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At least 505 records · Page 28Linked to original sources
PubMed2005

Novel hypothesis for the cause of panic disorder via the neuroepithelial bodies in the lung.

Panic disorder (PD) is a complex condition that is further complicated by its numerous inducers, which include hypercapnia, hypoxia, sodium lactate, caffeine and cholecystokinin. It seems unlikely that there are specific suffocation receptors for each of these inducers in the brain. The pulmonary neuroepithelial bodies (NEBs), which are situated at the bifurcation point of the small bronchi, act as storage cells for 5-hydroxytryptamine (5-HT) and sensors for suffocation. If we suppose that PD might represent an inflammation of the NEBs, bradykinin (BK) which augments the airway hyper-response to diverse indcers might cause these cells to release 5-HT along with peptides and panneuroendcrine markers from their dence-core secretory granules. It was revealed that BK with 5-HT could cross the blood-brain barrier (BBB). When 5-HT released from these cells along with BK cross the BBB, the release of 5-HT at the axonal terminals in the serotonergic neurons in the brain will be inhibited, since the 5-HT1 autoreceptor have a higher affinity for 5-HT than do the 5-HT2 receptors. The inhibition of 5-HT at the axonal terminal causes to suppress the periaqueductal gray matter, which inhibits flight reactions to impending danger, pain or asphyxia. In short, this serotonergic situation might bring about PD. According to this theory, the type of inducer that the PD patient is exposed to is unimportant as long as it stimulates the NEBs, and through the effect of 5-HT and BK, PD would be revaluated as a somatic disease that directly and reversibly affects the brain.

Anxiety↗
PubMed2005

Rostral cingulate gyrus: A putative target for deep brain stimulation in treatment-refractory depression.

Surgery for intractable affective illnesses has generated considerable controversy over the last century. Deep brain stimulation (DBS) has revolutionized neurosurgical practice, especially in the field of advanced Parkinson's disease and, more recently, in selected medical-refractory cases of obsessive-compulsive disorder. In this paper, we propose a discrete area of the rostral cingulate gyrus as a potential target for DBS in medical-resistant depressive patients. Brodmann's area 24a has proved to be a vital link in the integration of neural circuits underlying depression, both through proposed neurobiological models and accurate neuroimaging studies. The full reversibility and adjustability of DBS offer the best chance to treat the multidimensional and life-span profile of depression, so giving hope to a great number of desperate human beings.

Brain↗
PubMed2006

Interactive dualism as a partial solution to the mind-brain problem for psychiatry.

With the collapse of the psychoanalytic and the behaviorist models, and the failure of reductive biologism to account for mental life, psychiatry has been searching for a broad, integrative theory on which to base daily practice. The most recent attempt at such a model, Engel's 'biopsychosocial model', has been shown to be devoid of any scientific content, meaning that psychiatry, alone among the medical disciplines, has no recognised scientific basis. It is no coincidence that psychiatry is constantly under attack from all quarters. In order to develop, the discipline requires an integrative and interactive model which can take account of both the mental and the physical dimensions of human life, yet still remain within the materialist scientific ethos. This paper proposes an entirely new model of mind based in Chalmers' 'interactive dualism' which satisfies those needs. It attributes the causation of all behaviour to mental life, but proposes a split in the nature of mentality such that mind becomes a composite function with two, profoundly different aspects. Causation is assigned to a fast, inaccessible cognitive realm operating within the brain machinery while conscious experience is seen as the outcome of a higher order level of brain processing. The particular value of this model is that it immediately offers a practical solution to the mind-brain problem in that, while all information-processing takes place in the mental realm, it is not in the same order of abstraction as perception. This leads to a model of rational interaction which acknowledges both psyche and soma. It can fill the gap left by the demise of Engel's empty 'biopsychosocial model'.

Biological Psychiatry↗
PubMed2006

The dual origins of affect in nightmares: the roles of physiological homeostasis and memory.

