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Alex Santana Dos Santos, Marcos Eduardo Valle
Autoassociative morphological memories (AMMs) are robust and computationally efficient memory models with unlimited storage capacity. In this paper, we present the max-plus and min-plus projection autoassociative morphological memories (PAMMs) as well as their compositions. Briefly, the max-plus PAMM yields the largest max-plus combination of the stored vectors which is less than or equal to the input. Dually, the vector recalled by the min-plus PAMM corresponds to the smallest min-plus combination which is larger than or equal to the input...
February 3, 2018: Neural Networks: the Official Journal of the International Neural Network Society
John R Vokey, Randall K Jamieson, Jason M Tangen, Rachel A Searston, Scott W Allen
Scarf et al. (Proc Natl Acad Sci 113(40):11272-11276, 2016) demonstrated that pigeons, as with baboons (Grainger et al. in Science 336(6078):245-248, 2012; Ziegler in Psychol Sci. , 2013), can be trained to display several behavioural hallmarks of human orthographic processing. But, Vokey and Jamieson (Psychol Sci 25(4):991-996, 2014) demonstrated that a standard, autoassociative neural network model of memory applied to pixel maps of the words and nonwords reproduces all of those results...
February 20, 2018: Animal Cognition
Dean Korošak, Marjan Slak Rupnik
Major part of a pancreatic islet is composed of β-cells that secrete insulin, a key hormone regulating influx of nutrients into all cells in a vertebrate organism to support nutrition, housekeeping or energy storage. β-cells constantly communicate with each other using both direct, short-range interactions through gap junctions, and paracrine long-range signaling. However, how these cell interactions shape collective sensing and cell behavior in islets that leads to insulin release is unknown. When stimulated by specific ligands, primarily glucose, β-cells collectively respond with expression of a series of transient Ca2+ changes on several temporal scales...
2018: Frontiers in Physiology
Raphael Y de Camargo, Renan S Recio, Marcelo B Reyes
Background: Recent research suggests that the CA3 subregion of the hippocampus has properties of both autoassociative network, due to its ability to complete partial cues, tolerate noise, and store associations between memories, and heteroassociative one, due to its ability to store and retrieve sequences of patterns. Although there are several computational models of the CA3 as an autoassociative network, more detailed evaluations of its heteroassociative properties are missing. Methods: We developed a model of the CA3 subregion containing 10,000 integrate-and-fire neurons with both recurrent excitatory and inhibitory connections, and which exhibits coupled oscillations in the gamma and theta ranges...
2018: PeerJ
Edmund T Rolls
A quantitative computational theory of the operation of the hippocampus as an episodic memory system is described. The CA3 system operates as a single attractor or autoassociation network (1) to enable rapid one-trial associations between any spatial location (place in rodents or spatial view in primates) and an object or reward and (2) to provide for completion of the whole memory during recall from any part. The theory is extended to associations between time and object or reward to implement temporal order memory, which is also important in episodic memory...
December 7, 2017: Cell and Tissue Research
Serena E Dillon, Demitra Tsivos, Michael Knight, Bryony McCann, Catherine Pennington, Anna I Shiel, Myra E Conway, Margaret A Newson, Risto A Kauppinen, Elizabeth J Coulthard
Both recognition of familiar objects and pattern separation, a process that orthogonalises overlapping events, are critical for effective memory. Evidence is emerging that human pattern separation requires dentate gyrus. Dentate gyrus is intimately connected to CA3 where, in animals, an autoassociative network enables recall of complete memories to underpin object/event recognition. Despite huge motivation to treat age-related human memory disorders, interaction between human CA3 and dentate subfields is difficult to investigate due to small size and proximity...
October 25, 2017: Scientific Reports
Qian Sun, Alaba Sotayo, Alejandro S Cazzulino, Anna M Snyder, Christine A Denny, Steven A Siegelbaum
The hippocampal CA3 region is classically viewed as a homogeneous autoassociative network critical for associative memory and pattern completion. However, recent evidence has demonstrated a striking heterogeneity along the transverse, or proximodistal, axis of CA3 in spatial encoding and memory. Here we report the presence of striking proximodistal gradients in intrinsic membrane properties and synaptic connectivity for dorsal CA3. A decreasing gradient of mossy fiber synaptic strength along the proximodistal axis is mirrored by an increasing gradient of direct synaptic excitation from entorhinal cortex...
