Spiking behavior and epileptiform oscillations in a discrete model of cortical neural networks

Summary We investigate the phenomenon of epileptiform activity using a discrete model of cortical neural networks. Our model is reduced to the elementary features of neurons and assumes simplified dynamics of action potentials and postsynaptic potentials. The discrete model provides a comparably high simulation speed which allows the rendering of phase diagrams and simulations of large neural networks in reasonable time. Further the reduction to the basic features of neurons provides insight into the essentials of a possible mechanism of epilepsy. Our computer simulations suggest that the detailed dynamics of postsynaptic and action potentials are not indispensable for obtaining epileptiform behavior on the system level. The simulation results of autonomously evolving networks exhibit a regime in which the network dynamics spontaneously switch between fluctuating and oscillating behavior and produce isolated network spikes without external stimulation. Inhibitory neurons have been found to play an important part in the synchronization of neural firing: an increased number of synapses established by inhibitory neurons onto other neurons induces a transition to the spiking regime. A decreased frequency accompanying the hypersynchronous population activity has only occurred with slow inhibitory postsynaptic potentials.     

Simulation of an iterative learning control system for fed-batch cell culture processes

This paper describes an iterative learning control scheme for fed-batch operation where repetitive trajectory tracking tasks are required. The proposed learning strategy is model-independent, and it takes advantage of the repetitive feature of system operations with a certain degree of intelligence and requires only small size of dynamic database for the learning process. The convergence of the learning process is proven. An example of simultaneously tracking two predefined trajectories by iterative learning control with two control inputs is given to illustrate the methodology. Satisfactory performance of the learning system can be observed from the simulation results.     

Two-point heterogeneous connections in a continuum neural field model

We examine a novel heterogeneous connection scheme in a 1D continuum neural field model. Multiple two-point connections are added to a local connection function in order to model the “patchy” connections seen in, for example visual cortex. We use a numerical approach to solve the equations, choosing the locations of the two-point connections stochastically. We observe self-sustained persistent fluctuations of activity which can be classified into two types (one of which is similar to that seen in network models of discrete excitable neurons, the other being particular to this model). We study the effect of parameters such as system size and the range, number and strength of connections, on the probability that a particular realisation of the connections is able to exhibit persistent fluctuations.     

A model for control of breathing in mammals: coupling neural dynamics to peripheral gas exchange and transport

A new model for aspects of the control of respiration in mammals has been developed. The model integrates a reduced representation of the brainstem respiratory neural controller together with peripheral gas exchange and transport mechanisms. The neural controller consists of two components. One component represents the inspiratory oscillator in the pre-Bötzinger complex (pre-BötC) incorporating biophysical mechanisms for rhythm generation. The other component represents the ventral respiratory group (VRG), which is driven by the pre-BötC for generation of inspiratory (pre)motor output. The neural model was coupled to simplified models of the lungs incorporating oxygen and carbon dioxide transport. The simplified representation of the brainstem neural circuitry has regulation of both frequency and amplitude of respiration and is done in response to partial pressures of oxygen and carbon dioxide in the blood using proportional (P) and proportional plus integral (PI) controllers. We have studied the coupled system under open and closed loop control. We show that two breathing regimes can exist in the model. In one regime an increase in the inspiratory frequency is accompanied by an increase in amplitude. In the second regime an increase in frequency is accompanied by a decrease in amplitude. The dynamic response of the model to changes in the concentration of inspired O₂ or inspired CO₂ was compared qualitatively with experimental data reported in the physiological literature. We show that the dynamic response with a PI-controller fits the experimental data better but suggests that when high levels of CO₂ are inspired the respiratory system cannot reach steady state. Our model also predicts that there could be two possible mechanisms for apnea appearance when 100% O₂ is inspired following a period of 5% inspired O₂. This paper represents a novel attempt to link neural control and gas transport mechanisms, highlights important issues in amplitude and frequency control and sets the stage for more complete neurophysiological control models.     

Bionomics of Prays citri (Lepidoptera: Yponomeutidae) and their use in a model of control by male mass trapping*

Abstract. 1. The paper describes the population dynamics of Prays citri (Milliere), relevant to the control of this pest by synthetic pheromone, as well as infestation of lemon trees in various areas and seasons. 2. The responses of males to natural and synthetic sex pheromones are compared, and ratios for the degree of attraction of traps compared with the degree of attraction of competing wild females are calculated. 3. The ratios obtained with various population densities over two seasons of the pest's activity are presented. 4. The data are used in a model simulating control of Prays citri by male mass trapping.     

