| 1. |
- Ohlsson, Mattias, et al.
(författare)
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A study of the mean field approach to knapsack problems
- 1997
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Ingår i: Neural Networks. - 0893-6080. ; 10:2, s. 263-271
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Tidskriftsartikel (refereegranskat)abstract
- The mean field theory approach to knapsack problems is extended to multiple knapsacks and generalized assignment problems with Potts mean field equations governing the dynamics. Numerical tests against 'state of the art' conventional algorithms shows good performance for the mean field approach. The inherently parallelism of the mean field equations makes them suitable for direct implementations in microchips. It is demonstrated numerically that the performance is essentially not affected when only a limited number of bits is used in the mean field equations. Also, a hybrid algorithm with linear programming and mean field components is showed to further improve the performance for the difficult homogeneous N x M knapsack problem.
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| 2. |
- Hemani, Ahmed, et al.
(författare)
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Cell placement by self-organisation
- 1990
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Ingår i: Neural Networks. - 0893-6080 .- 1879-2782. ; 3:4, s. 377-383
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Tidskriftsartikel (refereegranskat)
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| 3. |
- Karlholm, Jörgen
(författare)
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Associative Memories with Short--Range Higher Order Couplings
- 1993
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Ingår i: Neural Networks. - 0893-6080 .- 1879-2782. ; 6:3, s. 409-421
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Tidskriftsartikel (refereegranskat)abstract
- A study of recurrent associative memories with exclusively short-range connections is presented. To increase the capacity, higher order couplings are used. We study capacity and pattern completion ability of networks consisting of units with binary (±1) output. Results show that perfect learning of random patterns is difficult for very short coupling ranges, and that the average expected capacities (allowing small errors) in these cases are much smaller than the theoretical maximum, 2 bits per coupling. However, it is also shown that by choosing ranges longer than certain limit sizes, depending on network size and order, we can come close to the theoretical capacity limit. We indicate that these limit sizes increase very slowly with net size. Thus, couplings to at least 28 and 36 neighbors suffice for second order networks with 400 and 90,000 units, respectively. From simulations it is found that even networks with coupling ranges below the limit size are able to complete input patterns with more than 10% errors. Especially remarkable is the ability to correct inputs with large local errors (part of the pattern is masked). We present a local learning algorithm for heteroassociation in recurrent networks without hidden units. The algorithm is used in a multinet system to improve pattern completion ability on correlated patterns.
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| 4. |
- Kim, Sung-Phil, et al.
(författare)
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Divide-and-conquer approach for brain machine interfaces: nonlinear mixture of competitive linear models.
- 2003
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Ingår i: Neural networks : the official journal of the International Neural Network Society. - 0893-6080. ; 16:5-6, s. 865-71
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Tidskriftsartikel (refereegranskat)abstract
- This paper proposes a divide-and-conquer strategy for designing brain machine interfaces. A nonlinear combination of competitively trained local linear models (experts) is used to identify the mapping from neuronal activity in cortical areas associated with arm movement to the hand position of a primate. The proposed architecture and the training algorithm are described in detail and numerical performance comparisons with alternative linear and nonlinear modeling approaches, including time-delay neural networks and recursive multilayer perceptrons, are presented. This new strategy allows training the local linear models using normalized LMS and using a relatively smaller nonlinear network to efficiently combine the predictions of the linear experts. This leads to savings in computational requirements, while the performance is still similar to a large fully nonlinear network.
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| 5. |
- Bengtsson, Fredrik, et al.
(författare)
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Cross-correlations between pairs of neurons in cerebellar cortex in vivo.
- 2013
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Ingår i: Neural Networks. - : Elsevier BV. - 1879-2782 .- 0893-6080. ; 47:Dec.,06, s. 88-94
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Tidskriftsartikel (refereegranskat)abstract
- In the present paper we apply a new neurophysiological technique to make single-electrode, dual loose-patch recordings from pairs of neuronal elements in the cerebellar cortex in vivo. The analyzed cell pairs consisted of an inhibitory molecular layer interneuron and a Purkinje cell (PC) or a Golgi cell and a granule cell, respectively. To detect the magnitude of the unitary inhibitory synaptic inputs we used histograms of the spike activity of the target cell, triggered by the spikes of the inhibitory cell. Using this analysis, we found that single interneurons had no detectable effect on PC firing, which could be explained by an expected very low synaptic weight of individual interneuron-PC connections. However, interneurons did have a weak delaying effect on the overall series of interspike intervals of PCs. Due to the very high number of inhibitory synapses on each PC, a concerted activation of the interneurons could still achieve potent PC inhibition as previously shown. In contrast, in the histograms of the Golgi cell-granule cell pairs, we found a weak inhibitory effect on the granule cell but only at the time period defined as the temporal domain of the slow IPSP previously described for this connection. Surprisingly, the average granule cell firing frequency sampled at one second was strongly modulated with a negative correlation to the overall firing level of the Golgi cell when the latter was modified through current injection via the patch pipette. These findings are compatible with that tonic inhibition is the dominant form of Golgi cell-granule cell inhibition in the adult cerebellum in vivo.