August 2, 2017: Neuron
Chris Gorman, Anthony Robins, Alistair Knott
We present an investigation of the potential use of Hopfield networks to learn neurally plausible, distributed representations of category prototypes. Hopfield networks are dynamical models of autoassociative memory which learn to recreate a set of input states from any given starting state. These networks, however, will almost always learn states which were not presented during training, so called spurious states. Historically, spurious states have been an undesirable side-effect of training a Hopfield network and there has been much research into detecting and discarding these unwanted states...
July 2017: Neural Networks: the Official Journal of the International Neural Network Society
Ankit Kumar, Pritiraj Mohanty
Towards practical realization of brain-inspired computing in a scalable physical system, we investigate a network of coupled micromechanical oscillators. We numerically simulate this array of all-to-all coupled nonlinear oscillators in the presence of stochasticity and demonstrate its ability to synchronize and store information in the relative phase differences at synchronization. Sensitivity of behavior to coupling strength, frequency distribution, nonlinearity strength, and noise amplitude is investigated...
March 24, 2017: Scientific Reports
Nelson Rebola, Mario Carta, Christophe Mulle
The CA3 region of the hippocampus is important for rapid encoding of memory. Computational theories have proposed specific roles in hippocampal function and memory for the sparse inputs from the dentate gyrus to CA3 and for the extended local recurrent connectivity that gives rise to the CA3 autoassociative network. Recently, we have gained considerable new insight into the operation and plasticity of CA3 circuits, including the identification of novel forms of synaptic plasticity and their underlying mechanisms, and structural plasticity in the GABAergic control of CA3 circuits...
April 2017: Nature Reviews. Neuroscience
Naoki Masuyama, Chu Kiong Loo, Manjeevan Seera, Naoyuki Kubota
Quantum-inspired computing is an emerging research area, which has significantly improved the capabilities of conventional algorithms. In general, quantum-inspired hopfield associative memory (QHAM) has demonstrated quantum information processing in neural structures. This has resulted in an exponential increase in storage capacity while explaining the extensive memory, and it has the potential to illustrate the dynamics of neurons in the human brain when viewed from quantum mechanics perspective although the application of QHAM is limited as an autoassociation...
February 6, 2017: IEEE Transactions on Neural Networks and Learning Systems
Edmund T Rolls
The art of memory (ars memoriae) used since classical times includes using a well-known scene to associate each view or part of the scene with a different item in a speech. This memory technique is also known as the "method of loci." The new theory is proposed that this type of memory is implemented in the CA3 region of the hippocampus where there are spatial view cells in primates that allow a particular view to be associated with a particular object in an event or episodic memory. Given that the CA3 cells with their extensive recurrent collateral system connecting different CA3 cells, and associative synaptic modifiability, form an autoassociation or attractor network, the spatial view cells with their approximately Gaussian view fields become linked in a continuous attractor network...
May 2017: Hippocampus
Phillip Howard, Daniel W Apley, George Runger
Autoassociative neural networks (ANNs) have been proposed as a nonlinear extension of principal component analysis (PCA), which is commonly used to identify linear variation patterns in high-dimensional data. While principal component scores represent uncorrelated features, standard backpropagation methods for training ANNs provide no guarantee of producing distinct features, which is important for interpretability and for discovering the nature of the variation patterns in the data. Here, we present an alternating nonlinear PCA method, which encourages learning of distinct features in ANNs...
October 26, 2016: IEEE Transactions on Neural Networks and Learning Systems
Nolan Conaway, Kenneth J Kurtz
Since the work of Minsky and Papert ( 1969 ), it has been understood that single-layer neural networks cannot solve nonlinearly separable classifications (i.e., XOR). We describe and test a novel divergent autoassociative architecture capable of solving nonlinearly separable classifications with a single layer of weights. The proposed network consists of class-specific linear autoassociators. The power of the model comes from treating classification problems as within-class feature prediction rather than directly optimizing a discriminant function...
