2021.03.16.435261 fatcat:45fvfjz6mnh47dehmj2velz6gm

Quasi-optimized memorization and retrieval dynamics in sparsely connected neural network models

Karl E. Kürten
1990 Journal de Physique  
2014 Several network topologies with sparse connectivity suitable for information processing in neural network models are studied.  ...  On the other hand, networks with random or purely nearest neighbour interactions are not competitive candidates for the realization of associative memories.  ...  It is a pleasure to thank the von Seelen group for their hospitality at the Institute for Neuroinformatics in Bochum during several visits.  ... 
doi:10.1051/jphys:0199000510150158500 fatcat:2lmr2gec5zh5lkjeyfhiqsm2u4

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 1

Sue Denham, Panayiota Poirazi, Erik De Schutter, Karl Friston, Ho Ka Chan, Thomas Nowotny, Dongqi Han, Sungho Hong, Sophie Rosay, Tanja Wernle, Alessandro Treves, Sarah Goethals (+90 others)
2017 BMC Neuroscience  
Computational research has shown that these rhythms can be generated in purely inhibitory networks or networks with both excitatory and inhibitory neurons (E-I networks).  ...  Our preliminary results show that with learning the network is reweighted into a new structure with relatively high levels of SW (Fig. 1A) , but a fully connected pattern.  ...  These barcodes are then introduced into a tabula rasa network that has no structure.  ... 
doi:10.1186/s12868-017-0370-3 fatcat:qq2cmqlotbg7vpqlqmmcql4u5i

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3

Adam J. H. Newton, Alexandra H. Seidenstein, Robert A. McDougal, Alberto Pérez-Cervera, Gemma Huguet, Tere M-Seara, Caroline Haimerl, David Angulo-Garcia, Alessandro Torcini, Rosa Cossart, Arnaud Malvache, Kaoutar Skiker (+526 others)
2017 BMC Neuroscience  
Computational research has shown that these rhythms can be generated in purely inhibitory networks or networks with both excitatory and inhibitory neurons (E-I networks).  ...  Our preliminary results show that with learning the network is reweighted into a new structure with relatively high levels of SW (Fig. 1A) , but a fully connected pattern.  ...  These barcodes are then introduced into a tabula rasa network that has no structure.  ... 
doi:10.1186/s12868-017-0372-1 fatcat:q5x3vgivujgshmtthc6ki4fcfu

The neural basis of cognitive development: a constructivist manifesto

S R Quartz, T J Sejnowski
1997 Behavioral and Brain Sciences  
The interaction between the environment and neural growth results in a flexible type of learning: "constructive learning" minimizes the need for prespecification in accordance with recent neurobiological  ...  According to "neural constructivism," the representational features of cortex are built from the dynamic interaction between neural growth mechanisms and environmentally derived neural activity.  ...  Is neural constructivism a return to tabula rasa learning?  ... 
pmid:10097006 fatcat:ammov422u5dy7aouvbm5ezhg2e

The neural basis of cognitive development: A constructivist manifesto

Steven R. Quartz, Terrence J. Sejnowski
1997 Behavioral and Brain Sciences  
The interaction between the environment and neural growth results in a flexible type of learning: "constructive learning" minimizes the need for prespecification in accordance with recent neurobiological  ...  According to "neural constructivism," the representational features of cortex are built from the dynamic interaction between neural growth mechanisms and environmentally derived neural activity.  ...  Is neural constructivism a return to tabula rasa learning?  ... 
doi:10.1017/s0140525x97001581 fatcat:2x57otwpxrcwpklsixe3sukshu

From neuron to neural networks dynamics

B. Cessac, M. Samuelides
2007 The European Physical Journal Special Topics  
This paper presents an overview of some techniques and concepts coming from dynamical system theory and used for the analysis of dynamical neural networks models.  ...  We then consider neuron couplings, with a brief description of synapses, synaptic plasticity and learning, in a second section.  ...  Neural Networks with such binary "spin" like neurons had a great success [95] but we shall not discuss them in this paper.  ... 
doi:10.1140/epjst/e2007-00058-2 fatcat:ajxl6csjdzbrng3jrouf4b6swa

From Neuron to Neural Networks dynamics [article]