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| 6. |
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| 7. |
- Buda, Mateusz, et al.
(författare)
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A systematic study of the class imbalance problem in convolutional neural networks
- 2018
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Ingår i: Neural Networks. - : PERGAMON-ELSEVIER SCIENCE LTD. - 0893-6080 .- 1879-2782. ; 106, s. 249-259
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Tidskriftsartikel (refereegranskat)abstract
- In this study, we systematically investigate the impact of class imbalance on classification performance of convolutional neural networks (CNNs) and compare frequently used methods to address the issue. Class imbalance is a common problem that has been comprehensively studied in classical machine learning, yet very limited systematic research is available in the context of deep learning. In our study, we use three benchmark datasets of increasing complexity, MNIST, CIFAR-10 and ImageNet, to investigate the effects of imbalance on classification and perform an extensive comparison of several methods to address the issue: oversampling, undersampling, two-phase training, and thresholding that compensates for prior class probabilities. Our main evaluation metric is area under the receiver operating characteristic curve (ROC AUC) adjusted to multi-class tasks since overall accuracy metric is associated with notable difficulties in the context of imbalanced data. Based on results from our experiments we conclude that (i) the effect of class imbalance on classification performance is detrimental; (ii) the method of addressing class imbalance that emerged as dominant in almost all analyzed scenarios was oversampling; (iii) oversampling should be applied to the level that completely eliminates the imbalance, whereas the optimal undersampling ratio depends on the extent of imbalance; (iv) as opposed to some classical machine learning models, oversampling does not cause overfitting of CNNs; (v) thresholding should be applied to compensate for prior class probabilities when overall number of properly classified cases is of interest.
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| 8. |
- Chen, Zetao, et al.
(författare)
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Bio-inspired homogeneous multi-scale place recognition
- 2015
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Ingår i: Neural Networks. - : Elsevier. - 0893-6080 .- 1879-2782. ; 72, s. 48-61
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Tidskriftsartikel (refereegranskat)abstract
- Robotic mapping and localization systems typically operate at either one fixed spatial scale, or over two, combining a local metric map and a global topological map. In contrast, recent high profile discoveries in neuroscience have indicated that animals such as rodents navigate the world using multiple parallel maps, with each map encoding the world at a specific spatial scale. While a number of theoretical-only investigations have hypothesized several possible benefits of such a multi-scale mapping system, no one has comprehensively investigated the potential mapping and place recognition performance benefits for navigating robots in large real world environments, especially using more than two homogeneous map scales. In this paper we present a biologically-inspired multi-scale mapping system mimicking the rodent multi-scale map. Unlike hybrid metric-topological multi-scale robot mapping systems, this new system is homogeneous, distinguishable only by scale, like rodent neural maps. We present methods for training each network to learn and recognize places at a specific spatial scale, and techniques for combining the output from each of these parallel networks. This approach differs from traditional probabilistic robotic methods, where place recognition spatial specificity is passively driven by models of sensor uncertainty. Instead we intentionally create parallel learning systems that learn associations between sensory input and the environment at different spatial scales. We also conduct a systematic series of experiments and parameter studies that determine the effect on performance of using different neural map scaling ratios and different numbers of discrete map scales. The results demonstrate that a multi-scale approach universally improves place recognition performance and is capable of producing better than state of the art performance compared to existing robotic navigation algorithms. We analyze the results and discuss the implications with respect to several recent discoveries and theories regarding how multi-scale neural maps are learnt and used in the mammalian brain.
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| 9. |
- Ding, Yijie, et al.
(författare)
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Shared subspace-based radial basis function neural network for identifying ncRNAs subcellular localization
- 2022
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Ingår i: Neural Networks. - Oxford : Elsevier. - 0893-6080 .- 1879-2782. ; 156, s. 170-178
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Tidskriftsartikel (refereegranskat)abstract
- Non-coding RNAs (ncRNAs) play an important role in revealing the mechanism of human disease for anti-tumor and anti-virus substances. Detecting subcellular locations of ncRNAs is a necessary way to study ncRNA. Traditional biochemical methods are time-consuming and labor-intensive, and computational-based methods can help detect the location of ncRNAs on a large scale. However, many models did not consider the correlation information among multiple subcellular localizations of ncRNAs. This study proposes a radial basis function neural network based on shared subspace learning (RBFNN-SSL), which extract shared structures in multi-labels. To evaluate performance, our classifier is tested on three ncRNA datasets. Our model achieves better performance in experimental results. © 2022 The Author(s)
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| 10. |
- Foulsham, Tom, et al.
(författare)
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Modeling eye movements in visual agnosia with a saliency map approach: Bottom–up guidance or top–down strategy?
