Energy coding in neural network with inhibitory neurons

This paper aimed at assessing and comparing the effects of the inhibitory neurons in the neural network on the neural energy distribution, and the network activities in the absence of the inhibitory neurons to understand the nature of neural energy distribution and neural energy coding. Stimulus, synchronous oscillation has significant difference between neural networks with and without inhibitory neurons, and this difference can be quantitatively evaluated by the characteristic energy distribution. In addition, the synchronous oscillation difference of the neural activity can be quantitatively described by change of the energy distribution if the network parameters are gradually adjusted. Compared with traditional method of correlation coefficient analysis, the quantitative indicators based on nervous energy distribution characteristics are more effective in reflecting the dynamic features of the neural network activities. Meanwhile, this neural coding method from a global perspective of neural activity effectively avoids the current defects of neural encoding and decoding theory and enormous difficulties encountered. Our studies have shown that neural energy coding is a new coding theory with high efficiency and great potential.     

A neural circuit model forming semantic network with exception using spike-timing-dependent plasticity of inhibitory synapses

We propose a neural circuit model forming a semantic network with exceptions using the spike-timing-dependent plasticity (STDP) of inhibitory synapses. To evaluate the proposed model, we conducted nine types of computer simulation by combining the three STDP rules for inhibitory synapses and the three spike pairing rules. The simulation results obtained with the STDP rule for inhibitory synapses by Haas et al. [Haas, J.S., Nowotny, T., Abarbanel, H.D.I., 2006, Spike-timing-dependent plasticity of inhibitory synapses in the entorhinal cortex. J. Neurophysiol. 96, 3305–3313] are successful, whereas, the other results are unsuccessful. The results and examinations suggested that the inhibitory connection from the concept linked with an exceptional feature to the general feature is necessary for forming a semantic network with an exception.     

On the dynamics of electrically-coupled neurons with inhibitory synapses

We study the dynamics and bifurcations of noise-free neurons coupled by gap junctions and inhibitory synapses, using both delayed delta functions and alpha functions to model the latter. We focus on the case of two cells, as in the studies of Chow and Kopell (2000) and Lewis and Rinzel (2003), but also show that stable asynchronous splay states exist for globally coupled networks of N cells dominated by subthreshold electrical coupling. Our results agree with those of Lewis and Rinzel (2003) in the weak coupling range, but our Poincaré map analysis yields more information about global behavior and domains of attraction, and we show that the explicit discontinuous maps derived using delayed delta functions compare well with the continuous history-dependent, implicitly-defined maps derived from alpha functions. We find that increased bias currents, super-threshold electrical coupling and synaptic delays promote synchrony, while sub-threshold electrical coupling and fast synapses promote asynchrony. We compare our analytical results with simulations of an ionic current model of spiking cells, and briefly discuss implications for stimulus response modes of locus coeruleus and for central pattern generators. Action Editor: F. Skinner     

A density functional theory parameterised neural network model of zirconia

We have developed a Behler–Parrinello Neural Network (BPNN) that can be employed to calculate energies and forces of zirconia bulk structures with oxygen vacancies with similar accuracy as that of the density functional theory (DFT) calculations that were used to train the BPNN. In this work, we have trained the BPNN potential with a reference set of 2178 DFT calculations and validated it against a dataset of untrained data. We have shown that the bulk structural parameters, equation of states, oxygen vacancy formation energies and diffusion barriers predicted by the BPNN potential are in good agreement with the reference DFT data. The transferability of the BPNN potential has also been benchmarked with the prediction of structures that were not included in the reference set. The evaluation time of the BPNN is orders of magnitude less than corresponding DFT calculations, although the training process of the BPNN potential requires non-negligible amount of computational resources to prepare the dataset. The computational efficiency of the NN enabled it to be used in molecular dynamics simulations of the temperature-dependent diffusion of an oxygen vacancy and the corresponding diffusion activation energy.     

Recognition of culture state using two-dimensional gel electrophoresis with an artificial neural network

Proteomic technologies were applied to the examination of nutrient components in culture broth. In bioprocesses, many types of media have been proposed and used on the commercial scale. Natural nutrients, the chemical components of which cannot be identified completely, are often used in fermentation processes such as in the production of baker's yeast, alcoholic beverages, amino acids, and pharmaceuticals. The catabolic activities of the microorganisms in these processes vary with the species used. We used an artificial neural network (ANN) to recognize the sufficiency of chemical elements based on the protein spots resolved in 2-DE, and we evaluated this technique using the leave-one-out method. We also attempted to reduce the number of input data for spot selection based on sensitivity analysis of the ANN, and the selected data were used to improve accuracy.     