B. Cessac, M. Samuelides
2006 arXiv   pre-print
This paper presents an overview of some techniques and concepts coming from dynamical system theory and used for the analysis of dynamical neural networks models.  ...  We then consider neuron couplings, with a brief description of synapses, synaptic plasticiy and learning, in a second section.  ...  Neural Networks with such binary "spin" like neurons had a great success (90) but we shall not discuss them in this chapter.  ... 
arXiv:nlin/0609038v1 fatcat:del6kbqslza2torwpzi6rxveva

Metalearned Neural Memory [article]

Tsendsuren Munkhdalai, Alessandro Sordoni, Tong Wang, Adam Trischler
2019 arXiv   pre-print
We augment recurrent neural networks with an external memory mechanism that builds upon recent progress in metalearning.  ...  We conceptualize this memory as a rapidly adaptable function that we parameterize as a deep neural network.  ...  Therefore, we initialize the memory function tabula rasa at each task episode from a fixed random parameter set.  ... 
arXiv:1907.09720v2 fatcat:2rdr35zpwbflxe77qdyu7qtzoa

Toward an interpretation of dynamic neural activity in terms of chaotic dynamical systems

Ichiro Tsuda
2001 Behavioral and Brain Sciences  
Skeleton network for dynamic associative memory. The network consists of two blocks, I and II.  ...  Block I consists of a recurrent network of a pyramidal-type neuron and a network providing global feedback, whose strength is randomly fixed.  ...  ACKNOWLEDGMENT ACKNOWLEDGMENT We would like to express our sincere thanks to Takao Namiki for critically reading the manuscript of this response.  ... 
doi:10.1017/s0140525x01000097 fatcat:d2mpwqliqjdetnzhwbktezj4ka

The puzzle of chaotic neurodynamics

Roman Borisyuk
2001 Behavioral and Brain Sciences  
Skeleton network for dynamic associative memory. The network consists of two blocks, I and II.  ...  Block I consists of a recurrent network of a pyramidal-type neuron and a network providing global feedback, whose strength is randomly fixed.  ...  ACKNOWLEDGMENT ACKNOWLEDGMENT We would like to express our sincere thanks to Takao Namiki for critically reading the manuscript of this response.  ... 
doi:10.1017/s0140525x01240096 fatcat:gxbhb45wf5gmdjjd5usvs6ue4y

A Simple Model of Prefrontal Cortex Function in Delayed-Response Tasks

Stanislas Dehaene, Jean-Pierre Changeux
1989 Journal of Cognitive Neuroscience  
of neurons, may extend the capacities of the network to the learning of less systematic, more complex rules.  ...  It is a well-taken point for neurobiologists that learning is very unlikely to take place from a tabula rasa or a fully connected network and most certainly requires highly structured neuronal archi- tectures  ... 
doi:10.1162/jocn.1989.1.3.244 pmid:23968508 fatcat:w5vtvddkyvelbpvcecrwili2gu

On the dynamics of cortical development: synchrony and synaptic self-organization

James Joseph Wright, Paul David Bourke
2013 Frontiers in Computational Neuroscience  
It is proposed that, during embryonic development, synchronous firing of neurons and their competition for limited metabolic resources leads to selection of an array of neurons with ultra-small-world characteristics  ...  The model is provisionally extended to hierarchical interactions of the visual cortex with higher centers, and a general principle for cortical processing of spatio-temporal images is sketched.  ...  have pointed out that those species with less orderliness have smaller visual cortices and/or less defined organization of "like-tolike" connections-an argument congruent with the findings on brain size  ... 
doi:10.3389/fncom.2013.00004 pmid:23596410 pmcid:PMC3573321 fatcat:fgtn5vm5jzhhvaxieaclov4chy

Energy Efficient Sparse Connectivity from Imbalanced Synaptic Plasticity Rules

João Sacramento, Andreas Wichert, Mark C. W. van Rossum, Peter E. Latham
2015 PLoS Computational Biology  
In this work we address the canonical computational problem of storing memories with synaptic plasticity.  ...  However, instead of optimising solely for information capacity, we search for energy efficient solutions.  ...  JS is very grateful to Prof Ana Paiva for sponsoring a visit to the Institute for Adaptive and Neural Computation. Author Contributions  ... 
doi:10.1371/journal.pcbi.1004265 pmid:26046817 pmcid:PMC4457870 fatcat:zxnm3k2vhnfppb74zbgwfefbri
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