S3UCA: Soft-Margin Support Vector Machine-Based Social Network User Credibility Assessment Method

Among the algorithms used to assess user credibility in social networks, most of them quantify user information and then calculate the user credibility measure by linear summation. The algorithm above, however, ignores the aliasing of user credibility results under the linear summation dimension, resulting in a low evaluation accuracy. To solve this problem, we propose a user credibility evaluation method based on a soft-margin support-vector machine (SVM). This method transforms the user credibility evaluation dimension from a linear summation dimension to a plane coordinate dimension, which reduces the evaluation errors caused by user aliasing in the classification threshold interval. In the quantization of user information, the ladder assignment method is used to process the user text information and numeric information, and the weight assignment method of information entropy is used to calculate the weight assignment among different feature items, which reduces the errors caused by the inconsistency of the order of magnitude among different types of user information. Simulation results demonstrate the superiority of the proposed method in the user’s credibility evaluation results.

Weighted Fractional-Order Transform Based on Periodic Matrix

Tao et al. proposed the definition of the linear summation of fractional-order matrices based on the theory of Yeh and Pei. This definition was further extended and applied to image encryption. In this paper, we propose a reformulation of the definitions of Yeh et al. and Tao et al. and analyze them theoretically. The results show that many weighted terms are invalid. Therefore, we use the proposed reformulation to prove that the effective weighted terms depend on the period of the matrix. This also shows that the image encryption methods based on the weighted fractional-order transform will lead to the security risk of key invalidation. Finally, our hypothesis is verified by the unified theoretical framework of multiple-parameter discrete fractional-order transforms.

Predominantly linear summation of metabotropic postsynaptic potentials follows coactivation of neurogliaform interneurons

Summation of ionotropic receptor-mediated responses is critical in neuronal computation by shaping input-output characteristics of neurons. However, arithmetics of summation for metabotropic signals are not known. We characterized the combined ionotropic and metabotropic output of neocortical neurogliaform cells (NGFCs) using electrophysiological and anatomical methods in the rat cerebral cortex. These experiments revealed that GABA receptors are activated outside release sites and confirmed coactivation of putative NGFCs in superficial cortical layers in vivo. Triple recordings from presynaptic NGFCs converging to a postsynaptic neuron revealed sublinear summation of ionotropic GABAA responses and linear summation of metabotropic GABAB responses. Based on a model combining properties of volume transmission and distributions of all NGFC axon terminals, we predict that in 83% of cases one or two NGFCs can provide input to a point in the neuropil. We suggest that interactions of metabotropic GABAergic responses remain linear even if most superficial layer interneurons specialized to recruit GABAB receptors are simultaneously active.

The additive nature of the human multisensory evoked pupil response

Pupillometry has received increased interest for its usefulness in measuring various sensory processes as an alternative to behavioural assessments. This is also apparent for multisensory investigations. Studies of the multisensory pupil response, however, have produced conflicting results. Some studies observed super-additive multisensory pupil responses, indicative of multisensory integration (MSI). Others observed additive multisensory pupil responses even though reaction time (RT) measures were indicative of MSI. Therefore, in the present study, we investigated the nature of the multisensory pupil response by combining methodological approaches of previous studies while using supra-threshold stimuli only. In two experiments we presented auditory and visual stimuli to observers that evoked a(n) (onset) response (be it constriction or dilation) in a simple detection task and a change detection task. In both experiments, the RT data indicated MSI as shown by race model inequality violation. Still, the multisensory pupil response in both experiments could best be explained by linear summation of the unisensory pupil responses. We conclude that the multisensory pupil response for supra-threshold stimuli is additive in nature and cannot be used as a measure of MSI, as only a departure from additivity can unequivocally demonstrate an interaction between the senses.