Analysis of reflex modulation with a biologically realistic neural network

In this study, a neuromusculoskeletal model was built to give insight into the mechanisms behind the modulation of reflexive feedback strength as experimentally identified in the human shoulder joint. The model is an integration of a biologically realistic neural network consisting of motoneurons and interneurons, modeling 12 populations of spinal neurons, and a one degree-of-freedom musculoskeletal model, including proprioceptors. The model could mimic the findings of human postural experiments, using presynaptic inhibition of the Ia afferents to modulate the feedback gains. In a pathological case, disabling one specific neural connection between the inhibitory interneurons and the motoneurons could mimic the experimental findings in complex regional pain syndrome patients. It is concluded that the model is a valuable tool to gain insight into the spinal contributions to human motor control. Applications lay in the fields of human motor control and neurological disorders, where hypotheses on motor dysfunction can be tested, like spasticity, clonus, and tremor. Action Editor: Karen Sigvardt     

A discrete time neural network model with spiking neurons

We derive rigorous results describing the asymptotic dynamics of a discrete time model of spiking neurons introduced in Soula et al. (Neural Comput. 18, 1, 2006). Using symbolic dynamic techniques we show how the dynamics of membrane potential has a one to one correspondence with sequences of spikes patterns (“raster plots”). Moreover, though the dynamics is generically periodic, it has a weak form of initial conditions sensitivity due to the presence of a sharp threshold in the model definition. As a consequence, the model exhibits a dynamical regime indistinguishable from chaos in numerical experiments.      

Firing statistics of inhibitory neuron with delayed feedback. II: Non-Markovian behavior

The instantaneous state of a neural network consists of both the degree of excitation of each neuron the network is composed of and positions of impulses in communication lines between the neurons. In neurophysiological experiments, the neuronal firing moments are registered, but not the state of communication lines. But future spiking moments depend essentially on the past positions of impulses in the lines. This suggests, that the sequence of intervals between firing moments (inter-spike intervals, ISIs) in the network could be non-Markovian.     

Dopaminergic acceleration and GABAergic inhibition in extrinsic neural control of the hermit crab heart

The acceleratory and inhibitory cardio-regulatory nerves of hermit crabs (Aniculus aniculus, Dardanus crassimanus) were studied using histochemical, immunocytochemical and pharmacological tests. Glyoxylic acid-induced fluorescence was observed in two of three axons of the dorsal cardiac nerve. One axon of the nerve showed gamma-aminobutyric acid-like immunoreactivity. Effects of stimulation of cardio-acceleratory axons were blocked by the dopaminergic antagonists, haloperidol and chlorpromazine, but not by cholinergic, adrenergic or serotonergic blockers, suggesting that dopamine is the primary potential candidate for the neurotransmitter of cardio-accelerator neurons. Picrotoxin antagonized inhibition of the cardiac ganglion induced by gammaam-inobutyric acid and by cardio-inhibitory axons. Both small and large ganglionic cells may receive dopaminergic and GABAergic extrinsic neural control.Abbreviations ACh acetylcholine - CA cardio-accelerator - CA1 and CA2 first and second cardio-accelerators - CI cardio-inhibitor - EJP excitatory junction potential - GABA gamma-aminobutyric acid - EPSP excitatory postsynaptic potential - IPSP inhibitory postsynaptic potential - LGC large ganglionic cell - SGC small ganglionic cell - 5-HT serotonin     

BP神经网络优化微生物浸矿工艺

以低品位黄铜矿溶液为原料,浸矿制备Cu2＋能有效提高低品位黄铜矿的利用价值。基于浸矿过程中存在多因素影响的现象,通过正交试验与神经网络分析方法,对浸矿条件（接种量、矿石品位、Fe2＋添加量及浸矿溶液pH）实行优化。结果表明：在正交试验组中最佳试验结果为浸矿产128．753mg／LCu^2＋;BP神经网络优化后的最佳实验组合为微生物接种量12％、矿石品位0．3％、添加Fe^2＋24g/L及浸矿溶液pH1．7,该条件下验证试验产Cu^2＋ 141．352mg／L,通过正交试验及神经网络优化提高了微生物浸出低品位黄铜矿酸性溶液Cu^2＋的产量。     