- 2011
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Ingår i: Neural Networks. - : Elsevier BV. - 1879-2782 .- 0893-6080. ; 24:6, s. 665-677
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Tidskriftsartikel (refereegranskat)abstract
- Two recent papers (Foulsham, Barton, Kingstone, Dewhurst, & Underwood, 2009; Mannan, Kennard, & Husain, 2009) report that neuropsychological patients with a profound object recognition problem (visual agnosic subjects) show differences from healthy observers in the way their eye movements are controlled when looking at images. The interpretation of these papers is that eye movements can be modeled as the selection of points on a saliency map, and that agnosic subjects show an increased reliance on visual saliency, i.e., brightness and contrast in low-level stimulus features. Here we review this approach and present new data from our own experiments with an agnosic patient that quantifies the relationship between saliency and fixation location. In addition, we consider whether the perceptual difficulties of individual patients might be modeled by selectively weighting the different features involved in a saliency map. Our data indicate that saliency is not always a good predictor of fixation in agnosia: even for our agnosic subject, as for normal observers, the saliency–fixation relationship varied as a function of the task. This means that top–down processes still have a significant effect on the earliest stages of scanning in the setting of visual agnosia, indicating severe limitations for the saliency map model. Top–down, active strategies – which are the hallmark of our human visual system – play a vital role in eye movement control, whether we know what we are looking at or not.
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| 11. |
- Fransén, Erik, 1962-
(författare)
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Functional role of entorhinal cortex in working memory processing
- 2005
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Ingår i: Neural Networks. - : Elsevier BV. - 0893-6080 .- 1879-2782. ; 18:9, s. 1141-1149
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Tidskriftsartikel (refereegranskat)abstract
- Our learning and memory system has the challenge to work in a world where items to learn are dispersed in space and time. From the information extracted by the perceptual systems, the learning system must select and combine information. Both these operations may require a temporary storage where significance and correlations could be assessed. This work builds on the common hypothesis that hippocampus and subicular, entorhinal and parahippocampal/postrhinal areas are essential for the above-mentioned functions. We bring up two examples of models: the first one is modeling of in vivo and in vitro data from entorhinal cortex layer 11 of delayed match-to-sample working memory experiments, the second one studying mechanisms in theta rhythmicity in EC. In both cases, we discuss how cationic currents might be involved and relate their kinetics and pharmacology to behavioral and cellular experimental results.
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| 12. |
|
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| 13. |
- Green, Michael, et al.
(författare)
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Exploring new possibilities for case based explanation of artificial neural network ensembles
- 2009
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Ingår i: Neural Networks. - : Elsevier BV. - 1879-2782 .- 0893-6080. ; 22:1, s. 75-81
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Tidskriftsartikel (refereegranskat)abstract
- Artificial neural network (ANN) ensembles have long suffered from a lack of interpretability. This has severely limited the practical usability of ANNs in settings where an erroneous decision can be disastrous. Several attempts have been made to alleviate this problem. Many of them are based on decomposing the decision boundary of the ANN into a set of rules. We explore and compare a set of new methods for this explanation process on two artificial data sets (Monks 1 and 3), and one acute coronary syndrome data set consisting of 861 electrocardiograms (ECG) collected retrospectively at the emergency department at Lund University Hospital. The algorithms managed to extract good explanations in more than 84% of the cases. More to the point, the best method provided 99% and 91% good explanations in Monks data 1 and 3 respectively. Also there was a significant overlap between the algorithms. Furthermore, when explaining a given ECG, the overlap between this method and one of the physicians was the same as the one between the two physicians in this study. Still the physicians were significantly, p-value <0.001, more similar to each other than to any of the methods. The algorithms have the potential to be used as an explanatory aid when using ANN ensembles in clinical decision support systems.
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| 14. |
- Guo, Xiaoyi, et al.
(författare)
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Sequence homology score-based deep fuzzy network for identifying therapeutic peptides
- 2024
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Ingår i: Neural Networks. - Kidlington : Elsevier. - 0893-6080 .- 1879-2782. ; 178
-
Tidskriftsartikel (refereegranskat)abstract
- The detection of therapeutic peptides is a topic of immense interest in the biomedical field. Conventional biochemical experiment-based detection techniques are tedious and time-consuming. Computational biology has become a useful tool for improving the detection efficiency of therapeutic peptides. Most computational methods do not consider the deviation caused by noise. To improve the generalization performance of therapeutic peptide prediction methods, this work presents a sequence homology score-based deep fuzzy echo-state network with maximizing mixture correntropy (SHS-DFESN-MMC) model. Our method is compared with the existing methods on eight types of therapeutic peptide datasets. The model parameters are determined by 10 fold cross-validation on their training sets and verified by independent test sets. Across the 8 datasets, the average area under the receiver operating characteristic curve (AUC) values of SHS-DFESN-MMC are the highest on both the training (0.926) and independent sets (0.923). © 2024 The Authors
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| 15. |
- Han, Ridong, et al.
