Electrophysiological properties of spinal dorsal horn neurons in vitro: calcium-dependent action potentials and actions of neuroactive peptides

The electrophysiological properties of the dorsal horn neurons in the superficial parts of the spinal dorsal horn, and actions of substance P, somatostatin and enkephalin on these cells have been investigated by intracellular recording in an immature rat spinal cord slice preparation;The ionic nature of the action potentials has been analyzed by modifying the ionic microenvironment, and by using ions or drugs known to block specific voltage-dependent conductances. We have shown that action potentials in immature rat dorsal horn neurons are generated by voltage-dependent conductance increases to sodium and calcium ions, and in particular that two distinct types of calcium spikes are probably present in these cells;Somatostatin and enkephalin hyperpolarized dorsal horn neurons and caused reduction or abolition of spontaneous firing. While the hyperpolarization produced by enkephalin was always associated with a fall in neuronal input resistance, in the case of somatostatin the similar effect was less consistently observed. These responses were brought about by both pre- and postsynaptic actions of the peptides;Bath application of substance P depolarized dorsal horn neurons and increased their excitability. The depolarization was most commonly associated with an increase in neuronal input resistance. SP-depolarization, in addition to a decrease in a voltage-dependent potassium conductance, may have been due to increases in sodium and calcium conductances. In about one-third of the cells, substance P induced a biphasic membrane response consisting of an initial hyperpolarization followed by a depolarization. The hyperpolarization was probably of presynaptic origin;Substance P modified the duration of the Ca-spike, the most consistent change being an initial decrease of the spike duration. Our data are consistent with a possibility that substance P shortens the duration of the Ca spike by decreasing a voltage sensitive inward Ca current and/or augmenting an outward potassium current

Optimal Fuzzy Model Construction with Statistical Information using Genetic Algorithm

Fuzzy rule based models have a capability to approximate any continuous function to any degree of accuracy on a compact domain. The majority of FLC design process relies on heuristic knowledge of experience operators. In order to make the design process automatic we present a genetic approach to learn fuzzy rules as well as membership function parameters. Moreover, several statistical information criteria such as the Akaike information criterion (AIC), the Bhansali-Downham information criterion (BDIC), and the Schwarz-Rissanen information criterion (SRIC) are used to construct optimal fuzzy models by reducing fuzzy rules. A genetic scheme is used to design Takagi-Sugeno-Kang (TSK) model for identification of the antecedent rule parameters and the identification of the consequent parameters. Computer simulations are presented confirming the performance of the constructed fuzzy logic controller

Effect of excitatory and inhibitory agents and a glial inhibitor on optically-recorded primary-afferent excitation

The effects of GABA, excitatory amino-acid receptors antagonists and a glial metabolism inhibitor on primary-afferent excitation in the spinal dorsal horn were studied by imaging the presynaptic excitation of high-threshold afferents in cord slices from young rats with a voltage-sensitive dye. Primary afferent fibers and terminals were anterogradely labeled with a voltage-sensitive dye from the dorsal root attached to the spinal cord slice. Single-pulse stimulation of C fiber-activating strength to the dorsal root elicited compound action potential-like optical responses in the superficial dorsal horn. The evoked presynaptic excitation was increased by the GABAA receptor antagonists picrotoxin and bicuculline, by glutamate receptor antagonists D-AP5 and CNQX, and by the glial metabolism inhibitor mono-fluoroacetic acid (MFA). The increase in presynaptic excitation by picrotoxin was inhibited in the presence of D-AP5, CNQX and MFA. Presynaptic modulation in the central terminal of fine primary afferents by excitatory and inhibitory amino acids may represent a mechanism that regulates the transmission of pain

Quaternion Neuro-Fuzzy Learning Algorithm for Fuzzy Rule Generation

Abstract—In order to generate or tune fuzzy rules, Neuro- Fuzzy learning algorithms with Gaussian type membership functions based on gradient-descent method are well known. In this paper, we propose a new learning approach, the Quaternion Neuro-Fuzzy learning algorithm. This method is an extension of the conventional method to four-dimensional space by using a quaternion neural network that maps quaternion to real values. Input, antecedent membership functions and consequent singletons are quaternion, and output is real. Four-dimensional input can be better represented by quaternion than by real values. We compared it with the conventional method by several function identification problems, and revealed that the proposed method outperformed the counterpart: The number of rules was reduced to 5 from 625, the number of epochs by one fortieth, and error by one tenth in the best cases.The Second International Conference on Robot, Vision and Signal Processing December 10-12, 2013 Kitakyushu, Japa

Artificial Bee Colony Algorithm with Improved Explorations for Numerical Function Optimization

A major problem with Artificial Bee Colony (ABC) algorithm is its premature convergence to local optima, which originates from lack of explorative search capability of the algorithm. This paper introduces ABC with Improved Explorations (ABC-IX), a novel algorithm that modifies both the selection and perturbation operations of the basic ABC algorithm in an explorative way. Unlike the basic ABC algorithm, ABC-IX employs a probabilistic, explorative selection scheme based on simulated annealing which can accept both better and worse candidate solutions. ABC-IX also maintains a self-adaptive perturbation rate, separately for each candidate solution, to promote more explorations. ABC-IX is tested on a number of benchmark problems for numerical optimization and compared with several recent variants of ABC. Results show that ABC-IX often outperforms the other ABC-variants on most of the problems

Generation of Fuzzy Rules Based on Complex-valued Neuro-Fuzzy Learning Algorithm

In order to generate or tune fuzzy rules, Neuro-Fuzzy learning algorithms with Gaussian type membership functions based on gradient-descent method are well known. In this paper, we propose a new learning approach, the Complex-valued Neuro-Fuzzy learning algorithm. This method is an extension of the conventional method to complex domain by using a complex-valued neural network that maps complex values to real values. Input, antecedent membership functions and consequent singleton are complex, and output is real. Two-dimensional input can be better represented by complex numbers than by real values. We compared it with the conventional method by several function identification problems, and revealed that the proposed method outperformed the counterpart, and that it is a useful tool for learning a fuzzy system model.The 3rd International Workshop on Advanced Computational Intelligence and Intelligent Informatics (IWACIII 2013) will be held in Shanghai, China from October 18 to 21 in 2013

Ensemble of Single‐Layered Complex‐Valued Neural Networks for Classification Tasks

This paper presents ensemble approaches in single-layered complex-valued neural network (CVNN) to solve real-valued classification problems. Each component CVNN of an ensemble uses a recently proposed activation function for its complex-valued neurons (CVNs). A gradient-descent based learning algorithm was used to train the component CVNNs. We applied two ensemble methods, negative correlation learning and bagging, to create the ensembles. Experimental results on a number of real-world benchmark problems showed a substantial performance improvement of the ensembles over the individual single-layered CVNN classifiers. Furthermore, the generalization performances were nearly equivalent to those obtained by the ensembles of real-valued multilayer neural networks