Spinal motor control system incorporates an internal model of limb dynamics

The existence and utilization of an internal representation of the controlled object is one of the most important features of the functioning of neural motor control systems. This study demonstrates that this property already exists at the level of the spinal motor control system (SMCS), which is capable of generating motor patterns for reflex rhythmic movements, such as locomotion and scratching, without the aid of the peripheral afferent feedback, but substantially modifies the generated activity in response to peripheral afferent stimuli. The SMCS is presented as an optimal control system whose optimality requires that it incorporate an internal model (IM) of the controlled object's dynamics. A novel functional mechanism for the integration of peripheral sensory signals with the corresponding predictive output from the IM, the summation of information precision (SIP) is proposed. In contrast to other models in which the correction of the internal representation of the controlled object's state is based on the calculation of a mismatch between the internal and external information sources, the SIP mechanism merges the information from these sources in order to optimize the precision of the controlled object's state estimate. It is demonstrated, based on scratching in decerebrate cats as an example of the spinal control of goal-directed movements, that the results of computer modeling agree with the experimental observations related to the SMCS's reactions to phasic and tonic peripheral afferent stimuli. It is also shown that the functional requirements imposed by the mathematical model of the SMCS comply with the current knowledge about the related properties of spinal neuronal circuitry. The crucial role of the spinal presynaptic inhibition mechanism in the neuronal implementation of SIP is elucidated. Important differences between the IM and a state predictor employed for compensating for a neural reflex time delay are discussed. Received: 8 February 2000 / Accepted: 24 March 2000     

A simple model based on the first principles

A simple model based on the first principles is proposed, which describes the reduplication of a molecular structure as a sequence of stages with probabilities that can be estimated on the basis of the general theory of molecular transformations. It is demonstrated that the reproduction of genetic information in time (life phenomenon) over a long period is impossible without the simultaneous "death effect". Taking into account the peculiarities of transient states (quantum beats) leads to the appearance of self-oscillations.     

A computational model for predicting protein interactions based on multidomain collaboration

Recently, several domain-based computational models for predicting protein-protein interactions (PPIs) have been proposed. The conventional methods usually infer domain or domain combination (DC) interactions from already known interacting sets of proteins, and then predict PPIs using the information. However, the majority of these models often have limitations in providing detailed information on which domain pair (single domain interaction) or DC pair (multidomain interaction) will actually interact for the predicted protein interaction. Therefore, a more comprehensive and concrete computational model for the prediction of PPIs is needed. We developed a computational model to predict PPIs using the information of intraprotein domain cohesion and interprotein DC coupling interaction. A method of identifying the primary interacting DC pair was also incorporated into the model in order to infer actual participants in a predicted interaction. Our method made an apparent improvement in the PPI prediction accuracy, and the primary interacting DC pair identification was valid specifically in predicting multidomain protein interactions. In this paper, we demonstrate that 1) the intraprotein domain cohesion is meaningful in improving the accuracy of domain-based PPI prediction, 2) a prediction model incorporating the intradomain cohesion enables us to identify the primary interacting DC pair, and 3) a hybrid approach using the intra/interdomain interaction information can lead to a more accurate prediction.     

Hybrid FMS control architectures based on holonic principles

The escalation in processor technologies and the corresponding reduction in costs have enabled alternative FMS control architectures to be developed without the restrictions of “fixed machine controller boundaries”. These new architectures can be based upon the use of intelligent servo axes, which are desccribed in this article, as flexible numerical control (FNC). In current parlance, the FNC is a “part movement holon” within a manufacturing cell. The control architectures that can be derived from the FNC concept are referred to as hybrid architectures and share the emerging attributes of holonics. This article details the problems that arise in the scheduling and control of FMSs in the light of hybrid control architectures. A number of traditional scheduling approaches have been devised to cope with the scheduling of parts to discrete machines, but the problem here is to ascribe the processing (machining) of part features to axis groups. This article documents how two research programs, undertaken at the CIM Centre at Swinburne University of Technology in Hawthorn, Victoria, Australia, have endeavored to address the problem of hybrid architectures and their associated scheduling.     

A computational model for the primate neocortex based on its functional architecture

Experimental evidence has shown that the primate neocortex consists in the main of a set of cortical regions which form a perception hierarchy, an action hierarchy and connections between them. By using a computer science analysis, we develop a computational architecture for the brain in which each cortical region is represented by a computational module with processing and storage abilities. Modules are interconnected according to the connectivity of the corresponding cortical regions. We develop computational principles for designing such a hierarchical and parallel computing system. We demonstrate this approach by proposing a causal functioning model of the brain. We report on results obtained with an implementation of this model. We conclude with a brief discussion of some consequences and predictions of our work.     

A new computational model for protein folding based on atomic solvation.