(författare)
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Document-level Relation Extraction with Relation Correlations
- 2024
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Ingår i: Neural Networks. - Oxford : Elsevier. - 0893-6080 .- 1879-2782. ; 171, s. 14-24
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Tidskriftsartikel (refereegranskat)abstract
- Document-level relation extraction faces two often overlooked challenges: long-tail problem and multi-label problem. Previous work focuses mainly on obtaining better contextual representations for entity pairs, hardly address the above challenges. In this paper, we analyze the co-occurrence correlation of relations, and introduce it into the document-level relation extraction task for the first time. We argue that the correlations can not only transfer knowledge between data-rich relations and data-scarce ones to assist in the training of long-tailed relations, but also reflect semantic distance guiding the classifier to identify semantically close relations for multi-label entity pairs. Specifically, we use relation embedding as a medium, and propose two co-occurrence prediction sub-tasks from both coarse- and fine-grained perspectives to capture relation correlations. Finally, the learned correlation-aware embeddings are used to guide the extraction of relational facts. Substantial experiments on two popular datasets (i.e., DocRED and DWIE) are conducted, and our method achieves superior results compared to baselines. Insightful analysis also demonstrates the potential of relation correlations to address the above challenges. The data and code are released at https://github.com/RidongHan/DocRE-Co-Occur. © 2023 Elsevier Ltd
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| 16. |
- Hasselmo, Michael E., et al.
(författare)
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A phase code for memory could arise from circuit mechanisms in entorhinal cortex
- 2009
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Ingår i: Neural Networks. - : PERGAMON-ELSEVIER. - 0893-6080 .- 1879-2782. ; 22:8, s. 1129-1138
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Tidskriftsartikel (refereegranskat)abstract
- Neurophysiological data reveals intrinsic cellular properties that suggest how entorhinal cortical neurons could code memory by the phase of their firing. Potential cellular mechanisms for this phase coding in models of entorhinal function are reviewed. This mechanism for phase coding provides a substrate for modeling the responses of entorhinal grid cells, as well as the replay of neural spiking activity during waking and sleep. Efforts to implement these abstract models in more detailed biophysical compartmental simulations raise specific issues that could be addressed in larger scale population models incorporating mechanisms of inhibition.
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| 17. |
- Hesslow, Germund, et al.
(författare)
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Classical conditioning of motor responses: What is the learning mechanism?
- 2013
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Ingår i: Neural Networks. - : Elsevier BV. - 1879-2782 .- 0893-6080. ; 47:Mar,28, s. 81-87
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Tidskriftsartikel (refereegranskat)abstract
- According to a widely held assumption, the main mechanism underlying motor learning in the cerebellum, such as eyeblink conditioning, is long-term depression (LTD) of parallel fibre to Purkinje cell synapses. Here we review some recent physiological evidence from Purkinje cell recordings during conditioning with implications for models of conditioning. We argue that these data pose four major challenges to the LTD hypothesis of conditioning. (i) LTD cannot account for the pause in Purkinje cell firing that is believed to drive the conditioned blink. (ii) The temporal conditions conducive to LTD do not match those for eyeblink conditioning. (iii) LTD cannot readily account for the adaptive timing of the conditioned response. (iv) The data suggest that parallel fibre to Purkinje cell synapses are not depressed after learning a Purkinje cell CR. Models based on metabotropic glutamate receptors are also discussed and found to be incompatible with the recording data.
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| 18. |
- Jaeger, Dieter, et al.
(författare)
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Computation in the Cerebellum
- 2013
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Ingår i: Neural Networks. - : Elsevier BV. - 1879-2782 .- 0893-6080. ; 47, s. 1-2
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
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| 19. |
- Johansson, Christopher, et al.
(författare)
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Towards Cortex Sized Artificial Neural Systems
- 2007
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Ingår i: Neural Networks. - : Elsevier BV. - 0893-6080 .- 1879-2782. ; 20:1, s. 48-61
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Tidskriftsartikel (refereegranskat)abstract
- We propose, implement, and discuss an abstract model of the mammalian neocortex. This model is instantiated with a sparse recurrently connected neural network that has spiking leaky integrator units and continuous Hebbian learning. First we study the structure, modularization, and size of neocortex, and then we describe a generic computational model of the cortical circuitry. A characterizing feature of the model is that it is based on the modularization of neocortex into hypercolumns and minicolumns.Both a floating- and fixed-point arithmetic implementation of the model are presented along with simulation results. We conclude that an implementation on a cluster computer is not communication but computation bounded. A mouse and rat cortex sized version of our model executes in 44% and 23% of real-time respectively. Further, an instance of the model with 1.6 x 10(6) units and 2 x 10(11) connections performed noise reduction and pattern completion. These implementations represent the current frontier of large-scale abstract neural network simulations in terms of network size and running speed.
