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Neural Networks: the Official Journal of the International Neural Network Society

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https://www.readbyqxmd.com/read/28039780/an-empirical-model-of-activity-in-macaque-inferior-temporal-cortex
#1
Salman Khan, Bryan Tripp
There are compelling computational models of many properties of the primate ventral visual stream, but a gap remains between the models and the physiology. To facilitate ongoing refinement of these models, we have compiled diverse information from the electrophysiology literature into a statistical model of inferotemporal (IT) cortex responses. This is a purely descriptive model, so it has little explanatory power. However it is able to directly incorporate a rich and extensible set of tuning properties. So far, we have approximated tuning curves and statistics of tuning diversity for occlusion, clutter, size, orientation, position, and object selectivity in early versus late response phases...
December 13, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28039779/reaction-times-in-visual-search-can-be-explained-by-a-simple-model-of-neural-synchronization
#2
Yakov Kazanovich, Roman Borisyuk
We present an oscillatory neural network model that can account for reaction times in visual search experiments. The model consists of a central oscillator that represents the central executive of the attention system and a number of peripheral oscillators that represent objects in the display. The oscillators are described as generalized Kuramoto type oscillators with adapted parameters. An object is considered as being included in the focus of attention if the oscillator associated with this object is in-phase with the central oscillator...
December 10, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28064015/cognitive-memory-and-mapping-in-a-brain-like-system-for-robotic-navigation
#3
Huajin Tang, Weiwei Huang, Aditya Narayanamoorthy, Rui Yan
Electrophysiological studies in animals may provide a great insight into developing brain-like models of spatial cognition for robots. These studies suggest that the spatial ability of animals requires proper functioning of the hippocampus and the entorhinal cortex (EC). The involvement of the hippocampus in spatial cognition has been extensively studied, both in animal as well as in theoretical studies, such as in the brain-based models by Edelman and colleagues. In this work, we extend these earlier models, with a particular focus on the spatial coding properties of the EC and how it functions as an interface between the hippocampus and the neocortex, as proposed by previous work...
December 7, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28088645/towards-solving-the-hard-problem-of-consciousness-the-varieties-of-brain-resonances-and-the-conscious-experiences-that-they-support
#4
Stephen Grossberg
The hard problem of consciousness is the problem of explaining how we experience qualia or phenomenal experiences, such as seeing, hearing, and feeling, and knowing what they are. To solve this problem, a theory of consciousness needs to link brain to mind by modeling how emergent properties of several brain mechanisms interacting together embody detailed properties of individual conscious psychological experiences. This article summarizes evidence that Adaptive Resonance Theory, or ART, accomplishes this goal...
December 6, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28040585/on-global-exponential-stability-of-positive-neural-networks-with-time-varying-delay
#5
Le Van Hien
This paper presents a new result on the existence, uniqueness and global exponential stability of a positive equilibrium of positive neural networks in the presence of bounded time-varying delay. Based on some novel comparison techniques, a testable condition is derived to ensure that all the state trajectories of the system converge exponentially to a unique positive equilibrium. The effectiveness of the obtained results is illustrated by a numerical example.
December 1, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28107672/a-computational-model-of-conditioning-inspired-by-drosophila-olfactory-system
#6
Faramarz Faghihi, Ahmed A Moustafa, Ralf Heinrich, Florentin Wörgötter
Recent studies have demonstrated that Drosophila melanogaster (briefly Drosophila) can successfully perform higher cognitive processes including second order olfactory conditioning. Understanding the neural mechanism of this behavior can help neuroscientists to unravel the principles of information processing in complex neural systems (e.g. the human brain) and to create efficient and robust robotic systems. In this work, we have developed a biologically-inspired spiking neural network which is able to execute both first and second order conditioning...
November 23, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/27955818/adaptive-exponential-synchronization-of-complex-valued-cohen-grossberg-neural-networks-with-known-and-unknown-parameters
#7
Jin Hu, Chunna Zeng
The complex-valued Cohen-Grossberg neural network is a special kind of complex-valued neural network. In this paper, the synchronization problem of a class of complex-valued Cohen-Grossberg neural networks with known and unknown parameters is investigated. By using Lyapunov functionals and the adaptive control method based on parameter identification, some adaptive feedback schemes are proposed to achieve synchronization exponentially between the drive and response systems. The results obtained in this paper have extended and improved some previous works on adaptive synchronization of Cohen-Grossberg neural networks...
