International journal of neural systems
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Complex-valued Hopfield networks which possess the energy function are analyzed. The dynamics of the network with certain forms of an activation function is de-composable into the dynamics of the amplitude and phase of each neuron. ⋯ Therefore its phase synchronization mechanism is useful for the area-wide offset control of the traffic signals. The computer simulations show the effectiveness under the various traffic conditions.
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Comparative Study
Model-free functional MRI analysis using topographic independent component analysis.
Data-driven fMRI analysis techniques include independent component analysis (ICA) and different types of clustering in the temporal domain. Since each of these methods has its particular strengths, it is natural to look for an approach that unifies Kohonen's self-organizing map and ICA. ⋯ While achieved by a slight modification of the ICA model, it can be at the same time used to define a topographic order (clusters) between the components, and thus has the usual computational advantages associated with topographic maps. In this contribution, we can show that when applied to fMRI analysis it outperforms FastICA.
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A correspondence is established between the basic elements of logic reasoning systems (knowledge bases, rules, inference and queries) and the structure and dynamical evolution laws of neural networks. The correspondence is pictured as a translation dictionary which might allow to go back and forth between symbolic and network formulations, a desirable step in learning-oriented systems and multicomputer networks. In the framework of Horn clause logics, it is found that atomic propositions with n arguments correspond to nodes with nth order synapses, rules to synaptic intensity constraints, forward chaining to synaptic dynamics and queries either to simple node activation or to a query tensor dynamics.
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We present a simulation environment called SPIKELAB which incorporates a simulator that is able to simulate large networks of spiking neurons using a distributed event driven simulation. Contrary to a time driven simulation, which is usually used to simulate spiking neural networks, our simulation needs less computational resources because of the low average activity of typical networks. The paper addresses the speed up using an event driven versus a time driven simulation and how large networks can be simulated by a distribution of the simulation using already available computing resources. It also presents a solution for the integration of digital or analogue neuromorphic circuits into the simulation process.
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In this paper we have studied cortical dynamics as assessed using graphical methods during deep anaesthesia. Graphical analysis was carried out by autocorrelation functions and return maps with different lags. During moderate and deep anaesthesia, the electroencephalogram (EEG) shows a burst suppression pattern, consisting of abruptly-occurring high amplitude bursts alternating with periods of relative silence. ⋯ The graphical methods used revealed differences in dynamics and topology of bursts as evoked by different stimuli. Spontaneous bursts clearly had different dynamics from evoked burst; which could not be seen directly from the raw EEG data. This study suggests that graphical analysis is a useful tool to obtain information about the dynamics of neuronal processes behind cortical responses during deep anaesthesia.