Random Neuronal Networks show homeostatic regulation of global activity while showing persistent changes in specific connectivity paths to theta burst stimuli.

Learning in neuronal networks based on Hebbian principle has been shown to lead to destabilizing effects. Mechanisms have been identified that maintain homeostasis in such networks. However, the way in which these two opposing forces operate to support learning while maintaining stability is an active area of research. In this study, using neuronal networks grown on multi electrode arrays, we show that theta burst stimuli lead to persistent changes in functional connectivity along specific paths while the network maintains a global homeostasis. Simultaneous observations of spontaneous activity and stimulus evoked responses over several hours with theta burst training stimuli shows that global activity of the network quantified from spontaneous activity, which is disturbed due to theta burst stimuli is restored by homeostatic mechanisms while stimulus evoked changes in specific connectivity paths retain a memory trace of the training.

Continuous, real-time monitoring of neonatal position and temperature during Kangaroo Mother Care using a wearable sensor: a techno-feasibility pilot study.

BACKGROUND: Remote biomonitoring of vital parameters in hospitals and homes has the potential to improve coverage and quality of maternal and neonatal health. Wearable sensors coupled with modern information and communication technology now offer an opportunity to monitor temperatures and kangaroo mother care (KMC) adherence in a continuous and real-time manner remotely for several days' duration in hospital and home settings. Using an innovative remote biomonitoring device to measure both temperature and baby position, we undertook a techno-feasibility study in preparation for a clinical trial. METHODS: We designed and developed a wearable sensor for tracking KMC adherence and neonatal temperature, using social innovation design principles. After screening mother-infant dyads using clinical and logistic eligibility criteria, we piloted this wearable sensor along with a gateway device and the commercial cellular network. The dyads were recruited during hospitalization and followed up in the hospital and home phases for several days. Simple descriptive statistical analysis was undertaken. RESULTS: Recruitment rate was 50% (6/12), and consenting rate was 83% (5/6) during a 2-month period. These five neonates contributed a total of 39 study days (15 hospital days and 24 home days). Their mean [± standard deviation (S.D.)] birth weight was 1490 (± 244) g.The mean (± S.D.) of the vital signs for the five babies was temperature [36.5 °C (± 0.3)], heart rate [146.5/min (± 14)], and oxygen saturation [94% (± 4)]. No severe or moderate side-effects were noted; one baby developed mild dermatitis under the device that was transient and self-limiting, yielding an incidence proportion of 20% and incidence rate of 2.6/100 person-days.None of the mothers reported any discomfort with the use of the device. Temperatures detected from 81 paired readings revealed that those from the wearable sensor were 0.2 °C lower than those detected by clinical thermometers [36.4 (± 0.7) vs 36.6 (± 0.3); < 0.001].There was also iterative feedback that was useful for hardware and software design specifications of the wearable sensor, the gateway device, and the analytics platform. Lastly, lessons were learnt with regard to the logistics of research team interactions with healthcare professionals and study participants during the hospitalization and post-discharge home phases of the study. CONCLUSIONS: The pilot study has shown that it is feasible and acceptable to track KMC adherence as well as maternal and newborn temperatures in a potentially safe manner on a real-time mode for several days' duration during hospitalization and home phases. The pilot has also helped inform modifications in clinical monitoring, technological modifications, and logistics planning in preparation for the definitive clinical trial. TRIAL REGISTRATION: Clinical Trials Registry of India, CTRI/2017/09/009789.

Remote biomonitoring of temperatures in mothers and newborns: design, development and testing of a wearable sensor device in a tertiary-care hospital in southern India.

OBJECTIVE: Newer technologies such as wearables, sensors, mobile telephony and computing offer opportunities to monitor vital physiological parameters and tackle healthcare problems, thereby improving access and quality of care. We describe the design, development and testing of a wearable sensor device for remote biomonitoring of body temperatures in mothers and newborns in southern India. METHODS: Based on client needs and technological requirements, a wearable sensor device was designed and developed using principles of 'social innovation' design. The device underwent multiple iterations in product design and engineering based on user feedback, and then following preclinical testing, a techno-feasibility study and clinical trial were undertaken in a tertiary-care teaching hospital in Bangalore, India. Clinical trial phases I and IIa for evaluation of safety and efficacy were undertaken in the following sequence: 7 healthy adult volunteers; 18 healthy mothers; 3 healthy babies; 10 stable babies in the neonatal care intensive unit and 1 baby with morbidities. Time-stamped skin temperature readings obtained at 5 min intervals over a 1-hour period from the device secured on upper arms of mothers and abdomen of neonates were compared against readings from thermometers used routinely in clinical practice. RESULTS: Devices were comfortably secured on to adults and neonates, and data were efficiently transmitted via the gateway device for secure storage and retrieval for analysis. The mean skin temperatures in mothers were lower than the axillary temperatures by 2°C; and in newborns, there was a precision of -0.5°C relative to axillary measurements. While occasional minimal adverse events were noted in healthy volunteers, no adverse events were noted in mothers or neonates. CONCLUSIONS: This proof-of-concept study shows that this device is promising in terms of feasibility, safety and accuracy (with appropriate calibration) with potential for further refinements in device accuracy and pursuit of further phases of clinical research for improved maternal and neonatal health.

Evaluation of fiber Bragg grating sensor interrogation using InGaAs linear detector arrays and Gaussian approximation on embedded hardware.