November 17, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/27939066/dissipativity-and-stability-analysis-of-fractional-order-complex-valued-neural-networks-with-time-delay
#8
G Velmurugan, R Rakkiyappan, V Vembarasan, Jinde Cao, Ahmed Alsaedi
As we know, the notion of dissipativity is an important dynamical property of neural networks. Thus, the analysis of dissipativity of neural networks with time delay is becoming more and more important in the research field. In this paper, the authors establish a class of fractional-order complex-valued neural networks (FCVNNs) with time delay, and intensively study the problem of dissipativity, as well as global asymptotic stability of the considered FCVNNs with time delay. Based on the fractional Halanay inequality and suitable Lyapunov functions, some new sufficient conditions are obtained that guarantee the dissipativity of FCVNNs with time delay...
November 9, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/27923168/improved-exponential-convergence-result-for-generalized-neural-networks-including-interval-time-varying-delayed-signals
#9
G Rajchakit, R Saravanakumar, Choon Ki Ahn, Hamid Reza Karimi
This article examines the exponential stability analysis problem of generalized neural networks (GNNs) including interval time-varying delayed states. A new improved exponential stability criterion is presented by establishing a proper Lyapunov-Krasovskii functional (LKF) and employing new analysis theory. The improved reciprocally convex combination (RCC) and weighted integral inequality (WII) techniques are utilized to obtain new sufficient conditions to ascertain the exponential stability result of such delayed GNNs...
November 9, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/27887770/robust-learning-in-spikeprop
#10
Sumit Bam Shrestha, Qing Song
Training a Spiking Neural Network using SpikeProp and its derivatives faces stability issues. Surges, marked by a sudden rise in learning cost, are a common occurrence during the learning process. They disrupt the learning process and often destabilize the process resulting in failure. A proper learning rate, which is neither too small nor too big, is important to minimize surges. Furthermore, external disturbances due to imperfection in sample data as well as internal disturbances are additional destabilizing source during the learning process...
November 8, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/27914262/a-new-switching-control-for-finite-time-synchronization-of-memristor-based-recurrent-neural-networks
#11
Jie Gao, Peiyong Zhu, Ahmed Alsaedi, Fuad E Alsaadi, Tasawar Hayat
In this paper, finite-time synchronization (FTS) of memristor-based recurrent neural networks (MNNs) with time-varying delays is investigated by designing a new switching controller. First, by using the differential inclusions theory and set-valued maps, sufficient conditions to ensure FTS of MNNs are obtained under the two cases of 0<α<1 and α=0, and it is derived that α=0 is the critical value of 0<α<1. Next, it is discussed deeply on the relation between the parameter α and the synchronization time...
November 4, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/27890606/a-new-hyperbox-selection-rule-and-a-pruning-strategy-for-the-enhanced-fuzzy-min-max-neural-network
#12
Mohammed Falah Mohammed, Chee Peng Lim
In this paper, we extend our previous work on the Enhanced Fuzzy Min-Max (EFMM) neural network by introducing a new hyperbox selection rule and a pruning strategy to reduce network complexity and improve classification performance. Specifically, a new k-nearest hyperbox expansion rule (for selection of a new winning hyperbox) is first introduced to reduce the network complexity by avoiding the creation of too many small hyperboxes within the vicinity of the winning hyperbox. A pruning strategy is then deployed to further reduce the network complexity in the presence of noisy data...
November 3, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/27889240/a-balanced-motor-primitive-framework-can-simultaneously-explain-motor-learning-in-unimanual-and-bimanual-movements
#13
Ken Takiyama, Yutaka Sakai
Certain theoretical frameworks have successfully explained motor learning in either unimanual or bimanual movements. However, no single theoretical framework can comprehensively explain motor learning in both types of movement because the relationship between these two types of movement remains unclear. Although our recent model of a balanced motor primitive framework attempted to simultaneously explain motor learning in unimanual and bimanual movements, this model focused only on a limited subset of bimanual movements and therefore did not elucidate the relationships between unimanual movements and various bimanual movements...
November 3, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/27955819/decentralized-event-triggered-synchronization-of-uncertain-markovian-jumping-neutral-type-neural-networks-with-mixed-delays
#14
Sibel Senan, M Syed Ali, R Vadivel, Sabri Arik
In this study, we present an approach for the decentralized event-triggered synchronization of Markovian jumping neutral-type neural networks with mixed delays. We present a method for designing decentralized event-triggered synchronization, which only utilizes locally available information, in order to determine the time instants for transmission from sensors to a central controller. By applying a novel Lyapunov-Krasovskii functional, as well as using the reciprocal convex combination method and some inequality techniques such as Jensen's inequality, we obtain several sufficient conditions in terms of a set of linear matrix inequalities (LMIs) under which the delayed neural networks are stochastically stable in terms of the error systems...