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| 20. |
- Karami, Saeed, et al.
(författare)
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Unsupervised feature selection based on variance–covariance subspace distance
- 2023
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Ingår i: Neural Networks. - Oxford : Elsevier. - 0893-6080 .- 1879-2782. ; 166, s. 188-203
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Tidskriftsartikel (refereegranskat)abstract
- Subspace distance is an invaluable tool exploited in a wide range of feature selection methods. The power of subspace distance is that it can identify a representative subspace, including a group of features that can efficiently approximate the space of original features. On the other hand, employing intrinsic statistical information of data can play a significant role in a feature selection process. Nevertheless, most of the existing feature selection methods founded on the subspace distance are limited in properly fulfilling this objective. To pursue this void, we propose a framework that takes a subspace distance into account which is called “Variance–Covariance subspace distance”. The approach gains advantages from the correlation of information included in the features of data, thus determines all the feature subsets whose corresponding Variance–Covariance matrix has the minimum norm property. Consequently, a novel, yet efficient unsupervised feature selection framework is introduced based on the Variance–Covariance distance to handle both the dimensionality reduction and subspace learning tasks. The proposed framework has the ability to exclude those features that have the least variance from the original feature set. Moreover, an efficient update algorithm is provided along with its associated convergence analysis to solve the optimization side of the proposed approach. An extensive number of experiments on nine benchmark datasets are also conducted to assess the performance of our method from which the results demonstrate its superiority over a variety of state-of-the-art unsupervised feature selection methods. The source code is available at https://github.com/SaeedKarami/VCSDFS. © 2023 The Author(s)
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| 21. |
- Krestel, Ralf, et al.
(författare)
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Diversifying customer review rankings
- 2015
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Ingår i: Neural Networks. - : Elsevier Ltd. - 0893-6080 .- 1879-2782. ; 66, s. 36-45
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Tidskriftsartikel (refereegranskat)abstract
- E-commerce Web sites owe much of their popularity to consumer reviews accompanying product descriptions. On-line customers spend hours and hours going through heaps of textual reviews to decide which products to buy. At the same time, each popular product has thousands of user-generated reviews, making it impossible for a buyer to read everything. Current approaches to display reviews to users or recommend an individual review for a product are based on the recency or helpfulness of each review.In this paper, we present a framework to rank product reviews by optimizing the coverage of the ranking with respect to sentiment or aspects, or by summarizing all reviews with the top-K reviews in the ranking. To accomplish this, we make use of the assigned star rating for a product as an indicator for a review's sentiment polarity and compare bag-of-words (language model) with topic models (latent Dirichlet allocation) as a mean to represent aspects. Our evaluation on manually annotated review data from a commercial review Web site demonstrates the effectiveness of our approach, outperforming plain recency ranking by 30% and obtaining best results by combining language and topic model representations.
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| 22. |
- Metta, Giorgio, et al.
(författare)
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The iCub humanoid robot : An open-systems platform for research in cognitive development
- 2010
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Ingår i: Neural Networks. - : Elsevier BV. - 0893-6080 .- 1879-2782. ; 23:8-9, s. 1125-1134
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Tidskriftsartikel (refereegranskat)abstract
- We describe a humanoid robot platform - the iCub - which was designed to support collaborative research in cognitive development through autonomous exploration and social interaction. The motivation for this effort is the conviction that significantly greater impact can be leveraged by adopting an open systems policy for software and hardware development. This creates the need for a robust humanoid robot that offers rich perceptuo-motor capabilities with many degrees of freedom, a cognitive capacity for learning and development, a software architecture that encourages reuse & easy integration, and a support infrastructure that fosters collaboration and sharing of resources. The iCub satisfies all of these needs in the guise of an open-system platform which is freely available and which has attracted a growing community of users and developers. To date, twenty iCubs each comprising approximately 5000 mechanical and electrical parts have been delivered to several research labs in Europe and to one in the USA.
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| 23. |
- Morse, Anthony, et al.
(författare)
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Dynamic liquid association : Complex learning without implausible guidance
- 2009
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Ingår i: Neural Networks. - : Elsevier. - 0893-6080 .- 1879-2782. ; 22:7, s. 875-889
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Tidskriftsartikel (refereegranskat)abstract
- Simple associative networks have many desirable properties, but are fundamentally limited by their inability to accurately capture complex relationships. This paper presents a solution significantly extending the abilities of associative networks by using an untrained dynamic reservoir as an input filter. The untrained reservoir provides complex dynamic transformations, and temporal integration, and can be viewed as a complex non-linear feature detector from which the associative network can learn. Typically reservoir systems utilize trained single layer perceptrons to produce desired output responses. However given that both single layer perceptions and simple associative learning have the same computational limitations, i.e. linear separation, they should perform similarly in terms of pattern recognition ability. Further to this the extensive psychological properties of simple associative networks and the lack of explicit supervision required for associative learning motivates this extension overcoming previous limitations. Finally, we demonstrate the resulting model in a robotic embodiment, learning sensorimotor contingencies, and matching a variety of psychological data. (C) 2008 Elsevier Ltd. All rights reserved.
