Burst analysis tool for developing neuronal networks exhibiting highly varying action potential dynamics

In this paper we propose a firing statistics based neuronal network burst detection algorithm for neuronal networks exhibiting highly variable action potential dynamics. Electrical activity of neuronal networks is generally analyzed by the occurrences of spikes and bursts both in time and space. Commonly accepted analysis tools employ burst detection algorithms based on predefined criteria. However, maturing neuronal networks, such as those originating from human embryonic stem cells (hESCs), exhibit highly variable network structure and time-varying dynamics. To explore the developing burst/spike activities of such networks, we propose a burst detection algorithm which utilizes the firing statistics based on interspike interval (ISI) histograms. Moreover, the algorithm calculates ISI thresholds for burst spikes as well as for pre-burst spikes and burst tails by evaluating the cumulative moving average (CMA) and skewness of the ISI histogram. Because of the adaptive nature of the proposed algorithm, its analysis power is not limited by the type of neuronal cell network at hand. We demonstrate the functionality of our algorithm with two different types of microelectrode array (MEA) data recorded from spontaneously active hESC-derived neuronal cell networks. The same data was also analyzed by two commonly employed burst detection algorithms and the differences in burst detection results are illustrated. The results demonstrate that our method is both adaptive to the firing statistics of the network and yields successful burst detection from the data. In conclusion, the proposed method is a potential tool for analyzing of hESC-derived neuronal cell networks and thus can be utilized in studies aiming to understand the development and functioning of human neuronal networks and as an analysis tool for in vitro drug screening and neurotoxicity assays

Polynomial predictive filters : implementation and applications

In this thesis, smoothness of sampled real-world signals is exploited through the application of polynomial predictive filters. The principal reason for employing the polynomial signal model is principally twofold: firstly, assuming that the sampling rate is adequate, all real-world signals exhibit piecewise polynomial-like behavior, and secondly, polynomial-based signal processing is computationally efficient. By definition, polynomial predictive filters provide estimates of future values of polynomial-like signals. Thus, the potential applications of this research include a vast number of different delay sensitive operations on measurements like temperature, position, velocity, or power, especially in control engineering field. The polynomial-based predictive signal processing is a well-known technique, but polynomial-predictive filters have had severe drawbacks, which have hindered their application; their white noise attenuation is generally low, or they exhibit considerable passband gain peaks, rendering them unattractive for most applications. It has been possible to design IIR polynomial predictors, which exhibit applicable magnitude response properties, but the severe problem with them, as well as with the FIR polynomial predictors, has been that they have generally not been implementable in low-precision fixed-point environments because of their coefficient quantization sensitivity. In this thesis, coefficient quantization error-free designs of both FIR and IIR polynomial predictors are presented, thus providing methods for overcoming the above drawbacks and design problems. Polynomial differentiators are closely related to polynomial predictors; they are derived in a similar fashion, have design problems of a similar nature, and have applications in the control field. Both of these two filter types are discussed in this thesis; the proposed design methods are applicable to both of them. The implementation aspects of polynomial predictors and differentiators investigated here are also connected to the practical requirements of the application, namely delay alleviation in closed loop transmitter power control of multiuser mobile communications systems. Particularly, if predictive received power level estimation is implemented in handheld mobile terminals, this application specifies the implementation criteria as requirements on low imposed computational burden, low power consumption, and compact hardware size. All these criteria are met by providing the desired functionality using a small number of fixed-point arithmetic operations. Taking into account the results presented in this thesis, polynomial prediction fulfills these criteria. In this thesis, digital filter design methodologies are advanced by first-time introduction of exact low-degree polynomial prediction and discrete time differentiation in low-precision fixed-point computing environments, with, for example, 8 or 16 bits. Polynomial prediction is shown advantageous in the closed loop transmitter power control system application, and in comparisons with more complex and flexible predictors, it is shown to be a highly efficient method for this particular application. This thesis is seen as contributing to advances in practical polynomial predictor and differentiator design methods, and thereafter studies the application of polynomial predictors in mobile communications system transmitter power control. This research will be of interest to signal processing, control, and communications engineers and researchers alike.reviewe

Espoo, Finland Independent Component Analysis Applied to Multielectrode Field Potential Measurements

Independent component analysis (ICA) is proposed for analysis of neural population (NP) activity from electrophysiological multielectrode field potential measurements (MFPMs). The proposed analysis method provides information on spatial locations of NPs, and time lags of NP activities. In some cases, analysis results may also be interpreted as independent operational modes of NPs. In this paper, the proposed analysis method is described. The proposed analysis is demonstrated with an exemplary analysis of an in vivo MFPM from the rat hippocampus. The proposed method can be applied in analysis of any recordings of neural networks in which contributions from a number of NPs are simultaneously recorded via a number of measurement points (MPs), as well in vivo as in vitro. 1

Lead field theory provides a powerful tool for designing microelectrode array impedance measurements for biological cell detection and observation

Abstract Background Our aim is to introduce a method to enhance the design process of microelectrode array (MEA) based electric bioimpedance measurement systems for improved detection and viability assessment of living cells and tissues. We propose the application of electromagnetic lead field theory and reciprocity for MEA design and measurement result interpretation. Further, we simulated impedance spectroscopy (IS) with two- and four-electrode setups and a biological cell to illustrate the tool in the assessment of the capabilities of given MEA electrode constellations for detecting cells on or in the vicinity of the microelectrodes. Results The results show the power of the lead field theory in electromagnetic simulations of cell–microelectrode systems depicting the fundamental differences of two- and four-electrode IS measurement configurations to detect cells. Accordingly, the use in MEA system design is demonstrated by assessing the differences between the two- and four-electrode IS configurations. Further, our results show how cells affect the lead fields in these MEA system, and how we can utilize the differences of the two- and four-electrode setups in cell detection. The COMSOL simulator model is provided freely in public domain as open source. Conclusions Lead field theory can be successfully applied in MEA design for the IS based assessment of biological cells providing the necessary visualization and insight for MEA design. The proposed method is expected to enhance the design and usability of automated cell and tissue manipulation systems required for bioreactors, which are intended for the automated production of cell and tissue grafts for medical purposes. MEA systems are also intended for toxicology to assess the effects of chemicals on living cells. Our results demonstrate that lead field concept is expected to enhance also the development of such methods and devices

Extracellular Electrical Stimulation-based in Vitro Neuroscience: A Minireview of Methods and a Paradigm Shift Proposal

Biological neuronal cells communicate using neurochemistry and electrical signals. Electrical stimulation (ES) is utilized to study neuronal cells and networks. Currently, ES is applied and responses observed in an open-loop fashion, which does not resemble natural network I/O. We hypothesize that real-time closed-loop full-duplex (simultaneous two-way) paradigms could provide deeper insight in natural neuronal networks, helping to understand our brains and to control neuronal network states to cure diseases. We present a minireview of ES-based extracellular in vitro neuroscience, our first long-term closed-loop ES experiment results as the proof-of-feasibility of the method, and our paradigm-shifting proposal of dialogical bio-ICT paradigms.acceptedVersionPeer reviewe