Strong negative affect is a key and distressing ingredient of nightmares. Affect in nightmares arises either from the new generation of affective states due to physiological imbalances that occur during sleep or from the reactivation of affect-laden memories. The disruption of physiological balance produces a negative hedonic state, restoration of this balance produces a positive hedonic state, and when balance is attained, a neutral hedonic state results. As a result, hedonic states provoke behaviors in defense of homeostasis, then guide and terminate them. When, due to inadvertent behavior, a pronounced disruption of homeostasis occurs after sleep onset, the resultant strong negative hedonic state is likely to precipitate a nightmare and may lead to awakening. During normal wakefulness, associations of the interplay between stimuli and behaviors that disrupt homeostasis, those that restore homeostasis, and the affective states generated in the process, are committed to memory as affecto-cognitive ensembles. Sleep serves to build or rebuild neural architecture to effect development or to compensate for use- or disease-related wear (e.g. repair oxidative damage). Dreaming serves to synchronize or resynchronize such modified neural circuits with each other and those not modified. Hence, during dreaming, affecto-cognitive ensembles may get reactivated as part of the synchronization process. Where such an ensemble contains strong negative affect (i.e., due to strong affect generated during the original experience), a nightmare may be precipitated. Although both can occur throughout life, the latter type of nightmare is more likely in adults and the former in young children. For the latter memory-based behavioral therapy and for the former education and care are expected to be useful. For both types of nightmare, because strong negative affect is deemed dependent on noradrenergic outflow from the locus coeruleus, the administration of alpha-adrenergic antagonists will provide relief subject to certain caveats.

Affect↗
PubMed2006

Drug addiction: the neurobiology of disrupted self-control.

The nature of addiction is often debated along moral versus biological lines. However, recent advances in neuroscience offer insights that might help bridge the gap between these opposing views. Current evidence shows that most drugs of abuse exert their initial reinforcing effects by inducing dopamine surges in limbic regions, affecting other neurotransmitter systems and leading to characteristic plastic adaptations. Importantly, there seem to be intimate relationships between the circuits disrupted by abused drugs and those that underlie self-control. Significant changes can be detected in circuits implicated in reward, motivation and/or drive, salience attribution, inhibitory control and memory consolidation. Therefore, addiction treatments should attempt to reduce the rewarding properties of drugs while enhancing those of alternative reinforcers, inhibit conditioned memories and strengthen cognitive control. We posit that the time has come to recognize that the process of addiction erodes the same neural scaffolds that enable self-control and appropriate decision making.

Brain↗
PubMed2003

The top and the bottom of ADHD: a neuropsychological perspective.

Five models of attention deficit/hyperactivity disorder (ADHD) are reviewed. It is proposed that the cognitive-energetic model provides a reasonably comprehensive account of ADHD by incorporating the features of both the inhibition and delay aversion models. It is suggested that ADHD can only be accounted for by an analysis at three levels: top-down control, specific cognitive processes and energetic factors. It is argued that a refined and conceptually comprehensive neuropsychological battery is needed to advance research in ADHD. A widely distributed neural network involving frontal, basal ganglia, limbic and cerebellar loci seem implicated in ADHD.

Attention Deficit Disorder with Hyperactivity↗
PubMed2003

The dual pathway model of AD/HD: an elaboration of neuro-developmental characteristics.

The currently dominant neuro-cognitive model of Attention Deficit Hyperactivity Disorder (AD/HD) presents the condition as executive dysfunction (EDF) underpinned by disturbances in the fronto-dorsal striatal circuit and associated dopaminergic branches (e.g. meso-cortical). In contrast, motivationally-based accounts focus on altered reward processes and implicate fronto-ventral striatal reward circuits and those meso-limbic branches that terminate in the ventral striatum especially the nucleus accumbens. One such account, delay aversion (DEL), presents AD/HD as a motivational style-characterised by attempts to escape or avoid delay-arising from fundamental disturbances in these reward centres. While traditionally regarded as competing, EDF and DEL models have recently been presented as complimentary accounts of two psycho-patho-physiological subtypes of AD/HD with different developmental pathways, underpinned by different cortico-striatal circuits and modulated by different branches of the dopamine system. In the current paper we describe the development of this model in more detail. We elaborate on the neuro-circuitry possibly underpinning these two pathways and explore their developmental significance within a neuro-ecological framework.

Attention Deficit Disorder with Hyperactivity↗
PubMed2004

Pathological gambling: a comprehensive review of biobehavioral findings.

In this review, findings of biobehavioral research into pathological gambling (PG) are discussed, focusing on neuropsychological, psychophysiological, neuroimaging, neurochemical and genetic studies. Neuropsychological studies indicate deficiencies in certain executive functions. Psychophysiological studies indicate that arousal in PG is of importance when reward is present. Neuroimaging studies point to abnormalities in brain functioning. Recent research into the neurochemistry of PG indicates that abnormalities exist in different neurotransmitter systems. Finally, genetic studies indicate the existence of abnormal dopamine receptor genes in PG. Methodological and theoretical factors that may explain discrepancies between studies include differences in screening and assessment, heterogeneity of gambling problems and different underlying cognitive or motivational mechanisms. Results from the PG studies fit in with recent theoretical models of addiction and PG, which stress the involvement of brain reward pathways, neurotransmitter abnormalities, the frontal cortex and the psychophysiological stress system. A framework for future studies is suggested, indicating the need for studies that integrate knowledge from different research areas, and that employ stricter diagnostic screening methods and inclusion of clinical control groups.

