Efficient computation via sparse coding in electrosensory neural networks

The electric sense combines spatial aspects of vision and touch with temporal features of audition. Its accessible neural architecture shares similarities with mammalian sensory systems and allows for recordings from successive brain areas to test hypotheses about neural coding. Further, electrosensory stimuli encountered during prey capture, navigation, and communication, can be readily synthesized in the laboratory. These features enable analyses of the neural circuitry that reveal general principles of encoding and decoding, such as segregation of information into separate streams and neural response sparsification. A systems level understanding arises via linkage between cellular differentiation and network architecture, revealed by in vitro and in vivo analyses, while computational modeling reveals how single cell dynamics and connectivity shape the sparsification process.     

Pigeon oesophageal EMG activity: analysis of intramural neural control

The effects of agonist and antagonist cholinergic and adrenergic drugs on spontaneous electrical activity of transverse muscular strips of pigeon cervical oesophagus were examined. Tetrodotoxin failed to affect EMG activity. Cholinomimetics produced excitatory effects. The response to carbachol was enhanced by hexamethonium and reversed into an inhibitory effect by atropine. Noradrenaline evoked a concentration-dependent, biphasic effect (inhibition at low and excitation at high concentrations). Isoproterenol induced inhibitory response unaffected by tetrodotoxin. Phenylephrine induced excitatory response completely antagonized by tetrodotoxin and partially opposed by atropine. It is concluded that: i) the oesophageal spontaneous EMG activity is myogenic; ii) the intramural neurons have no tonic influence on the spontaneous EMG activity; iii) in the intramural plexuses there are cholinergic excitatory-, non-cholinergic excitatory- and inhibitory neurons, with unknown neurotransmitter; iv) excitatory alpha-adrenoceptors, located on the nervous elements and inhibitory beta-adrenoceptors, located on the smooth-muscle cells, are present.     

Central neural control of sympathetic nerve activity in heart failure following exercise training

Typical characteristics of chronic congestive heart failure (HF) are increased sympathetic drive, altered autonomic reflexes, and altered body fluid regulation. These abnormalities lead to an increased risk of mortality, particularly in the late stage of chronic HF. Recent evidence suggests that central nervous system (CNS) mechanisms may be important in these abnormalities during HF. Exercise training (ExT) has emerged as a nonpharmacological therapeutic strategy substitute with significant benefit to patients with HF. Regular ExT improves functional capacity as well as quality of life and perhaps prognosis in chronic HF patients. The mechanism(s) by which ExT improves the clinical status of HF patients is not fully known. Recent studies have provided convincing evidence that ExT significantly alleviates the increased sympathetic drive, altered autonomic reflexes, and altered body fluid regulation in HF. This review describes and highlights the studies that examine various central pathways involved in autonomic outflow that are altered in HF and are improved following ExT. The increased sympathoexcitation is due to an imbalance between inhibitory and excitatory mechanisms within specific areas in the CNS such as the paraventricular nucleus (PVN) of the hypothalamus. Studies summarized here have revealed that ExT improves the altered inhibitory pathway utilizing nitric oxide and GABA mechanisms within the PVN in HF. ExT alleviates elevated sympathetic outflow in HF through normalization of excitatory glutamatergic and angiotensinergic mechanisms within the PVN. ExT also improves volume reflex function and thus fluid balance in HF. Preliminary observations also suggest that ExT induces structural neuroplasticity in the brain of rats with HF. We conclude that improvement of the enhanced CNS-mediated increase in sympathetic outflow, specifically to the kidneys related to fluid balance, contributes to the beneficial effects of ExT in HF.     

Biologically-informed neural networks guide mechanistic modeling from sparse experimental data

Biologically-informed neural networks (BINNs), an extension of physics-informed neural networks [1], are introduced and used to discover the underlying dynamics of biological systems from sparse experimental data. In the present work, BINNs are trained in a supervised learning framework to approximate in vitro cell biology assay experiments while respecting a generalized form of the governing reaction-diffusion partial differential equation (PDE). By allowing the diffusion and reaction terms to be multilayer perceptrons (MLPs), the nonlinear forms of these terms can be learned while simultaneously converging to the solution of the governing PDE. Further, the trained MLPs are used to guide the selection of biologically interpretable mechanistic forms of the PDE terms which provides new insights into the biological and physical mechanisms that govern the dynamics of the observed system. The method is evaluated on sparse real-world data from wound healing assays with varying initial cell densities [2].     