October 28, 2016: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/27890605/developmental-metaplasticity-in-neural-circuit-codes-of-firing-and-structure
#15
Yoram Baram
Firing-rate dynamics have been hypothesized to mediate inter-neural information transfer in the brain. While the Hebbian paradigm, relating learning and memory to firing activity, has put synaptic efficacy variation at the center of cortical plasticity, we suggest that the external expression of plasticity by changes in the firing-rate dynamics represents a more general notion of plasticity. Hypothesizing that time constants of plasticity and firing dynamics increase with age, and employing the filtering property of the neuron, we obtain the elementary code of global attractors associated with the firing-rate dynamics in each developmental stage...
January 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/27855307/a-limit-cycle-self-organizing-map-architecture-for-stable-arm-control
#16
Di-Wei Huang, Rodolphe J Gentili, Garrett E Katz, James A Reggia
Inspired by the oscillatory nature of cerebral cortex activity, we recently proposed and studied self-organizing maps (SOMs) based on limit cycle neural activity in an attempt to improve the information efficiency and robustness of conventional single-node, single-pattern representations. Here we explore for the first time the use of limit cycle SOMs to build a neural architecture that controls a robotic arm by solving inverse kinematics in reach-and-hold tasks. This multi-map architecture integrates open-loop and closed-loop controls that learn to self-organize oscillatory neural representations and to harness non-fixed-point neural activity even for fixed-point arm reaching tasks...
January 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/27846430/synchronization-of-discrete-time-neural-networks-with-delays-and-markov-jump-topologies-based-on-tracker-information
#17
Xinsong Yang, Zhiguo Feng, Jianwen Feng, Jinde Cao
In this paper, synchronization in an array of discrete-time neural networks (DTNNs) with time-varying delays coupled by Markov jump topologies is considered. It is assumed that the switching information can be collected by a tracker with a certain probability and transmitted from the tracker to controller precisely. Then the controller selects suitable control gains based on the received switching information to synchronize the network. This new control scheme makes full use of received information and overcomes the shortcomings of mode-dependent and mode-independent control schemes...
January 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/27842242/stabilization-of-metastable-dynamical-rotating-waves-in-a-ring-of-unidirectionally-coupled-sigmoidal-neurons-due-to-shortcuts
#18
Yo Horikawa
Effects of shortcut connection on metastable dynamical rotating waves in a ring of sigmoidal neurons with unidirectional excitatory coupling are considered. A kinematical equation describing the propagation of wave fronts is derived with a sign function for the output function of neurons. Unstable rotating waves can be stabilized in the presence of an inhibitory shortcut. When a shortcut is excitatory and connects the most distant neurons, the dynamical metastability of rotating waves is lost. The duration of transient rotating waves then increases only linearly with the number of neurons, not exponentially...
January 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/27842241/finite-time-synchronization-of-uncertain-coupled-switched-neural-networks-under-asynchronous-switching
#19
Yuanyuan Wu, Jinde Cao, Qingbo Li, Ahmed Alsaedi, Fuad E Alsaadi
This paper deals with the finite-time synchronization problem for a class of uncertain coupled switched neural networks under asynchronous switching. By constructing appropriate Lyapunov-like functionals and using the average dwell time technique, some sufficient criteria are derived to guarantee the finite-time synchronization of considered uncertain coupled switched neural networks. Meanwhile, the asynchronous switching feedback controller is designed to finite-time synchronize the concerned networks. Finally, two numerical examples are introduced to show the validity of the main results...
January 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/27814468/a-modular-architecture-for-transparent-computation-in-recurrent-neural-networks
#20
Giovanni S Carmantini, Peter Beim Graben, Mathieu Desroches, Serafim Rodrigues
Computation is classically studied in terms of automata, formal languages and algorithms; yet, the relation between neural dynamics and symbolic representations and operations is still unclear in traditional eliminative connectionism. Therefore, we suggest a unique perspective on this central issue, to which we would like to refer as transparent connectionism, by proposing accounts of how symbolic computation can be implemented in neural substrates. In this study we first introduce a new model of dynamics on a symbolic space, the versatile shift, showing that it supports the real-time simulation of a range of automata...
January 2017: Neural Networks: the Official Journal of the International Neural Network Society
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