Neuronal dynamics during the learning of trace conditioning in a CA3 model of hippocampal function

The present article develops quantitative behavioral and neurophysiological predictions for rabbits trained on an air-puff version of the trace-interval classical conditioning paradigm. Using a minimal hippocampal model, consisting of 8,000 primary cells sparsely and randomly interconnected as a model of hippocampal region CA-3, the simulations identify conditions which produce a clear split in the number of trials individual animals should need to learn a criterion response. A trace interval that is difficult to learn, but still learnable by half the experimental population, produces a bimodal population of learners: an early learner group and a late learner group. The model predicts that late learners are characterized by two kinds of CA-3 neuronal activity fluctuations that are not seen in the early learners. As is typical in our minimal hippocampal models, the off-rate constant of the N-methyl-d-aspartate receptor receptor gives a timescale to the model that leads to a temporally quantifiable behavior, the learnable trace interval.     

Functional model of biological neural networks

A functional model of biological neural networks, called temporal hierarchical probabilistic associative memory (THPAM), is proposed in this paper. THPAM comprises functional models of dendritic trees for encoding inputs to neurons, a first type of neuron for generating spike trains, a second type of neuron for generating graded signals to modulate neurons of the first type, supervised and unsupervised Hebbian learning mechanisms for easy learning and retrieving, an arrangement of dendritic trees for maximizing generalization, hardwiring for rotation-translation-scaling invariance, and feedback connections with different delay durations for neurons to make full use of present and past informations generated by neurons in the same and higher layers. These functional models and their processing operations have many functions of biological neural networks that have not been achieved by other models in the open literature and provide logically coherent answers to many long-standing neuroscientific questions. However, biological justifications of these functional models and their processing operations are required for THPAM to qualify as a macroscopic model (or low-order approximate) of biological neural networks.     

Learning and coding of concepts in neural networks

Our environment consists of virtually an infinite number of scenarios in which we have to function. In order to respond properly to an incoming stimulus, the brain has first to analyze it, and to find out the basic familiar elements that are part of it. In other words, by using a library which contains a relatively small number of basic concepts, the brain analyzes the multitude of incoming events. Some of those basic concepts are innate, but many of them must be learned, in order to accommodate for the arbitrary environment around us. A classifying box is defined as the neural network that finds out the familiar concepts that are present in an incoming stimulus. Models for classifying boxes are introduced, and possible mechanisms by which they may establish their libraries of concepts are suggested, and then compared and evaluated by computer simulations.     

Efficient nonlinear predictive control of a biochemical reactor using neural models

This paper describes the application of artificial neural networks to modelling and control of a continuous fermentor. A computationally efficient nonlinear model predictive control (MPC) algorithm with nonlinear prediction and linearisation (MPC-NPL) which needs solving on-line a quadratic programming problem is developed. It is demonstrated that the algorithm results in closed-loop control performance similar to that obtained in nonlinear MPC, which hinges on full on-line non-convex optimisation. The computational complexity of the MPC-NPL algorithm is shown, control accuracy and robustness are also demonstrated in the case of noisy measurements and disturbances affecting the process.     

Changes in stimulus control during guided skill learning in rats

We examined the changes in stimulus control occurring during guided skill learning in rats. Twenty rats were trained to complete a left-right sequence of lever presses guided by the onset and offset of panel lights over their respective levers. Once sequence accuracy was high and stable, the rats were divided into two groups. For the No-Lights group, the lights were eliminated without changing the response requirements. Sequence accuracy decreased in all subjects, but accuracy was higher than that predicted by random chance. More practice produced greater autonomy and reduced dependence on the guiding lights. For the Reversed-Lights group, the lights were presented in reversed order without changing the response requirements. Sequence accuracy immediately plummeted and did not recover, violating expectations of automatization. The guiding lights appeared to overshadow other sources of stimulus control.     

Evidence for the involvement of a SchistoFLRF-amide-like peptide in the neural control of locust oviduct