Impulsive control strategies in biological control of pesticide

By presenting and analyzing the pest-predator model under insecticides used impulsively, two impulsive strategies in biological control are put forward. The first strategy: the pulse period is fixed, but the proportional constant E(1) changes, which represents the fraction of pests killed by applying insecticide. For this scheme, two thresholds, E(1)(**) and E(1)(*) for E(1) are obtained. If E(1)>or=E(1)(*), both the pest and predator (natural enemies) populations go to extinction. If E(1)(**)相似文献   

Patterns of muscle activity during different behaviors in chicks: implications for neural control

The large behavioral repertoire that spans the embryonic and postembryonic stages of development make chicks an ideal system for identifying patterns of muscle activity that are common to different behaviors and those that are behavior-specific. The main goal of this work was to identify the similar and dissimilar aspects of the recruitment patterns and the regulation of muscle activity during three distinct postembryonic behaviors: walking, swimming and airstepping. We identified two synergies that were common to each of these behaviors. The synergies were not disrupted by the absence of FT1 activity in airstepping. Within each synergy the recruitment time, recruitment order and duration of activity were not rigid, but varied according to the context-specific resistance that the leg encountered. Unlike the other muscles, FT2 activity was not recruited as part of the same synergy in each behavior. When weight-bearing contact with the substrate did not occur, as in swimming and airstepping, as well as in walking in chicks with deafferented legs, FT2 activity was not recruited as part of either synergy, but was recruited during the time between them. Although not identical, embryonic motility and hatching motor pattern both show the two synergies described for the postembryonic behaviors. Like the latter behaviors, the synergies tolerated the absence of activity from specific muscles. Thus, we suggest that the CNS produces different behaviors using many of the same muscles by organizing the patterned activity around two common synergies while permitting the different muscles that participate in a synergy to be modified in tandem or on an individual basis. Furthermore, the common synergies are established early during prenatal development in chicks.Abbreviations aIF anterior iliofibularius - CF caudilioflexorius - CNS central nervous system - EMG electromyogram - FT femorotibialis - GL gastrocnemius lateralis - P1 protraction 1 - P2 protraction 2 - pIF posterior iliofibularis - R retraction - SA sartorius - TA tibialis anterior - pIF posterior iliofibularis - ms millisecond     

Bias-free estimation of information content in temporally sparse neuronal activity

Applying information theoretic measures to neuronal activity data enables the quantification of neuronal encoding quality. However, when the sample size is limited, a naïve estimation of the information content typically contains a systematic overestimation (upward bias), which may lead to misinterpretation of coding characteristics. This bias is exacerbated in Ca²⁺ imaging because of the temporal sparsity of elevated Ca²⁺ signals. Here, we introduce methods to correct for the bias in the naïve estimation of information content from limited sample sizes and temporally sparse neuronal activity. We demonstrate the higher accuracy of our methods over previous ones, when applied to Ca²⁺ imaging data recorded from the mouse hippocampus and primary visual cortex, as well as to simulated data with matching tuning properties and firing statistics. Our bias-correction methods allowed an accurate estimation of the information place cells carry about the animal’s position (spatial information) and uncovered the spatial resolution of hippocampal coding. Furthermore, using our methods, we found that cells with higher peak firing rates carry higher spatial information per spike and exposed differences between distinct hippocampal subfields in the long-term evolution of the spatial code. These results could be masked by the bias when applying the commonly used naïve calculation of information content. Thus, a bias-free estimation of information content can uncover otherwise overlooked properties of the neural code.     

Comparative strategies in the investigation of neural networks.