Behavior, Addictive↗
PubMed2005

PC12 and HEL cell communication in a reconstituted synapse analyzed with mathematical modeling.

A synapse simulating model comprising of the nerve growth factor (NGF)-differentiated PC12 cells releasing neurotransmitter (NT) and sensor 92.1.7.human erythroleukemia (HEL) cells has been used for simulating the connection between neurons and target cells. A Ca(2+) elevation was observed in both cell types when the PC12 cells were challenged with nicotine. The response patterns of individual cell were subsequently analyzed mathematically. The Ca(2+) signals of the PC12 cells were described by an equation representing a simple bi-exponential function. The NT-noradrenaline discharged by the PC12 cells in response to nicotine caused heterogeneous secondary Ca(2+) elevations in the HEL cells after a certain delay. Model fitting of this response disclosed slow "hidden" oscillations and heterogeneous secondary Ca(2+) signals could be grouped on the basis of the oscillation frequency. As determined in control experiments with noradrenaline (NA), the value of oscillation frequency also revealed a good correlation with the NT concentration.

Algorithms↗
PubMed2005

Resistance in cell membrane and nerve fiber.

A mathematical model describing the resistance in cell membrane and nerve fiber is proposed, which is naturally different from that for metal conductors. An allometric scaling law between the resistance and the section area is obtained. In the derivation, He Chengtian's interpolation, which has millennia history, is applied. Most famous models, such as the Hodgkin-Huxley model, FitzHugh-Nagumo models, should be, therefore, revised.

Action Potentials↗
PubMed2006

Cerebellar involvement in verb generation: an fMRI study.

A possible role of the human cerebellum in the generation of verbs corresponding to presented nouns has been suggested. Previous functional brain imaging studies have compared generation of verbs with the reading of nouns as a measure of verb generation. In the present fMRI study involving healthy human subjects, the effects of speech articulation and motor imagery associated with verb production were investigated in greater detail. Generation of verbs to visually presented nouns was compared to a condition in which subjects read those same verbs that had been individually generated by each subject. Activation in lobule HVI/Crus I of the right cerebellar hemisphere was found as a measure of verb generation. In contrast, reading of verbs as a measure of speech articulation evoked cerebellar activations in both left and right paravermal lobule VI. These results suggest an involvement of the right lateral cerebellar hemisphere in linguistic functions during verb generation. Alternatively, effects of inner speech could also possibly explain the results.

Adolescent↗
PubMed2005

The asymptotic equipartition property in reinforcement learning and its relation to return maximization.

We discuss an important property called the asymptotic equipartition property on empirical sequences in reinforcement learning. This states that the typical set of empirical sequences has probability nearly one, that all elements in the typical set are nearly equi-probable, and that the number of elements in the typical set is an exponential function of the sum of conditional entropies if the number of time steps is sufficiently large. The sum is referred to as stochastic complexity. Using the property we elucidate the fact that the return maximization depends on two factors, the stochastic complexity and a quantity depending on the parameters of environment. Here, the return maximization means that the best sequences in terms of expected return have probability one. We also examine the sensitivity of stochastic complexity, which is a qualitative guide in tuning the parameters of action-selection strategy, and show a sufficient condition for return maximization in probability.

Algorithms↗
PubMed2005

Global exponential stability of generalized recurrent neural networks with discrete and distributed delays.

This paper is concerned with analysis problem for the global exponential stability of a class of recurrent neural networks (RNNs) with mixed discrete and distributed delays. We first prove the existence and uniqueness of the equilibrium point under mild conditions, assuming neither differentiability nor strict monotonicity for the activation function. Then, by employing a new Lyapunov-Krasovskii functional, a linear matrix inequality (LMI) approach is developed to establish sufficient conditions for the RNNs to be globally exponentially stable. Therefore, the global exponential stability of the delayed RNNs can be easily checked by utilizing the numerically efficient Matlab LMI toolbox, and no tuning of parameters is required. A simulation example is exploited to show the usefulness of the derived LMI-based stability conditions.

Artificial Intelligence↗
PubMed2005

Recovering real-world images from single-scale boundaries with a novel filling-in architecture.