Firing statistics of inhibitory neuron with delayed feedback. I. Output ISI probability density

Activity of inhibitory neuron with delayed feedback is considered in the framework of point stochastic processes. The neuron receives excitatory input impulses from a Poisson stream, and inhibitory impulses from the feedback line with a delay. We investigate here, how does the presence of inhibitory feedback affect the output firing statistics. Using binding neuron (BN) as a model, we derive analytically the exact expressions for the output interspike intervals (ISI) probability density, mean output ISI and coefficient of variation as functions of model's parameters for the case of threshold 2. Using the leaky integrate-and-fire (LIF) model, as well as the BN model with higher thresholds, these statistical quantities are found numerically. In contrast to the previously studied situation of no feedback, the ISI probability densities found here both for BN and LIF neuron become bimodal and have discontinuity of jump type. Nevertheless, the presence of inhibitory delayed feedback was not found to affect substantially the output ISI coefficient of variation. The ISI coefficient of variation found ranges between 0.5 and 1. It is concluded that introduction of delayed inhibitory feedback can radically change neuronal output firing statistics. This statistics is as well distinct from what was found previously (Vidybida and Kravchuk, 2009) by a similar method for excitatory neuron with delayed feedback.     

Analysis of protein transmembrane helical regions by a neural network.

Neural networks were used to generalize common themes found in transmembrane-spanning protein helices. Various-sized databases were used containing nonoverlapping sequences, each 25 amino acids long. Training consisted of sorting these sequences into 1 of 2 groups: transmembrane helical peptides or nontransmembrane peptides. Learning was measured using a test set 10% the size of the training set. As training set size increased from 214 sequences to 1,751 sequences, learning increased in a nonlinear manner from 75% to a high of 98%, then declined to a low of 87%. The final training database consisted of roughly equal numbers of transmembrane (928) and nontransmembrane (1,018) sequences. All transmembrane sequences were entered into the database with respect to their lipid membrane orientation: from inside the membrane to outside. Generalized transmembrane helix and nontransmembrane peptides were constructed from the maximally weighted connecting strengths of fully trained networks. Four generalized transmembrane helices were found to contain 9 consensus residues: a K-R-F triplet was found at the inside lipid interface, 2 isoleucine and 2 other phenylalanine residues were present in the helical body, and 2 tryptophan residues were found near the outside lipid interface. As a test of the training method, bacteriorhodopsin was examined to determine the position of its 7 transmembrane helices.     

Dependency analysis of frequency and strength of gamma oscillations on input difference between excitatory and inhibitory neurons

It has been found that gamma oscillations and the oscillation frequencies are regulated by the properties of external stimuli in many biology experimental researches. To unveil the underlying mechanism, firstly, we reproduced the experimental observations in an excitatory/inhibitory (E/I) neuronal network that the oscillation became stronger and moved to a higher frequency band (gamma band) with the increasing of the input difference between E/I neurons. Secondly, we found that gamma oscillation was induced by the unbalance between positive and negative synaptic currents, which was caused by the input difference between E/I neurons. When this input difference became greater, there would be a stronger gamma oscillation (i.e., a higher peak power in the power spectrum of the population activity of neurons). Further investigation revealed that the frequency dependency of gamma oscillation on the input difference between E/I neurons could be explained by the well-known mechanisms of inter-neuron-gamma (ING) and pyramidal-interneuron-gamma (PING). Finally, we derived mathematical analysis to verify the mechanism of frequency regulations and the results were consistent with the simulation results. The results of this paper provide a possible mechanism for the external stimuli-regulated gamma oscillations.     

人工神经网络在发酵工业中的应用

人工神经网络技术具有很强的非线性映射能力,用于系统的非线性建模,具有无可比拟的优势,广泛应用于发酵过程中培养基的优化和系统建模与控制方面,本主要介绍了人工神经网络的基本原理与使用方法,以及BP神经网络在非线性函数逼近的优点,详细介绍了其在发酵培养基优化,连续搅拌反应器神经网络估计,分批发酵及补料分批发酵过程建模与控制优化中的应用实例。     

Blood-brain barrier modeling with co-cultured neural progenitor cell-derived astrocytes and neurons