Comparative studies on nervous systems, though infrequently undertaken for the purpose of comparison, have yielded some important generalities about the formats of nervous networks, and about the cell biology of certain neural types. In the first category, it is clear that convergent evolutionary processes arrived at very similar networks to accomplish reciprocal and lateral inhibition, and load-compensation in "resistance reflexes." A newer general network format is described, command-derived inhibition, in which the central nervous elements controlling a rapid movement deliver presynaptic inhibition to the terminals of sensory neurons that carry reafferent excitation from the movement. It is argued that such circuits occur in several groups of animals, and that they include as a special class the efferent inhibitory neurons innervating acoustico-lateralis receptors in vertebrates. The properties of circuit elements that now seem to constitute useful generalizations include size principle (the inverse relationship between size and excitability in a variety of neurons), and the late differentiation of sensory neurons, failure to decussate, and their inability to mediate inhibition. Many other generalities have emerged, only to fall; one conclusion from such searches is that many supposedly "basic" properties of cell types or neural circuits are in fact not phylogenetically conservative, however much the physiologist may expect them to be.     

An oscillatory neural network model of sparse distributed memory and novelty detection

A model of sparse distributed memory is developed that is based on phase relations between the incoming signals and an oscillatory mechanism for information processing. This includes phase-frequency encoding of input information, natural frequency adaptation among the network oscillators for storage of input signals, and a resonance amplification mechanism that responds to familiar stimuli. Simulations of this model show different types of dynamics in response to new and familiar stimuli. The application of the model to hippocampal working memory is discussed.     

A sparse neural code for some speech sounds but not for others

The precise neural mechanisms underlying speech sound representations are still a matter of debate. Proponents of 'sparse representations' assume that on the level of speech sounds, only contrastive or otherwise not predictable information is stored in long-term memory. Here, in a passive oddball paradigm, we challenge the neural foundations of such a 'sparse' representation; we use words that differ only in their penultimate consonant ("coronal" [t] vs. "dorsal" [k] place of articulation) and for example distinguish between the German nouns Latz ([lats]; bib) and Lachs ([laks]; salmon). Changes from standard [t] to deviant [k] and vice versa elicited a discernible Mismatch Negativity (MMN) response. Crucially, however, the MMN for the deviant [lats] was stronger than the MMN for the deviant [laks]. Source localization showed this difference to be due to enhanced brain activity in right superior temporal cortex. These findings reflect a difference in phonological 'sparsity': Coronal [t] segments, but not dorsal [k] segments, are based on more sparse representations and elicit less specific neural predictions; sensory deviations from this prediction are more readily 'tolerated' and accordingly trigger weaker MMNs. The results foster the neurocomputational reality of 'representationally sparse' models of speech perception that are compatible with more general predictive mechanisms in auditory perception.     

Information transmission in oscillatory neural activity

Periodic neural activity not locked to the stimulus or to motor responses is usually ignored. Here, we present new tools for modeling and quantifying the information transmission based on periodic neural activity that occurs with quasi-random phase relative to the stimulus. We propose a model to reproduce characteristic features of oscillatory spike trains, such as histograms of inter-spike intervals and phase locking of spikes to an oscillatory influence. The proposed model is based on an inhomogeneous Gamma process governed by a density function that is a product of the usual stimulus-dependent rate and a quasi-periodic function. Further, we present an analysis method generalizing the direct method (Rieke et al. in Spikes: exploring the neural code. MIT Press, Cambridge, 1999; Brenner et al. in Neural Comput 12(7):1531-1552, 2000) to assess the information content in such data. We demonstrate these tools on recordings from relay cells in the lateral geniculate nucleus of the cat.     

Discovering the role of the adrenal gland in the control of body function

This essay looks at the historical significance of three APS classic papers that are freely available online: Cannon WB and de la Paz D. Emotional stimulation of adrenal secretion. Am J Physiol 28: 64-70, 1911 (http://ajplegacy.physiology.org/cgi/reprint/28/1/64). Cannon WB. The emergency function of the adrenal medulla in pain and the major emotions. Am J Physiol 33: 356-372, 1914 (http://ajplegacy.physiology.org/cgi/reprint/33/2/356). Cannon WB. Studies on the conditions of activity in endocrine glands. V. The isolated heart as an indicator of adrenal secretion induced by pain, asphyxia and excitement. Am J Physiol 50: 399-432, 1919 (http://ajplegacy.physiology.org/cgi/reprint/50/3/399).     