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| 24. |
- Rongala, Udaya B., et al.
(författare)
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Cuneate spiking neural network learning to classify naturalistic texture stimuli under varying sensing conditions
- 2020
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Ingår i: Neural Networks. - : Elsevier BV. - 0893-6080. ; 123, s. 273-287
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Tidskriftsartikel (refereegranskat)abstract
- We implemented a functional neuronal network that was able to learn and discriminate haptic features from biomimetic tactile sensor inputs using a two-layer spiking neuron model and homeostatic synaptic learning mechanism. The first order neuron model was used to emulate biological tactile afferents and the second order neuron model was used to emulate biological cuneate neurons. We have evaluated 10 naturalistic textures using a passive touch protocol, under varying sensing conditions. Tactile sensor data acquired with five textures under five sensing conditions were used for a synaptic learning process, to tune the synaptic weights between tactile afferents and cuneate neurons. Using post-learning synaptic weights, we evaluated the individual and population cuneate neuron responses by decoding across 10 stimuli, under varying sensing conditions. This resulted in a high decoding performance. We further validated the decoding performance across stimuli, irrespective of sensing velocities using a set of 25 cuneate neuron responses. This resulted in a median decoding performance of 96% across the set of cuneate neurons. Being able to learn and perform generalized discrimination across tactile stimuli, makes this functional spiking tactile system effective and suitable for further robotic applications.
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| 25. |
- Saeed, Uzair, et al.
(författare)
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One-shot many-to-many facial reenactment using Bi-Layer Graph Convolutional Networks
- 2022
-
Ingår i: Neural Networks. - Oxford : Elsevier. - 0893-6080 .- 1879-2782. ; 156, s. 193-204
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Tidskriftsartikel (refereegranskat)abstract
- Facial reenactment is aimed at animating a source face image into a new place using a driving facial picture. In a few shot scenarios, the present strategies are designed with one or more identities or identity-sustained suffering protection challenges. These current solutions are either developed with one or more identities in mind, or face identity protection issues in one or more shot situations. Multiple pictures from the same entity have been used in previous research to model facial reenactment. In contrast, this paper presents a novel model of one-shot many-to-many facial reenactments that uses only one facial image of a face. The proposed model produces a face that represents the objective representation of the same source identity. The proposed technique can simulate motion from a single image by decomposing an object into two layers. Using bi-layer with Convolutional Neural Network (CNN), we named our model Bi-Layer Graph Convolutional Layers (BGCLN) which utilized to create the latent vector’s optical flow representation. This yields the precise structure and shape of the optical stream. Comprehensive studies suggest that our technique can produce high-quality results and outperform most recent techniques in both qualitative and quantitative data comparisons. Our proposed system can perform facial reenactment at 15 fps, which is approximately real time. Our code is publicly available at https://github.com/usaeed786/BGCLN
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| 26. |
- Sheikholharam Mashhadi, Peyman, 1982-, et al.
(författare)
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Parallel orthogonal deep neural network
- 2021
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Ingår i: Neural Networks. - Oxford : Elsevier BV. - 0893-6080 .- 1879-2782. ; 140, s. 167-183
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Tidskriftsartikel (refereegranskat)abstract
- Ensemble learning methods combine multiple models to improve performance by exploiting their diversity. The success of these approaches relies heavily on the dissimilarity of the base models forming the ensemble. This diversity can be achieved in many ways, with well-known examples including bagging and boosting.It is the diversity of the models within an ensemble that allows the ensemble to correct the errors made by its members, and consequently leads to higher classification or regression performance. A mistake made by a base model can only be rectified if other members behave differently on that particular instance, and provide the aggregator with enough information to make an informed decision. On the contrary, lack of diversity not only lowers model performance, but also wastes computational resources. Nevertheless, in the current state of the art ensemble approaches, there is no guarantee on the level of diversity achieved, and no mechanism ensuring that each member will learn a different decision boundary from the others.In this paper, we propose a parallel orthogonal deep learning architecture in which diversity is enforced by design, through imposing an orthogonality constraint. Multiple deep neural networks are created, parallel to each other. At each parallel layer, the outputs of different base models are subject to Gram–Schmidt orthogonalization. We demonstrate that this approach leads to a high level of diversity from two perspectives. First, the models make different errors on different parts of feature space, and second, they exhibit different levels of uncertainty in their decisions. Experimental results confirm the benefits of the proposed method, compared to standard deep learning models and well-known ensemble methods, in terms of diversity and, as a result, classification performance. © 2021 The Author(s). Published by Elsevier Ltd.