Linear summation of metabotropic postsynaptic potentials follows coactivation of neurogliaform interneurons

SummarySummation of ionotropic receptor-mediated responses is critical in neuronal computation by shaping input-output characteristics of neurons. However, arithmetics of summation for metabotropic signals are not known. We characterized the combined ionotropic and metabotropic output of neocortical neurogliaform cells (NGFCs) using electrophysiological and anatomical methods. These experiments revealed that GABA receptors are activated up to 1.8 microns from release sites and confirmed coactivation of putative NGFCs in superficial cortical layers in vivo. Triple recordings from presynaptic NGFCs converging to a postsynaptic neuron revealed sublinear summation of ionotropic GABAA responses and linear summation of metabotropic GABAB responses. Based on a model combining distances of volume transmission from release sites and distributions of all NGFC axon terminals, we postulate that 2 to 3 NGFCs provide input to a point in the neuropil. We suggest that interactions of metabotropic GABAergic responses remain linear even if most superficial layer interneurons specialized to recruit GABAB receptors are simultaneously active.

Synthesis of higher order feature codes through stimulus-specific supra-linear summation

How cortical circuits build representations of complex objects is poorly understood. The massive dimensional expansion from the thalamus to the primary sensory cortex may enable sparse, comprehensive representations of higher order features to facilitate object identification. To generate such a code, cortical neurons must integrate broadly over space, yet simultaneously obtain sharp tuning to specific stimulus features. The logic of cortical integration that may synthesize such a sparse, high dimensional code for complex features is not known. To address this question, we probed the integration and population coding of higher order stimuli in the somatosensory and visual cortices of awake mice using two-photon calcium imaging across cortical layers. We found that somatosensory and visual cortical neurons sum highly specific combinations of sensory inputs supra-linearly, but integrate other inputs sub-linearly, leading to selective responses to higher order features. This integrative process generates a sparse, but comprehensive code for complex stimuli from the earliest stages of cortical processing. These results from multiple sensory modalities imply that input-specific supra-linear summation may represent a widespread cortical mechanism for the synthesis of higher order feature codes. This new mechanism may explain how the brain exploits the thalamocortical expansion of dimensionality to encode arbitrary complex features of sensory stimuli.

Approximation of functions by linear summation methods in the Orlicz type spaces

Approximative properties of linear summation methods of Fourier series are considered in the Orlicz type spaces S_M. In particular, in terms of approximations by such methods, constructive characteristics are obtained for classes of functions whose moduli of smoothness do not exceed a certain majorant.

A non-spiking neuron model with dynamic leak to avoid instability in recurrent networks

Recurrent circuitry components are distributed widely within the brain, including both excitatory and inhibitory synaptic connections. Recurrent neuronal networks have potential stability problems, perhaps a predisposition to epilepsy. More generally, instability risks making internal representations of information unreliable. To assess the inherent stability properties of such recurrent networks, we tested a linear summation, non-spiking neuron model with and without a ‘dynamic leak’, corresponding to the low-pass filtering of synaptic input current by the RC circuit of the biological membrane. We first show that the output of this neuron model, in either of its two forms, follows its input at a higher fidelity than a wide range of spiking neuron models across a range of input frequencies. Then we constructed fully connected recurrent networks with equal numbers of excitatory and inhibitory neurons and randomly distributed weights across all synapses. When the networks were driven by pseudorandom sensory inputs with varying frequency, the recurrent network activity tended to induce high frequency self-amplifying components, sometimes evident as distinct transients, which were not present in the input data. The addition of a dynamic leak based on known membrane properties consistently removed such spurious high frequency noise across all networks. Furthermore, we found that the neuron model with dynamic leak imparts a network stability that seamlessly scales with the size of the network, conduction delays, the input density of the sensory signal and a wide range of synaptic weight distributions. Our findings suggest that neuronal dynamic leak serves the beneficial function of protecting recurrent neuronal circuitry from the self-induction of spurious high frequency signals, thereby permitting the brain to utilize this architectural circuitry component regardless of network size or recurrency.Author SummaryIt is known that neurons of the brain are extensively interconnected, which can result in many recurrent loops within its neuronal network. Such loops are prone to instability. Here we wanted to explore the potential noise and instability that could result in recurrently connected neuronal networks across a range of conditions. To facilitate such simulations, we developed a non-spiking neuron model that captures the main characteristics of conductance-based neuron models of Hodgkin-Huxley type, but is more computationally efficient. We found that a so-called dynamic leak, which is a natural consequence of the way the membrane of the neuron is constructed and how the neuron integrates synaptic inputs, provided protection against spurious, high frequency noise that tended to arise in our recurrent networks of varying size. We propose that this linear summation model provides a stable and useful tool for exploring the computational behavior of recurrent neural networks.