Filling-in models were successful in predicting psychophysical data for brightness perception. Nevertheless, their suitability for real-world image processing has never been examined. A unified architecture for both predicting psychophysical data and real-world image processing would constitute a powerful theory for early visual information processing. As a first contribution of the present paper, we identified three principal problems with current filling-in architectures, which hamper the goal of having such a unified architecture. To overcome these problems we propose an advance to filling-in theory, called BEATS filling-in, which is based on a novel nonlinear diffusion operator. BEATS filling-in furthermore introduces novel boundary structures. We compare, by means of simulation studies with real-world images, the performance of BEATS filling-in with the recently proposed confidence-based filling-in. As a second contribution we propose a novel mechanism for encoding luminance information in contrast responses ('multiplex contrasts'), which is based on recent neurophysiological findings. Again, by simulations, we show that 'multiplex contrasts' at a single, high-resolution filter scale are sufficient for recovering absolute luminance levels. Hence, 'multiplex contrasts' represent a novel theory addressing how the brain encodes and decodes luminance information.

Computer Simulation↗
PubMedDate not supplied

Quantifying information and performance for flash detection in the blowfly photoreceptor.

Performance on specific tasks in an organism's everyday activities is essential to survival. In this paper, we extend information-theoretic investigation of neural systems to task specific information using a detailed biophysical model of the blowfly photoreceptor. We determine the optimal detection performance using ideal observer analysis and find that detection threshold increases with background light according to a power function. We show how Fisher information is related to the detection performance and compare Fisher information and mutual information in this task-specific context. Our detailed model of the blowfly photoreceptor enables us to detangle the components of phototransduction and analyze the sensitivity of detection performance with respect to biophysical parameters. The biophysical model of the blowfly photoreceptor provides a rich framework for investigation of neural systems.

Algorithms↗
PubMedDate not supplied

Incremental learning of feature space and classifier for face recognition.

We have proposed a new approach to pattern recognition in which not only a classifier but also a feature space of input variables is learned incrementally. In this paper, an extended version of Incremental Principal Component Analysis (IPCA) and Resource Allocating Network with Long-Term Memory (RAN-LTM) are effectively combined to implement this idea. Since IPCA updates a feature space incrementally by rotating the eigen-axes and increasing the dimensions, the inputs of a neural classifier must also change in their values and the number of input variables. To solve this problem, we derive an approximation of the update formula for memory items, which correspond to representative training samples stored in the long-term memory of RAN-LTM. With these memory items, RAN-LTM is efficiently reconstructed and retrained to adapt to the evolution of the feature space. This function is incorporated into our face recognition system. In the experiments, the proposed incremental learning model is evaluated over a self-compiled video clip of 24 persons. The experimental results show that the incremental learning of a feature space is very effective to enhance the generalization performance of a neural classifier in a realistic face recognition task.

Algorithms↗
PubMedDate not supplied

A model of STDP based on spatially and temporally local information: derivation and combination with gated decay.

Temporal relationships between neuronal firing and plasticity have received significant attention in recent decades. Neurophysiological studies have shown the phenomenon of spike-timing-dependent plasticity (STDP). Various models were suggested to implement an STDP-like learning rule in artificial networks based on spiking neuronal representations. The rule presented here was developed under three constraints. First, it only depends on the information that is available at the synapse at the time of synaptic modification. Second, it naturally follows from neurophysiological and psychological research starting with Hebb's postulate [D. Hebb. (1949). The organization of behavior. Wiley, New York]. Third, it is simple, computationally cheap and its parameters are straightforward to determine. This rule is further extended by addition of four different types of gating derived from conventionally used types of gated decay in learning rules for continuous firing rate neural networks. The results show that the advantages of using these gatings are transferred to the new rule without sacrificing its dependency on spike-timing.

Algorithms↗
PubMedDate not supplied

Stochastic resonance in noisy spiking retinal and sensory neuron models.

Two new theorems show that small amounts of additive white noise can improve the bit count or mutual information of several popular models of spiking retinal neurons and spiking sensory neurons. The first theorem gives necessary and sufficient conditions for this noise benefit or stochastic resonance (SR) effect for subthreshold signals in a standard family of Poisson spiking models of retinal neurons. The result holds for all types of finite-variance noise and for all types of infinite-variance stable noise: SR occurs if and only if a sum of noise means or location parameters falls outside a 'forbidden interval' of values. The second theorem gives a similar forbidden-interval sufficient condition for the SR effect for several types of spiking sensory neurons that include the Fitzhugh-Nagumo neuron, the leaky integrate-and-fire neuron, and the reduced Type I neuron model if the additive noise is Gaussian white noise. Simulations show that neither the forbidden-interval condition nor Gaussianity is necessary for the SR effect.

Algorithms↗