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| 27. |
- Smith, Christian, et al.
(författare)
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Teleoperation for a ball-catching task with significant dynamics
- 2008
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Ingår i: Neural Networks. - : Elsevier. - 0893-6080 .- 1879-2782. ; 21:4, s. 604-620
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Tidskriftsartikel (refereegranskat)abstract
- In this paper we present ongoing work on how to incorporate human motion models into the design of a high performance teleoperation platform. A short description of human motion models used for ball-catching is followed by a more detailed study of a teleoperation platform on which to conduct experiments. Also, a pilot study using minimum jerk theory to explain user input behavior in teleoperated catching is presented.
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| 28. |
- Suryanarayana, Shreyas M., et al.
(författare)
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Roles for globus pallidus externa revealed in a computational model of action selection in the basal ganglia
- 2019
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Ingår i: Neural Networks. - : PERGAMON-ELSEVIER SCIENCE LTD. - 0893-6080 .- 1879-2782. ; 109, s. 113-136
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Tidskriftsartikel (refereegranskat)abstract
- The basal ganglia are considered vital to action selection - a hypothesis supported by several biologically plausible computational models. Of the several subnuclei of the basal ganglia, the globus pallidus externa (GPe) has been thought of largely as a relay nucleus, and its intrinsic connectivity has not been incorporated in significant detail, in any model thus far. Here, we incorporate newly revealed subgroups of neurons within the GPe into an existing computational model of the basal ganglia, and investigate their role in action selection. Three main results ensued. First, using previously used metrics for selection, the new extended connectivity improved the action selection performance of the model. Second, low frequency theta oscillations were observed in the subpopulation of the GPe (the TA or 'arkypallidal' neurons) which project exclusively to the striatum. These oscillations were suppressed by increased dopamine activity - revealing a possible link with symptoms of Parkinson's disease. Third, a new phenomenon was observed in which the usual monotonic relationship between input to the basal ganglia and its output within an action 'channel' was, under some circumstances, reversed. Thus, at high levels of input, further increase of this input to the channel could cause an increase of the corresponding output rather than the more usually observed decrease. Moreover, this phenomenon was associated with the prevention of multiple channel selection, thereby assisting in optimal action selection. Examination of the mechanistic origin of our results showed the so-called 'prototypical' GPe neurons to be the principal subpopulation influencing action selection. They control the striatum via the arkypallidal neurons and are also able to regulate the output nuclei directly. Taken together, our results highlight the role of the GPe as a major control hub of the basal ganglia, and provide a mechanistic account for its control function.
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| 29. |
- Tian, Songsong, et al.
(författare)
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A survey on few-shot class-incremental learning
- 2024
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Ingår i: Neural Networks. - Oxford : Elsevier. - 0893-6080 .- 1879-2782. ; 169, s. 307-324
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Forskningsöversikt (refereegranskat)abstract
- Large deep learning models are impressive, but they struggle when real-time data is not available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for deep neural networks to learn new tasks from just a few labeled samples without forgetting the previously learned ones. This setup can easily leads to catastrophic forgetting and overfitting problems, severely affecting model performance. Studying FSCIL helps overcome deep learning model limitations on data volume and acquisition time, while improving practicality and adaptability of machine learning models. This paper provides a comprehensive survey on FSCIL. Unlike previous surveys, we aim to synthesize few-shot learning and incremental learning, focusing on introducing FSCIL from two perspectives, while reviewing over 30 theoretical research studies and more than 20 applied research studies. From the theoretical perspective, we provide a novel categorization approach that divides the field into five subcategories, including traditional machine learning methods, meta learning-based methods, feature and feature space-based methods, replay-based methods, and dynamic network structure-based methods. We also evaluate the performance of recent theoretical research on benchmark datasets of FSCIL. From the application perspective, FSCIL has achieved impressive achievements in various fields of computer vision such as image classification, object detection, and image segmentation, as well as in natural language processing and graph. We summarize the important applications. Finally, we point out potential future research directions, including applications, problem setups, and theory development. Overall, this paper offers a comprehensive analysis of the latest advances in FSCIL from a methodological, performance, and application perspective. © 2023 The Author(s)
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| 30. |
- Vasco, M., et al.
