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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
P J W Elder, I Vargas-Baca
The frequency of the resonance of (125)Te of two organo-ditellurides, R-Te-Te-R (R = 4-CH3C6H4 and 2-(CH3)2NCH2C6H4), 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 = (C6H5(CH3)2Si)3C) 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...
October 28, 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 (2m)ⁿ memory capacity...
August 2, 2016: 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
Rajiv K Mishra, Sooyun Kim, Segundo J Guzman, Peter Jonas
CA3-CA3 recurrent excitatory synapses are thought to play a key role in memory storage and pattern completion. Whether the plasticity properties of these synapses are consistent with their proposed network functions remains unclear. Here, we examine the properties of spike timing-dependent plasticity (STDP) at CA3-CA3 synapses. Low-frequency pairing of excitatory postsynaptic potentials (EPSPs) and action potentials (APs) induces long-term potentiation (LTP), independent of temporal order. The STDP curve is symmetric and broad (half-width ∼150 ms)...
May 13, 2016: Nature Communications
Xiaoran Jiang, Vincent Gripon, Claude Berrou, Michael Rabbat
An extension to a recently introduced architecture of clique-based neural networks is presented. This extension makes it possible to store sequences with high efficiency. To obtain this property, network connections are provided with orientation and with flexible redundancy carried by both spatial and temporal redundancies, a mechanism of anticipation being introduced in the model. In addition to the sequence storage with high efficiency, this new scheme also offers biological plausibility. In order to achieve accurate sequence retrieval, a double-layered structure combining heteroassociation and autoassociation is also proposed...
May 2016: IEEE Transactions on Neural Networks and Learning Systems
Manuel Grana, Darya Chyzhyk
Multivariate mathematical morphology (MMM) aims to extend the mathematical morphology from gray scale images to images whose pixels are high-dimensional vectors, such as remote sensing hyperspectral images and functional magnetic resonance images (fMRIs). Defining an ordering over the multidimensional image data space is a fundamental issue MMM, to ensure that ensuing morphological operators and filters are mathematically consistent. Recent approaches use the outputs of two-class classifiers to build such reduced orderings...
September 2016: IEEE Transactions on Neural Networks and Learning Systems
Edmund T Rolls
The mechanisms for pattern completion and pattern separation are described in the context of a theory of hippocampal function in which the hippocampal CA3 system operates as a single attractor or autoassociation network to enable rapid, one-trial, associations between any spatial location (place in rodents, or spatial view in primates) and an object or reward, and to provide for completion of the whole memory during recall from any part. The factors important in the pattern completion in CA3 and also a large number of independent memories stored in CA3 include: a sparse distributed representation, representations that are independent due to the randomizing effect of the mossy fibres, heterosynaptic long-term depression as well as long-term potentiation in the recurrent collateral synapses, and diluted connectivity to minimize the number of multiple synapses between any pair of CA3 neurons which otherwise distort the basins of attraction...
March 2016: Neurobiology of Learning and Memory
Brad E Pfeiffer, David J Foster
Neuronal circuits produce self-sustaining sequences of activity patterns, but the precise mechanisms remain unknown. Here we provide evidence for autoassociative dynamics in sequence generation. During sharp-wave ripple (SWR) events, hippocampal neurons express sequenced reactivations, which we show are composed of discrete attractors. Each attractor corresponds to a single location, the representation of which sharpens over the course of several milliseconds, as the reactivation focuses at that location. Subsequently, the reactivation transitions rapidly to a spatially discontiguous location...
July 10, 2015: Science
Randa Kassab, Frédéric Alexandre
Many episodic memory studies have critically implicated the hippocampus in the rapid binding of sensory information from the perception of the external environment, reported by exteroception. Other structures in the medial temporal lobe, especially the amygdala, have been more specifically linked with emotional dimension of episodic memories, reported by interoception. The hippocampal projection to the amygdala is proposed as a substrate important for the formation of extero-interoceptive associations, allowing adaptive behaviors based on past experiences...
2015: Frontiers in Systems Neuroscience
Edmund T Rolls
The recall of information stored in the hippocampus involves a series of corticocortical backprojections via the entorhinal cortex, parahippocampal gyrus, and one or more neocortical stages. Each stage is considered to be a pattern association network, with the retrieval cue at each stage the firing of neurons in the previous stage. The leading factor that determines the capacity of this multistage pattern association backprojection pathway is the number of connections onto any one neuron, which provides a quantitative basis for why there are as many backprojections between adjacent stages in the hierarchy as forward projections...