Summary The presence of a SchistoFLRFamide-like peptide associated with the oviducts of Locusta migratoria has been shown using sequential reversed-phase high performance liquid chromatography separation coupled with radioimmunoassay and bioassay. The peptide is present in areas of the oviduct which receive extensive innervation, with sixfold less peptide in areas that receive little innervation. Material with FMRFamide-like immunoreactivity (determined by radioimmunoassay) is also present in the oviducal nerve and VIIth abdominal ganglion.SchistoFLRFamide is a potent modulator of contraction of this visceral muscle, inhibiting or reducing the amplitude and frequency of spontaneous contractions, relaxing basal tonus, and reducing the amplitude of neurally-evoked, proctolin-induced, glutamate-induced and high potassium-induced contractions. The FMRFamide-like immunoreactivity within the oviducts which co-elutes with SchistoFLRFamide on two separations is also capable of reducing the amplitude of neurally-evoked and proctolin-induced contractions, and of inhibiting spontaneous contractions and relaxing basal tonus.The effects of SchistoFLRFamide upon this visceral muscle are not abolished by the -adrenergic receptor antagonist phentolamine and do not appear to be mediated by cyclic AMP. Thus the receptors for Schisto-FLRFamide are distinct from those of octopamine which mediate similar physiological effects but which are blocked by phentolamine and which are coupled to adenylate cyclase.The results indicate that SchistoFLRFamide, or a very similar peptide, which has previously been identified as a modulator of locust heart beat, is also associated with visceral muscle of the reproductive system, and may play a neural role in concert with octopamine, at modulating muscular activity.Abbreviations BPP Bovine pancreatic polypeptide - BSA Bovine serum albumin - EJP Excitatory junctional potential - FaRPs FMRFamide-related peptides - FLI FMRFamide-like immuno-reactivity - LMS Leucomyosuppressin - RIA Radioimmunoassay - RP-HPLC Reversed-phase high performance liquid chromatography - TFA Trifluoroacetic acid     

A linear model for characterization of synchronization frequencies of neural networks

The synchronization frequency of neural networks and its dynamics have important roles in deciphering the working mechanisms of the brain. It has been widely recognized that the properties of functional network synchronization and its dynamics are jointly determined by network topology, network connection strength, i.e., the connection strength of different edges in the network, and external input signals, among other factors. However, mathematical and computational characterization of the relationships between network synchronization frequency and these three important factors are still lacking. This paper presents a novel computational simulation framework to quantitatively characterize the relationships between neural network synchronization frequency and network attributes and input signals. Specifically, we constructed a series of neural networks including simulated small-world networks, real functional working memory network derived from functional magnetic resonance imaging, and real large-scale structural brain networks derived from diffusion tensor imaging, and performed synchronization simulations on these networks via the Izhikevich neuron spiking model. Our experiments demonstrate that both of the network synchronization strength and synchronization frequency change according to the combination of input signal frequency and network self-synchronization frequency. In particular, our extensive experiments show that the network synchronization frequency can be represented via a linear combination of the network self-synchronization frequency and the input signal frequency. This finding could be attributed to an intrinsically-preserved principle in different types of neural systems, offering novel insights into the working mechanism of neural systems.     

Temporal correlation based learning in neuron models

We study a learning rule based upon the temporal correlation (weighted by a learning kernel) between incoming spikes and the internal state of the postsynaptic neuron, building upon previous studies of spike timing dependent synaptic plasticity (Kempter, R., Gerstner, W., van Hemmen, J.L., Wagner, H., 1998. Extracting Oscillations: Neuronal coincidence detection with noisy periodic spike input. Neural computation 10, 1987–2017; Kempter, R., Gerstner, W., van Hemmen, J.L., 1999. Hebbian learning and spiking neurons. Physical Reviewm E59, 4498–4514; van Hemmen, J.L., 2001. Theory of synaptic plasticity. In: Moss, F., Gielen, S. (Eds.), Handbook of biological physics. vol. 4, Neuro Informatics, neural modelling, Elsevier, Amsterdam, pp. 771–823. Our learning rule for the synaptic weight w _ij is where the t _j,μ are the arrival times of spikes from the presynaptic neuron j and the function u(t) describes the state of the postsynaptic neuron i. Thus, the spike-triggered average contained in the inner integral is weighted by a kernel Γ(s), the learning window, positive for negative, negative for positive values of the time difference s between post- and presynaptic activity. An antisymmetry assumption for the learning window enables us to derive analytical expressions for a general class of neuron models and to study the changes in input-output relationships following from synaptic weight changes. This is a genuinely non-linear effect (Song, S., Miller, K., Abbott, L., 2000. Competitive Hebbian learning through spike timing dependent synaptic plasticity. Nature Neuroscience 3, 919–926).     

Quantum-like model of behavioral response computation using neural oscillators

In this paper we propose the use of neural interference as the origin of quantum-like effects in the brain. We do so by using a neural oscillator model consistent with neurophysiological data. The model used was shown elsewhere to reproduce well the predictions of behavioral stimulus-response theory. The quantum-like effects are brought about by the spreading activation of incompatible oscillators, leading to an interference-like effect mediated by inhibitory and excitatory synapses.     