In vitro blood-brain barrier (BBB) models often consist of brain microvascular endothelial cells (BMECs) that are co-cultured with other cells of the neurovascular unit, such as astrocytes and neurons, to enhance BBB properties. Obtaining primary astrocytes and neurons for co-culture models can be laborious, while yield and heterogeneity of primary isolations can also be limiting. Neural progenitor cells (NPCs), because of their self-renewal capacity and ability to reproducibly differentiate into tunable mixtures of neurons and astrocytes, represent a facile, readily scalable alternative. To this end, differentiated rat NPCs were co-cultured with rat BMECs and shown to induce BBB properties such as elevated trans-endothelial electrical resistance, improved tight junction continuity, polarized p-glycoprotein efflux, and low passive permeability at levels indistinguishable from those induced by primary rat astrocyte co-culture. An NPC differentiation time of 12 days, with the presence of 10% fetal bovine serum, was found to be crucial for generating NPC-derived progeny capable of inducing the optimal response. This approach could also be extended to human NPC-derived astrocytes and neurons which similarly regulated BBB induction. The distribution of rat or human NPC-derived progeny under these conditions was found to be a roughly 3 : 1 mixture of astrocytes to neurons with varying degrees of cellular maturity. BMEC gene expression analysis was conducted using a BBB gene panel, and it was determined that 23 of 26 genes were similarly regulated by either differentiated rat NPC or rat astrocyte co-culture while three genes were differentially altered by the rat NPC-derived progeny. Taken together, these results demonstrate that NPCs are an attractive alternative to primary neural cells for use in BBB co-culture models.     

Computational simulation: astrocyte-induced depolarization of neighboring neurons mediates synchronous UP states in a neural network

Although recent reports have suggested that synchronous neuronal UP states are mediated by astrocytic activity, the mechanism responsible for this remains unknown. Astrocytic glutamate release synchronously depolarizes adjacent neurons, while synaptic transmissions are blocked. The purpose of this study was to confirm that astrocytic depolarization, propagated through synaptic connections, can lead to synchronous neuronal UP states. We applied astrocytic currents to local neurons in a neural network consisting of model cortical neurons. Our results show that astrocytic depolarization may generate synchronous UP states for hundreds of milliseconds in neurons even if they do not directly receive glutamate release from the activated astrocyte.     

Exponential input-to-state stability of recurrent neural networks with multiple time-varying delays

In this paper, input-to-state stability problems for a class of recurrent neural networks model with multiple time-varying delays are concerned with. By utilizing the Lyapunov–Krasovskii functional method and linear matrix inequalities techniques, some sufficient conditions ensuring the exponential input-to-state stability of delayed network systems are firstly obtained. Two numerical examples and its simulations are given to illustrate the efficiency of the derived results.     

On the stationary state of a network of inhibitory spiking neurons

The background activity of a cortical neural network is modeled by a homogeneous integrate-and-fire network with unreliable inhibitory synapses. For the case of fast synapses, numerical and analytical calculations show that the network relaxes into a stationary state of high attention. The majority of the neurons has a membrane potential just below the threshold; as a consequence the network can react immediately – on the time scale of synaptic transmission- on external pulses. The neurons fire with a low rate and with a broad distribution of interspike intervals. Firing events of the total network are correlated over short time periods. The firing rate increases linearly with external stimuli. In the limit of infinitely large networks, the synaptic noise decreases to zero. Nevertheless, the distribution of interspike intervals remains broad. Action Editor: Misha Tsodyks     

Effect of inhibitory feedback on correlated firing of spiking neural network

Understanding the properties and mechanisms that generate different forms of correlation is critical for determining their role in cortical processing. Researches on retina, visual cortex, sensory cortex, and computational model have suggested that fast correlation with high temporal precision appears consistent with common input, and correlation on a slow time scale likely involves feedback. Based on feedback spiking neural network model, we investigate the role of inhibitory feedback in shaping correlations on a time scale of 100 ms. Notably, the relationship between the correlation coefficient and inhibitory feedback strength is non-monotonic. Further, computational simulations show how firing rate and oscillatory activity form the basis of the mechanisms underlying this relationship. When the mean firing rate holds unvaried, the correlation coefficient increases monotonically with inhibitory feedback, but the correlation coefficient keeps decreasing when the network has no oscillatory activity. Our findings reveal that two opposing effects of the inhibitory feedback on the firing activity of the network contribute to the non-monotonic relationship between the correlation coefficient and the strength of the inhibitory feedback. The inhibitory feedback affects the correlated firing activity by modulating the intensity and regularity of the spike trains. Finally, the non-monotonic relationship is replicated with varying transmission delay and different spatial network structure, demonstrating the universality of the results.