A new model for calculating the solvation energy of proteins is developed and tested for its ability to identify the native conformation as the global energy minimum among a group of thousands of computationally generated compact non-native conformations for a series of globular proteins. In the model (called the WZS model), solvation preferences for a set of 17 chemically derived molecular fragments of the 20 amino acids are learned by a training algorithm based on maximizing the solvation energy difference between native and non-native conformations for a training set of proteins. The performance of the WZS model confirms the success of this learning approach; the WZS model misrecognizes (as more stable than native) only 7 of 8,200 non-native structures. Possible applications of this model to the prediction of protein structure from sequence are discussed.     

A computational model of four regions of the cerebellum based on feedback-error learning

We propose a computationally coherent model of cerebellar motor learning based on the feedback-error-learning scheme. We assume that climbing fiber responses represent motor-command errors generated by some of the premotor networks such as the feedback controllers at the spinal-, brain stem- and cerebral levels. Thus, in our model, climbing fiber responses are considered to convey motor errors in the motor-command coordinates rather than in the sensory coordinates. Based on the long-term depression in Purkinje cells each corticonuclear microcomplex in different regions of the cerebellum learns to execute predictive and coordinative control of different types of movements. Ultimately, it acquires an inverse model of a specific controlled object and complements crude control by the premotor networks. This general model is developed in detail as a specific neural circuit model for the lateral hemisphere. A new experiment is suggested to elucidate the coordinate frame in which climbing fiber responses are represented.     

A computational model of dendrite elongation and branching based on MAP2 phosphorylation

We introduce a new computational model of dendritic development in neurons. In contrast to previous models, our model explicitly includes cellular mechanisms involved in dendritic development. It is based on recent experimental data which indicates that the phosphorylation state of microtubule-associated protein 2 (MAP2) may play a key role in controlling dendritic elongation and branching (Audesirk et al., 1997). Dephosphorylated MAP2 favours elongation by promoting microtubule polymerization and bundling, whilst branching is more likely to occur when MAP2 is phosphorylated and microtubules are spaced apart. In the model, the rate of elongation and branching is directly determined by the ratio of phosphorylated to dephosphorylated MAP2. This is regulated by calmodulin-dependent protein kinase II (CaMKII) and calcineurin, which are both dependent on the intracellular calcium concentration. Results from computer simulations of the model suggest that the wide variety of branching patterns observed among different cell types may be generated by the same underlying mechanisms and that elongation and branching are not necessarily independent processes. The model predicts how the branching pattern will change following manipulations with calcium, CaMKII and MAP2 phosphorylation.     

A computational model of in vitro angiogenesis based on extracellular matrix fibre orientation

Recent interest in the process of vascularisation within the biomedical community has motivated numerous new research efforts focusing on the process of angiogenesis. Although the role of chemical factors during angiogenesis has been well documented, the role of mechanical factors, such as the interaction between angiogenic vessels and the extracellular matrix, remains poorly understood. In vitro methods for studying angiogenesis exist; however, measurements available using such techniques often suffer from limited spatial and temporal resolutions. For this reason, computational models have been extensively employed to investigate various aspects of angiogenesis. This paper outlines the formulation and validation of a simple and robust computational model developed to accurately simulate angiogenesis based on length, branching and orientation morphometrics collected from vascularised tissue constructs. Microvessels were represented as a series of connected line segments. The morphology of the vessels was determined by a linear combination of the collagen fibre orientation, the vessel density gradient and a random walk component. Excellent agreement was observed between computational and experimental morphometric data over time. Computational predictions of microvessel orientation within an anisotropic matrix correlated well with experimental data. The accuracy of this modelling approach makes it a valuable platform for investigating the role of mechanical interactions during angiogenesis.     

Rational pH-engineering of the thermostable xylanase based on computational model

Locating sites of amino acids related to enzyme properties is still a challenge for rational engineering. Based on the strategy that sites of amino acids can be located by dipeptides, a computational model was made for pH-related dipeptides of xylanase. According to the dipeptide model, pH of a thermostable xylanase B from Thermotoga maritima was rationally designed by locating pH-related amino acids in its sequence and structure. In agreement with expectation, the optimum pH (pH_opt) of the xylanase was improved by five amino acids substitutions: E70Q, E74Q, E77Q, G85Q and T87Q. In parallel assay reactions, at 90 °C, its pH_opt increases to 5.5 from 5.1, and its whole pH profile also shifts 0.5 units towards alkaline area; at 80 °C, the relative activity decreases very little over a wide pH range from 5.25 to 6.0. This result demonstrated that the bioinformatics model is useful for pH rational design and engineering of xylanase, a model molecular of a large family of ～10% proteins with (β/α)₈-barrel structure.     

