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Register a new userPerformance augmentation in hybrid bionic systems: techniques and experiment
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Bradly Alicea
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Recent developments in hybrid biological-technological systems (hybrid bionic systems) has made clear the need for evaluating ergonomic fit in such systems, especially as users first become adjusted to using such systems. This training is accompanied by physiological adaptation, and can be thought of computationally as a relative degree of matching between prosthetic devices, physiology, and behavior. Achieving performance augmentation involves two features of performance: a specific form of learning, memory, and mechanotransduction called sensorimotor learning, and physiological adaptation to novel physical information imposed by the augmented environment of hybrid bionic systems. A method borrowed from environmental medicine involving perturbing the environment for a range of internal physiological conditions was used to induce sensorimotor learning and memory associated physiological changes. In addition, features of the adult phenotype were considered as a mitigator of learning-related adaptations. Using a series of statistical tests and techniques, the results demonstrate than three forms of regulation are at work related to morphological, neural, and muscular control. A discussion of the conceptual relationship between homeostasis and adaptation will then be discussed in addition to potential applications to performance augmentation strategies.
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Seizure activity is a ubiquitous and pernicious pathophysiology that, in principle, should yield to mathematical treatments of (neuronal) ensemble dynamics - and therefore interventions on stochastic chaos. A seizure can be characterised as a deviation of neural activity from a stable dynamical regime, i.e. one in which signals fluctuate only within a limited range. In silico treatments of neural activity are an important tool for understanding how the brain can achieve stability, as well as how pathology can lead to seizures and potential strategies for mitigating instabilities, e.g. via external stimulation. Here, we demonstrate that the (neuronal) state equation used in Dynamic Causal Modelling generalises to a Fokker-Planck formalism when propagation of neuronal activity along structural connections is considered. Using the Jacobian of this generalised state equation, we show that an initially unstable system can be rendered stable via a reduction in diffusivity (i.e., connectivity that disperses neuronal fluctuations). We show, for neural systems prone to epileptic seizures, that such a reduction can be achieved via external stimulation. Specifically, we show that this stimulation should be applied in such a way as to temporarily mirror epileptic activity in the areas adjoining an affected brain region - thus fighting seizures with seizures. We offer proof of principle using simulations based on functional neuroimaging data collected from patients with idiopathic generalised epilepsy, in which we successfully suppress pathological activity in a distinct sub-network. Our hope is that this technique can form the basis for real-time monitoring and intervention devices that are capable of suppressing or even preventing seizures in a non-invasive manner.
The concepts and methods of Systems Biology are being extended to neuropharmacology, to test and design drugs against neurological and psychiatric disorders. Computational modeling by integrating compartmental neural modeling technique and detailed kinetic description of pharmacological modulation of transmitter-receptor interaction is offered as a method to test the electrophysiological and behavioral effects of putative drugs. Even more, an inverse method is suggested as a method for controlling a neural system to realize a prescribed temporal pattern. In particular, as an application of the proposed new methodology a computational platform is offered to analyze the generation and pharmacological modulation of theta rhythm related to anxiety is analyzed here in more detail.
In recent years, the field of neuroscience has gone through rapid experimental advances and extensive use of quantitative and computational methods. This accelerating growth has created a need for methodological analysis of the role of theory and the modeling approaches currently used in this field. Toward that end, we start from the general view that the primary role of science is to solve empirical problems, and that it does so by developing theories that can account for phenomena within their domain of application. We propose a commonly-used set of terms - descriptive, mechanistic, and normative - as methodological designations that refer to the kind of problem a theory is intended to solve. Further, we find that models of each kind play distinct roles in defining and bridging the multiple levels of abstraction necessary to account for any neuroscientific phenomenon. We then discuss how models play an important role to connect theory and experiment, and note the importance of well-defined translation functions between them. Furthermore, we describe how models themselves can be used as a form of experiment to test and develop theories. This report is the summary of a discussion initiated at the conference Present and Future Theoretical Frameworks in Neuroscience, which we hope will contribute to a much-needed discussion in the neuroscientific community.
Entropy is a classical measure to quantify the amount of information or complexity of a system. Various entropy-based measures such as functional and spectral entropies have been proposed in brain network analysis. However, they are less widely used than traditional graph theoretic measures such as global and local efficiencies because either they are not well-defined on a graph or difficult to interpret its biological meaning. In this paper, we propose a new entropy-based graph invariant, called volume entropy. It measures the exponential growth rate of the number of paths in a graph, which is a relevant measure if information flows through the graph forever. We model the information propagation on a graph by the generalized Markov system associated to the weighted edge-transition matrix. We estimate the volume entropy using the stationary equation of the generalized Markov system. A prominent advantage of using the stationary equation is that it assigns certain distribution of weights on the edges of the brain graph, which we call the stationary distribution. The stationary distribution shows the information capacity of edges and the direction of information flow on a brain graph. The simulation results show that the volume entropy distinguishes the underlying graph topology and geometry better than the existing graph measures. In brain imaging data application, the volume entropy of brain graphs was significantly related to healthy normal aging from 20s to 60s. In addition, the stationary distribution of information propagation gives a new insight into the information flow of functional brain graph.
There are many mathematical models of biochemical cell signaling pathways that contain a large number of elements (species and reactions). This is sometimes a big issue for identifying critical model elements and describing the model dynamics. Thus, techniques of model reduction can be used as a mathematical tool in order to minimize the number of variables and parameters. In this thesis, we review some well-known methods of model reduction for cell signaling pathways. We have also developed some approaches that provide us a great step forward in model reduction. The techniques are quasi steady state approximation (QSSA), quasi equilibrium approximation (QEA), lumping of species and entropy production analysis. They are applied on protein translation pathways with microRNA mechanisms, chemical reaction networks, extracellular signal regulated kinase (ERK) pathways, NFkB signal transduction pathways, elongation factors EFTu and EFTs signaling pathways and Dihydrofolate reductase (DHFR) pathways. The main aim of this thesis is to reduce the complex cell signaling pathway models. This provides one a better understanding of the dynamics of such models and gives an accurate approximate solution. Results show that there is a good agreement between the original models and the simplified models.
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