2015: Progress in Brain Research
E Lizundia, E Meaurio, J M Laza, J L Vilas, L M León Isidro
The development of thermally-sensitive poly(N-isopropylacrylamide) (PNIPAAm) and biocompatible/biodegradable poly(L-lactide) (PLLA) blends offers us an efficient strategy in order to obtain materials with improved functional properties to be used in the emerging field of biomedicine. In this sense, thermal properties of PLLA and PNIPAAm have been investigated by differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA). Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM) and wide angle X-ray diffraction (WAXD) were conducted to shed more light on the obtained results...
May 2015: Materials Science & Engineering. C, Materials for Biological Applications
Bruno J T Fernandes, George D C Cavalcanti, Tsang I Ren
Autoassociative artificial neural networks have been used in many different computer vision applications. However, it is difficult to define the most suitable neural network architecture because this definition is based on previous knowledge and depends on the problem domain. To address this problem, we propose a constructive autoassociative neural network called CANet (Constructive Autoassociative Neural Network). CANet integrates the concepts of receptive fields and autoassociative memory in a dynamic architecture that changes the configuration of the receptive fields by adding new neurons in the hidden layer, while a pruning algorithm removes neurons from the output layer...
2014: PloS One
Raymond P Kesner, Edmund T Rolls
The aims of the paper are to update Rolls' quantitative computational theory of hippocampal function and the predictions it makes about the different subregions (dentate gyrus, CA3 and CA1), and to examine behavioral and electrophysiological data that address the functions of the hippocampus and particularly its subregions. Based on the computational proposal that the dentate gyrus produces sparse representations by competitive learning and via the mossy fiber pathway forces new representations on the CA3 during learning (encoding), it has been shown behaviorally that the dentate gyrus supports spatial pattern separation during learning...
January 2015: Neuroscience and Biobehavioral Reviews
Miao Hu, Hai Li, Yiran Chen, Qing Wu, Garrett S Rose, Richard W Linderman
By mimicking the highly parallel biological systems, neuromorphic hardware provides the capability of information processing within a compact and energy-efficient platform. However, traditional Von Neumann architecture and the limited signal connections have severely constrained the scalability and performance of such hardware implementations. Recently, many research efforts have been investigated in utilizing the latest discovered memristors in neuromorphic systems due to the similarity of memristors to biological synapses...
October 2014: IEEE Transactions on Neural Networks and Learning Systems
Charles Esnault, Axelle Renodon-Cornière, Masayuki Takahashi, Nathalie Casse, Nicolas Delorme, Guy Louarn, Fabrice Fleury, Jean-François Pilard, Benoît Chénais
The interaction of human Rad51 protein (HsRad51) with single-stranded deoxyribonucleic acid (ssDNA) was investigated by using quartz crystal microbalance (QCM) monitoring and atomic force microscopy (AFM) visualization. Gold surfaces for QCM and AFM were modified by electrografting of the in situ generated aryldiazonium salt from the sulfanilic acid to obtain the organic layer Au-ArSO3 H. The Au-ArSO3 H layer was activated by using a solution of PCl5 in CH2 Cl2 to give a Au-ArSO2 Cl layer. The modified surface was then used to immobilize long ssDNA molecules...
December 1, 2014: Chemphyschem: a European Journal of Chemical Physics and Physical Chemistry
Cristina Savin, Peter Dayan, Máté Lengyel
A venerable history of classical work on autoassociative memory has significantly shaped our understanding of several features of the hippocampus, and most prominently of its CA3 area, in relation to memory storage and retrieval. However, existing theories of hippocampal memory processing ignore a key biological constraint affecting memory storage in neural circuits: the bounded dynamical range of synapses. Recent treatments based on the notion of metaplasticity provide a powerful model for individual bounded synapses; however, their implications for the ability of the hippocampus to retrieve memories well and the dynamics of neurons associated with that retrieval are both unknown...
February 2014: PLoS Computational Biology
Edmund T Rolls
The concept of a (single) limbic system is shown to be outmoded. Instead, anatomical, neurophysiological, functional neuroimaging, and neuropsychological evidence is described that anterior limbic and related structures including the orbitofrontal cortex and amygdala are involved in emotion, reward valuation, and reward-related decision-making (but not memory), with the value representations transmitted to the anterior cingulate cortex for action-outcome learning. In this 'emotion limbic system' a computational principle is that feedforward pattern association networks learn associations from visual, olfactory and auditory stimuli, to primary reinforcers such as taste, touch, and pain...
January 2015: Cortex; a Journal Devoted to the Study of the Nervous System and Behavior
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