Approximate Inference for Time-Varying Interactions and Macroscopic Dynamics of Neural Populations

The models in statistical physics such as an Ising model offer a convenient way to characterize stationary activity of neural populations. Such stationary activity of neurons may be expected for recordings from in vitro slices or anesthetized animals. However, modeling activity of cortical circuitries of awake animals has been more challenging because both spike-rates and interactions can change according to sensory stimulation, behavior, or an internal state of the brain. Previous approaches modeling the dynamics of neural interactions suffer from computational cost; therefore, its application was limited to only a dozen neurons. Here by introducing multiple analytic approximation methods to a state-space model of neural population activity, we make it possible to estimate dynamic pairwise interactions of up to 60 neurons. More specifically, we applied the pseudolikelihood approximation to the state-space model, and combined it with the Bethe or TAP mean-field approximation to make the sequential Bayesian estimation of the model parameters possible. The large-scale analysis allows us to investigate dynamics of macroscopic properties of neural circuitries underlying stimulus processing and behavior. We show that the model accurately estimates dynamics of network properties such as sparseness, entropy, and heat capacity by simulated data, and demonstrate utilities of these measures by analyzing activity of monkey V4 neurons as well as a simulated balanced network of spiking neurons.DFG, 103586207, GRK 1589: Verarbeitung sensorischer Informationen in neuronalen Systeme

Theoretical Interpretations and Applications of Radial Basis Function Networks

Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks. However, RBFNs are Knowledge-Based Networks that can be interpreted in several way: Artificial Neural Networks, Regularization Networks, Support Vector Machines, Wavelet Networks, Fuzzy Controllers, Kernel Estimators, Instanced-Based Learners. A survey of their interpretations and of their corresponding learning algorithms is provided as well as a brief survey on dynamic learning algorithms. RBFNs' interpretations can suggest applications that are particularly interesting in medical domains

Prediction of the functional properties of ceramic materials from composition using artificial neural networks

We describe the development of artificial neural networks (ANN) for the prediction of the properties of ceramic materials. The ceramics studied here include polycrystalline, inorganic, non-metallic materials and are investigated on the basis of their dielectric and ionic properties. Dielectric materials are of interest in telecommunication applications where they are used in tuning and filtering equipment. Ionic and mixed conductors are the subjects of a concerted effort in the search for new materials that can be incorporated into efficient, clean electrochemical devices of interest in energy production and greenhouse gas reduction applications. Multi-layer perceptron ANNs are trained using the back-propagation algorithm and utilise data obtained from the literature to learn composition-property relationships between the inputs and outputs of the system. The trained networks use compositional information to predict the relative permittivity and oxygen diffusion properties of ceramic materials. The results show that ANNs are able to produce accurate predictions of the properties of these ceramic materials which can be used to develop materials suitable for use in telecommunication and energy production applications

Adaptive nonlinear attitude control of spacecraft

Three adaptive nonlinear control approaches are proposed for attitude control of a Space Station. These control algorithms avoid the need for linearization, either by truncating a Taylor series or by a feedback linearization approach, which poses some difficulties in the presence of both uncertain mass properties and external disturbances. Global stability of the Space Station is guaranteed;A conventional linear-quadratic regulator synthesis technique is investigated. This linear control scheme provides a natural way to select control gains for the known spacecraft system parameters. Simulation results reveal that the uncertain mass property causes the nonlinear Space Station to be uncontrollable;On the basis of two nonlinear adaptive control approaches originally developed for robot manipulators, Two controllers are developed to overcome the problems introduced by uncertain inertias. The improvement on transient state responses is obtained by optimizing control parameters. A compensation term which adaptively adds periodic signals to the input is appended to both controllers to cancel the effect of the cyclic disturbances. The convergence properties of the adaptive algorithms are proved. The application to the Space Station attitude control of both nonlinear control schemes shows good performance in the presence of uncertain inertias and disturbances. Robustness with the unmodeled dynamics for both algorithms are also tested;Another new approach that utilizes the neural network with a radial basis function technique to compensate for the effect of the disturbances is also presented. The neural networks provide an estimate of the cyclic disturbances and act as an open-loop control so that the overall attitude oscillation caused by the disturbances is minimized. This neural network control law is applied to the Space Station in conjunction with the nonlinear controllers which are used to shape the system responses. The simulation results show that, compared to the case where only nonlinear controllers are used, the required control torques are significantly smaller. It has also been shown that the trained networks are capable of extrapolating;Finally, when the attitude control is achieved by control momentum gyros (CMGs), an approximate feedback linearization method is applied to simultaneous attitude control and momentum management with unknown constant disturbances. A direct adaptive controller is developed to stabilize the system with large disturbances. Simulations are carried out to show that the Space Station can be stabilized within a reasonable range, whereas ignoring the disturbances in the controller can lead to destruction of the Space Station and saturation of the CMGs

Geometric deep learning: going beyond Euclidean data

Many scientific fields study data with an underlying structure that is a non-Euclidean space. Some examples include social networks in computational social sciences, sensor networks in communications, functional networks in brain imaging, regulatory networks in genetics, and meshed surfaces in computer graphics. In many applications, such geometric data are large and complex (in the case of social networks, on the scale of billions), and are natural targets for machine learning techniques. In particular, we would like to use deep neural networks, which have recently proven to be powerful tools for a broad range of problems from computer vision, natural language processing, and audio analysis. However, these tools have been most successful on data with an underlying Euclidean or grid-like structure, and in cases where the invariances of these structures are built into networks used to model them. Geometric deep learning is an umbrella term for emerging techniques attempting to generalize (structured) deep neural models to non-Euclidean domains such as graphs and manifolds. The purpose of this paper is to overview different examples of geometric deep learning problems and present available solutions, key difficulties, applications, and future research directions in this nascent field

Oscillations, metastability and phase transitions in brain and models of cognition

Neuroscience is being practiced in many different forms and at many different organizational levels of the Nervous System. Which of these levels and associated conceptual frameworks is most informative for elucidating the association of neural processes with processes of Cognition is an empirical question and subject to pragmatic validation. In this essay, I select the framework of Dynamic System Theory. Several investigators have applied in recent years tools and concepts of this theory to interpretation of observational data, and for designing neuronal models of cognitive functions. I will first trace the essentials of conceptual development and hypotheses separately for discerning observational tests and criteria for functional realism and conceptual plausibility of the alternatives they offer. I will then show that the statistical mechanics of phase transitions in brain activity, and some of its models, provides a new and possibly revealing perspective on brain events in cognition