Computer control of pH and DO in a laboratory fermenter using a neural network technique

In this contribution, the advantages of the artificial neural network approach to the identification and control of a laboratory-scale biochemical reactor are demonstrated. It is very important to be able to maintain the levels of two process variables, pH and dissolved oxygen (DO) concentration, over the course of fermentation in biosystems control. A PC-supported, fully automated, multi-task control system has been designed and built by the authors. Forward and inverse neural process models are used to identify and control both the pH and the DO concentration in a fermenter containing a Saccharomyces cerevisiae based-culture. The models are trained off-line, using a modified back-propagation algorithm based on conjugate gradients. The inverse neural controller is augmented by a new adaptive term that results in a system with robust performance. Experimental results have confirmed that the regulatory and tracking performances of the control system proposed are good.     

Acceleration of mass transfer in methane-producing loop reactors

Gas bubbles entrapped in methanogenic granules subjected to hydrostatic pressure oscillations during recirculation in loop reactors will induce intraparticle liquid flows, and thereby enhance mass transfer in excess of diffusion. This breathing particle concept was clearly demonstrated in a well defined inorganic model system. The experimental results could be described satisfactory with a structured mathematical model, while a 30% improvement is predicted for methanogenic loop reactors as compared to constant pressure systems. It is concluded that acceleration of mass transfer in gas-producing systems offers challenging perspectives for both heterogeneous catalysis and biological fermentations.     

Complementary roles of basal ganglia and cerebellum in learning and motor control

The classical notion that the basal ganglia and the cerebellum are dedicated to motor control has been challenged by the accumulation of evidence revealing their involvement in non-motor, cognitive functions. From a computational viewpoint, it has been suggested that the cerebellum, the basal ganglia, and the cerebral cortex are specialized for different types of learning: namely, supervised learning, reinforcement learning and unsupervised learning, respectively. This idea of learning-oriented specialization is helpful in understanding the complementary roles of the basal ganglia and the cerebellum in motor control and cognitive functions.     

Towards a unifying model of neural net activity in the visual cortex

A neural net model describing the non-linear interactions between axonal spikes is presented. It reconciles aspects of pattern recognition (as action of an associative memory) with those of spike synchronization and phase locking. The stability of the synchronized state is studied in detail.     

A neural network model of attention-modulated neurodynamics

Visual attention appears to modulate cortical neurodynamics and synchronization through various cholinergic mechanisms. In order to study these mechanisms, we have developed a neural network model of visual cortex area V4, based on psychophysical, anatomical and physiological data. With this model, we want to link selective visual information processing to neural circuits within V4, bottom-up sensory input pathways, top-down attention input pathways, and to cholinergic modulation from the prefrontal lobe. We investigate cellular and network mechanisms underlying some recent analytical results from visual attention experimental data. Our model can reproduce the experimental findings that attention to a stimulus causes increased gamma-frequency synchronization in the superficial layers. Computer simulations and STA power analysis also demonstrate different effects of the different cholinergic attention modulation action mechanisms.     

A synthetic peptide ligand of neural cell adhesion molecule (NCAM) IgI domain prevents NCAM internalization and disrupts passive avoidance learning

The neural cell adhesion molecule (NCAM) mediates cell adhesion and signal transduction through trans-homophilic- and/or cis-heterophilic-binding mechanisms. Intraventricular infusions of anti-NCAM have revealed a functional requirement of NCAM for the consolidation of memory in rats and chicks in a specific interval 6-8 h after training. We have now extended these studies to a synthetic peptide ligand of NCAM (C3) with an affinity for the IgI domain and the capability of inhibiting NCAM-mediated neurite outgrowth in vitro. Intraventricular administration of a single 5 microg bolus of C3 strongly inhibited recall of a passive avoidance response in adult rats, when given during training or in the 6-8-h posttraining period. The effect of C3 on memory consolidation was similar to that obtained with anti-NCAM as the amnesia was not observed until the 48-h recall time. The unique amnesic action of C3 during training could be related to disrupted NCAM internalization following training. In the 3-4-h posttraining period NCAM 180, the synapse-associated isoform, was down-regulated in the hippocampal dentate gyrus. This effect was mediated by ubiquitination and was prevented by C3 administration during training. These findings indicate NCAM to be involved in both the acquisition and consolidation of a passive avoidance response in the rat. Moreover, the study provides the first in vivo evidence for NCAM internalization in learning and identifies a synthetic NCAM ligand capable of modulating memory processes in vivo.