A gain-field encoding of limb position and velocity in the internal model of arm dynamics

Adaptability of reaching movements depends on a computation in the brain that transforms sensory cues, such as those that indicate the position and velocity of the arm, into motor commands. Theoretical consideration shows that the encoding properties of neural elements implementing this transformation dictate how errors should generalize from one limb position and velocity to another. To estimate how sensory cues are encoded by these neural elements, we designed experiments that quantified spatial generalization in environments where forces depended on both position and velocity of the limb. The patterns of error generalization suggest that the neural elements that compute the transformation encode limb position and velocity in intrinsic coordinates via a gain-field; i.e., the elements have directionally dependent tuning that is modulated monotonically with limb position. The gain-field encoding makes the counterintuitive prediction of hypergeneralization: there should be growing extrapolation beyond the trained workspace. Furthermore, nonmonotonic force patterns should be more difficult to learn than monotonic ones. We confirmed these predictions experimentally.     

Measuring electric impedance of organs--methodologic principles]

Ischemia causes changes in organ tissue (e.g. during operation or transplantation) which may finally lead to irreversible injury, so that the organ can no longer be resuscitated. To the extent that these changes affect the electrical properties of the tissue they are manifested in the impedance spectrum. As an example, the course of impedance of a HTK-protected porcine liver is presented in the frequency range of 0.1 Hz to 10 MHz, which includes two dispersion--alpha- and beta-dispersion. Using a suitable electrical equivalent circuit analogue to the structure of the liver, the behavior of the alpha- and beta-dispersion is explained on the basis of gap junction closure and narrowing of the extracellular space due to cell swelling.     

Responsive neuromodulators based on artificial neural networks used to control seizure-like events in a computational model of epilepsy

Deep brain stimulation (DBS) has been noted for its potential to suppress epileptic seizures. To date, DBS has achieved mixed results as a therapeutic approach to seizure control. Using a computational model, we demonstrate that high-complexity, biologically-inspired responsive neuromodulation is superior to periodic forms of neuromodulation (responsive and non-responsive) such as those implemented in DBS, as well as neuromodulation using random and random repetitive-interval stimulation. We configured radial basis function (RBF) networks to generate outputs modeling interictal time series recorded from rodent hippocampal slices that were perfused with low Mg2?/high K? solution. We then compared the performance of RBF-based interictal modulation, periodic biphasic-pulse modulation, random modulation and random repetitive modulation on a cognitive rhythm generator (CRG) model of spontaneous seizure-like events (SLEs), testing efficacy of SLE control. A statistically significant improvement in SLE mitigation for the RBF interictal modulation case versus the periodic and random cases was observed, suggesting that the use of biologically-inspired neuromodulators may achieve better results for the purpose of electrical control of seizures in a clinical setting.     

A computational model of visual anisotropy

Visual anisotropy has been demonstrated in multiple tasks where performance differs between vertical, horizontal, and oblique orientations of the stimuli. We explain some principles of visual anisotropy by anisotropic smoothing, which is based on a variation on Koenderink's approach in [1]. We tested the theory by presenting gaussian elongated luminance profiles and measuring the perceived orientations by means of an adjustment task. Our framework is based on the smoothing of the image with elliptical gaussian kernels and it correctly predicted an illusory orientation bias towards the vertical axis. We discuss the scope of the theory in the context of other anisotropies in perception.     

A computational model for cell differentiation

In this paper, we present a word set generating mechanism, called cell-differentiation system, inspired by the tissue process formation in multicellular organisms, which might model some properties of evolving communities of living cells at the syntactical level. The tools utilized to model these biological phenomena belong to the formal language theory. In this context chromosomal mutations are defined as operations on strings and the differentiation according to the control of gene expression is represented by some random-context conditions in formal languages.In the presented formal framework we prove that in a simplified form of this formalism, with only one cell-type which is regular, one single cell and no mitosis involved, the problem of establishing whether or not the set of vectors of integers indicating the number of cells in each population, is finite, linear or semilinear, is recursively undecidable. However, one can algorithmically decide whether or not a cell-differentiation system of finite cell-type can produce a specific generation of cells.     

A balance control model of quiet upright stance based on an optimal control strategy

Models of balance control can aid in understanding the mechanisms by which humans maintain balance. A balance control model of quiet upright stance based on an optimal control strategy is presented here. In this model, the human body was represented by a simple single-segment inverted pendulum during upright stance, and the neural controller was assumed to be an optimal controller that generates ankle control torques according to a certain performance criterion. This performance criterion was defined by several physical quantities relevant to sway. In order to accurately simulate existing experimental data, an optimization procedure was used to specify the set of model parameters to minimize the scalar error between experimental and simulated sway measures. Thirty-two independent simulations were performed for both younger and older adults. The model's capabilities, in terms of reflecting sway behaviors and identifying aging effects, were then analyzed based on the simulation results. The model was able to accurately predict center-of-pressure-based sway measures, and identify potential changes in balance control mechanisms caused by aging. Correlations between sway measures and model parameters are also discussed.