Role of proprioceptors in neural control

The conventional notion that peripheral muscle-related signals provide the basis for resistance to external perturbations is no longer sufficient. Proprioceptive information seems to be required for spatial steering of multi-joint movements, and also for temporal coordination among the joints in certain tasks. In rhythmic movements, peripheral and centrally generated signals appear to interact in a complementary manner. The complex effects of proprioceptive afferents on motor output continue to be delineated vigorously. Global effects of local perturbation in multi-joint contexts are emerging as being particularly significant.     

Epidemiology in the strategies for malaria control

A rapid overview is presented of the evolution of the main orientations of malaria control, since the discovery of mosquito transmission. Stated control objectives appear to have oscillated between expectations to eradicate the vector, or at least the disease, and more modest approaches to minimise the effects of the infection. High optimism was raised when a new control measure, or new combination of existing measures, appeared to be highly effective and was expected to have universal applicability. The implementation of large scale campaigns eventually found the limits of applicability of the proposed strategy and the exaggerated expectations soon gave way to disillusion and, eventually, to a revival of research. The longest and most impacting period of exaggerated expectations was the global malaria eradication campaign of the 1950s and 1960s, which completely disregarded the study of local epidemiology, considering that all it was needed was to know if an area was "malarious" or not. Research was practically abandoned and, even when reinstated after the recognised failure of the campaign, it has retained an almost exclusive orientation towards the development of control tools, drugs or eventually vaccines. One of the earliest victims of the eradication campaign was the study of epidemic malaria and its determinants in different epidemic prone areas. In spite of an extremely long period of disillusion, lasting for almost two decades, the reality of the malaria problem led WHO and member countries to agree on a global strategy of control, aiming at a realistic use of existing tools, to at least reduce or prevent mortality. An essential element of this strategy is the prevention or control of malaria epidemics and the selective use of vector control, both of which have to be based on a solid knowledge of local epidemiology, the study of which has to rejoin the path abandoned fifty years ago.     

β-淀粉样蛋白神经毒性及其防治策略

β-淀粉样蛋白（amyloid β-peptide,Aβ）的过量表达和异常聚集是引起阿尔茨海默病的重要原因之一。以β-淀粉样蛋白级联假说为线索,阐述分泌酶对Aβ生成的影响,不同聚合状态Aβ的神经毒性以及Aβ毒性作用机制,总结Aβ生成、聚合、清除过程中神经毒性的相应防治措施,对阿尔茨海默病中β-淀粉样蛋白神经毒性最新研究进展作一综述。     

PASNet: pathway-associated sparse deep neural network for prognosis prediction from high-throughput data

Background

Predicting prognosis in patients from large-scale genomic data is a fundamentally challenging problem in genomic medicine. However, the prognosis still remains poor in many diseases. The poor prognosis may be caused by high complexity of biological systems, where multiple biological components and their hierarchical relationships are involved. Moreover, it is challenging to develop robust computational solutions with high-dimension, low-sample size data.

Results

In this study, we propose a Pathway-Associated Sparse Deep Neural Network (PASNet) that not only predicts patients’ prognoses but also describes complex biological processes regarding biological pathways for prognosis. PASNet models a multilayered, hierarchical biological system of genes and pathways to predict clinical outcomes by leveraging deep learning. The sparse solution of PASNet provides the capability of model interpretability that most conventional fully-connected neural networks lack. We applied PASNet for long-term survival prediction in Glioblastoma multiforme (GBM), which is a primary brain cancer that shows poor prognostic performance. The predictive performance of PASNet was evaluated with multiple cross-validation experiments. PASNet showed a higher Area Under the Curve (AUC) and F1-score than previous long-term survival prediction classifiers, and the significance of PASNet’s performance was assessed by Wilcoxon signed-rank test. Furthermore, the biological pathways, found in PASNet, were referred to as significant pathways in GBM in previous biology and medicine research.

Conclusions

PASNet can describe the different biological systems of clinical outcomes for prognostic prediction as well as predicting prognosis more accurately than the current state-of-the-art methods. PASNet is the first pathway-based deep neural network that represents hierarchical representations of genes and pathways and their nonlinear effects, to the best of our knowledge. Additionally, PASNet would be promising due to its flexible model representation and interpretability, embodying the strengths of deep learning. The open-source code of PASNet is available at https://github.com/DataX-JieHao/PASNet.