(författare)
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Leveraging hierarchy in multimodal generative models for effective cross-modality inference
- 2022
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Ingår i: Neural Networks. - : Elsevier BV. - 0893-6080 .- 1879-2782. ; 146, s. 238-255
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Tidskriftsartikel (refereegranskat)abstract
- This work addresses the problem of cross-modality inference (CMI), i.e., inferring missing data of unavailable perceptual modalities (e.g., sound) using data from available perceptual modalities (e.g., image). We overview single-modality variational autoencoder methods and discuss three problems of computational cross-modality inference, arising from recent developments in multimodal generative models. Inspired by neural mechanisms of human recognition, we contribute the NEXUS model, a novel hierarchical generative model that can learn a multimodal representation of an arbitrary number of modalities in an unsupervised way. By exploiting hierarchical representation levels, NEXUS is able to generate high-quality, coherent data of missing modalities given any subset of available modalities. To evaluate CMI in a natural scenario with a high number of modalities, we contribute the “Multimodal Handwritten Digit” (MHD) dataset, a novel benchmark dataset that combines image, motion, sound and label information from digit handwriting. We access the key role of hierarchy in enabling high-quality samples during cross-modality inference and discuss how a novel training scheme enables NEXUS to learn a multimodal representation robust to missing modalities at test time. Our results show that NEXUS outperforms current state-of-the-art multimodal generative models in regards to their cross-modality inference capabilities.
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| 31. |
- Viejo, Diego, et al.
(författare)
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Using GNG to improve 3D features extractio - Application to 6DoF Egomotion
- 2012
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Ingår i: Neural Networks. - : Elsevier BV. - 1879-2782 .- 0893-6080. ; 32, s. 138-146
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Tidskriftsartikel (refereegranskat)abstract
- Abstract in UndeterminedSeveral recent works deal with 3D data in mobile robotic problems, e.g. mapping or egomotion. Data comes from any kind of sensor such as stereo vision systems, time of flight cameras or 3D lasers, providing a huge amount of unorganized 3D data. In this paper, we describe an efficient method to build complete 3D models from a Growing Neural Gas (GNG). The GNG is applied to the 3D raw data and it reduces both the subjacent error and the number of points, keeping the topology of the 3D data. The GNG output is then used in a 3D feature extraction method. We have performed a deep study in which we quantitatively show that the use of GNG improves the 3D feature extraction method. We also show that our method can be applied to any kind of 3D data. The 3D features obtained are used as input in an Iterative Closest Point (ICP)-like method to compute the 6DoF movement performed by a mobile robot. A comparison with standard ICP is performed, showing that the use of GNG improves the results. Final results of 3D mapping from the egomotion calculated are also shown. (C) 2012 Elsevier Ltd. All rights reserved.
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| 32. |
- Wu, Hongjie, et al.
(författare)
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AttentionMGT-DTA : A multi-modal drug-target affinity prediction using graph transformer and attention mechanism
- 2024
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Ingår i: Neural Networks. - Oxford : Elsevier. - 0893-6080 .- 1879-2782. ; 169, s. 623-636
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Tidskriftsartikel (refereegranskat)abstract
- The accurate prediction of drug-target affinity (DTA) is a crucial step in drug discovery and design. Traditional experiments are very expensive and time-consuming. Recently, deep learning methods have achieved notable performance improvements in DTA prediction. However, one challenge for deep learning-based models is appropriate and accurate representations of drugs and targets, especially the lack of effective exploration of target representations. Another challenge is how to comprehensively capture the interaction information between different instances, which is also important for predicting DTA. In this study, we propose AttentionMGT-DTA, a multi-modal attention-based model for DTA prediction. AttentionMGT-DTA represents drugs and targets by a molecular graph and binding pocket graph, respectively. Two attention mechanisms are adopted to integrate and interact information between different protein modalities and drug-target pairs. The experimental results showed that our proposed model outperformed state-of-the-art baselines on two benchmark datasets. In addition, AttentionMGT-DTA also had high interpretability by modeling the interaction strength between drug atoms and protein residues. Our code is available at https://github.com/JK-Liu7/AttentionMGT-DTA. © 2023 The Author(s)
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| 33. |
- Zhdanov, Vladimir, 1952
(författare)
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Neural networks including microRNAs
- 2012
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Ingår i: Neural Networks. - : Elsevier BV. - 1879-2782 .- 0893-6080. ; 25, s. 200-204
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
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| 34. |
- Zhdanov, Vladimir, 1952
(författare)
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Three generic bistable scenarios of the interplay of voltage pulses and gene expression in neurons
- 2013
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Ingår i: Neural Networks. - : Elsevier BV. - 1879-2782 .- 0893-6080. ; 44, s. 51-63
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Tidskriftsartikel (refereegranskat)abstract
- The long-term changes of the neuron function are often related to the interplay of the membrane voltage pulses and gene expression. In the present work, this phenomenon is modeled by combining the standard stochastic integrate-and-fire neuron model with generic kinetic models describing gene expression. The three scenarios under consideration include, respectively, the voltage-related regulation of (i) gene transcription into mRNA, (ii) gene transcription into miRNA, and (iii) proteasome formation. Typical transient and steady-state kinetics are shown. The latter kinetics exhibit a unique steady state, bistability, or oscillations. The conditions of realization of these regimes are investigated numerically. The transient and oscillatory kinetics are predicted on the time scale of about one hour or longer. The implications of these results for interpretation of synaptic plasticity and learning and long-term memory are briefly discussed.
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