March 2017: Neural Computation
Phillip Howard, Daniel W Apley, George Runger
Autoassociative neural networks (ANNs) have been proposed as a nonlinear extension of principal component analysis (PCA), which is commonly used to identify linear variation patterns in high-dimensional data. While principal component scores represent uncorrelated features, standard backpropagation methods for training ANNs provide no guarantee of producing distinct features, which is important for interpretability and for discovering the nature of the variation patterns in the data. Here, we present an alternating nonlinear PCA method, which encourages learning of distinct features in ANNs...
January 2018: IEEE Transactions on Neural Networks and Learning Systems
P J W Elder, I Vargas-Baca
The frequency of the resonance of125 Te of two organo-ditellurides, R-Te-Te-R (R = 4-CH3 C6 H4 and 2-(CH3 )2 NCH2 C6 H4 ), in solution undergoes a low-field shift as the concentration of the sample increases. In sharp contrast, the resonance of a sterically hindered ditelluride (R = (C6 H5 (CH3 )2 Si)3 C) and telluric acid display the opposite effect. While the negative concentration coefficients can be explained by the change in magnetic susceptibility, the positive coefficients are consistent with autoassociation of the molecules through tellurium-centred supramolecular interactions...
November 9, 2016: Physical Chemistry Chemical Physics: PCCP
Daniel Heger, Katharina Krischer
Uncertain recognition success, unfavorable scaling of connection complexity, or dependence on complex external input impair the usefulness of current oscillatory neural networks for pattern recognition or restrict technical realizations to small networks. We propose a network architecture of coupled oscillators for pattern recognition which shows none of the mentioned flaws. Furthermore we illustrate the recognition process with simulation results and analyze the dynamics analytically: Possible output patterns are isolated attractors of the system...
August 2016: Physical Review. E
Caigen Zhou, Xiaoqin Zeng, Chaomin Luo, Huaguang Zhang
In this paper, local bipolar auto-associative memories are presented based on discrete recurrent neural networks with a class of gain type activation function. The weight parameters of neural networks are acquired by a set of inequalities without the learning procedure. The global exponential stability criteria are established to ensure the accuracy of the restored patterns by considering time delays and external inputs. The proposed methodology is capable of effectively overcoming spurious memory patterns and achieving memory capacity...
2017: IEEE Transactions on Neural Networks and Learning Systems
Silvia Viana da Silva, Matthias Georg Haberl, Pei Zhang, Philipp Bethge, Cristina Lemos, Nélio Gonçalves, Adam Gorlewicz, Meryl Malezieux, Francisco Q Gonçalves, Noëlle Grosjean, Christophe Blanchet, Andreas Frick, U Valentin Nägerl, Rodrigo A Cunha, Christophe Mulle
Synaptic plasticity in the autoassociative network of recurrent connections among hippocampal CA3 pyramidal cells is thought to enable the storage of episodic memory. Impaired episodic memory is an early manifestation of cognitive deficits in Alzheimer's disease (AD). In the APP/PS1 mouse model of AD amyloidosis, we show that associative long-term synaptic potentiation (LTP) is abolished in CA3 pyramidal cells at an early stage. This is caused by activation of upregulated neuronal adenosine A2A receptors (A2AR) rather than by dysregulation of NMDAR signalling or altered dendritic spine morphology...
2016: Nature Communications
James P Roach, Leonard M Sander, Michal R Zochowski
The brain can reproduce memories from partial data; this ability is critical for memory recall. The process of memory recall has been studied using autoassociative networks such as the Hopfield model. This kind of model reliably converges to stored patterns that contain the memory. However, it is unclear how the behavior is controlled by the brain so that after convergence to one configuration, it can proceed with recognition of another one. In the Hopfield model, this happens only through unrealistic changes of an effective global temperature that destabilizes all stored configurations...
May 2016: Physical Review. E
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