Fiber Bragg Grating (FBG) sensors have become popular for applications related to structural health monitoring, biomedical engineering, and robotics. However, for successful large scale adoption, FBG interrogation systems are as important as sensor characteristics. Apart from accuracy, the required number of FBG sensors per fiber and the distance between the device in which the sensors are used and the interrogation system also influence the selection of the interrogation technique. For several measurement devices developed for applications in biomedical engineering and robotics, only a few sensors per fiber are required and the device is close to the interrogation system. For these applications, interrogation systems based on InGaAs linear detector arrays provide a good choice. However, their resolution is dependent on the algorithms used for curve fitting. In this work, a detailed analysis of the choice of algorithm using the Gaussian approximation for the FBG spectrum and the number of pixels used for curve fitting on the errors is provided. The points where the maximum errors occur have been identified. All comparisons for wavelength shift detection have been made against another interrogation system based on the tunable swept laser. It has been shown that maximum errors occur when the wavelength shift is such that one new pixel is included for curve fitting. It has also been shown that an algorithm with lower computation cost compared to the more popular methods using iterative non-linear least squares estimation can be used without leading to the loss of accuracy. The algorithm has been implemented on embedded hardware, and a speed-up of approximately six times has been observed.

Random neuronal ensembles can inherently do context dependent coarse conjunctive encoding of input stimulus without any specific training.

Conjunctive encoding of inputs has been hypothesized to be a key feature in the computational capabilities of the brain. This has been inferred based on behavioral studies and electrophysiological recording from animals. In this report, we show that random neuronal ensembles grown on multi-electrode array perform a coarse-conjunctive encoding for a sequence of inputs with the first input setting the context. Such an encoding scheme creates similar yet unique population codes at the output of the ensemble, for related input sequences, which can then be decoded via a simple perceptron and hence a single STDP neuron layer. The random neuronal ensembles allow for pattern generalization and novel sequence classification without needing any specific learning or training of the ensemble. Such a representation of the inputs as population codes of neuronal ensemble outputs, has inherent redundancy and is suitable for further decoding via even probabilistic/random connections to subsequent neuronal layers. We reproduce this behavior in a mathematical model to show that a random neuronal network with a mix of excitatory and inhibitory neurons and sufficient connectivity creates similar coarse-conjunctive encoding of input sequences.

Detecting stages of needle penetration into tissues through force estimation at needle tip using fiber Bragg grating sensors.

Several medical procedures involve the use of needles. The advent of robotic and robot assisted procedures requires dynamic estimation of the needle tip location during insertion for use in both assistive systems as well as for automatic control. Most prior studies have focused on the maneuvering of solid flexible needles using external force measurements at the base of the needle holder. However, hollow needles are used in several procedures and measurements of forces in proximity of such needles can eliminate the need for estimating frictional forces that have high variations. These measurements are also significant for endoscopic procedures in which measurement of forces at the needle holder base is difficult. Fiber Bragg grating sensors, due to their small size, inert nature, and multiplexing capability, provide a good option for this purpose. Force measurements have been undertaken during needle insertion into tissue mimicking phantoms made of polydimethylsiloxane as well as chicken tissue using an 18-G needle instrumented with FBG sensors. The results obtained show that it is possible to estimate the different stages of needle penetration including partial rupture, which is significant for procedures in which precise estimation of needle tip position inside the organ or tissue is required.

Adaptive pulse width control and sampling for low power pulse oximetry.

Remote sensing of physiological parameters could be a cost effective approach to improving health care, and low-power sensors are essential for remote sensing because these sensors are often energy constrained. This paper presents a power optimized photoplethysmographic sensor interface to sense arterial oxygen saturation, a technique to dynamically trade off SNR for power during sensor operation, and a simple algorithm to choose when to acquire samples in photoplethysmography. A prototype of the proposed pulse oximeter built using commercial-off-the-shelf (COTS) components is tested on 10 adults. The dynamic adaptation techniques described reduce power consumption considerably compared to our reference implementation, and our approach is competitive to state-of-the-art implementations. The techniques presented in this paper may be applied to low-power sensor interface designs where acquiring samples is expensive in terms of power as epitomized by pulse oximetry.

Input coding for neuro-electronic hybrid systems.

Liquid State Machines have been proposed as a framework to explore the computational properties of neuro-electronic hybrid systems (Maass et al., 2002). Here the neuronal culture implements a recurrent network and is followed by an array of linear discriminants implemented using perceptrons in electronics/software. Thus in this framework, it is desired that the outputs of the neuronal network, corresponding to different inputs, be linearly separable. Previous studies have demonstrated this by either using only a small set of input stimulus patterns to the culture (Hafizovic et al., 2007), large number of input electrodes (Dockendorf et al., 2009) or by using complex schemes to post-process the outputs of the neuronal culture prior to linear discriminance (Ortman et al., 2011). In this study we explore ways to temporally encode inputs into stimulus patterns using a small set of electrodes such that the neuronal culture's output can be directly decoded by simple linear discriminants based on perceptrons. We demonstrate that network can detect the timing and order of firing of inputs on multiple electrodes. Based on this, we demonstrate that the neuronal culture can be used as a kernel to transform inputs which are not linearly separable in a low dimensional space, into outputs in a high dimension where they are linearly separable. Thus simple linear discriminants can now be directly connected to outputs of the neuronal culture and allow for implementation of any function for such a hybrid system.

Synconset waves and chains: spiking onsets in synchronous populations predict and are predicted by network structure.

Synfire waves are propagating spike packets in synfire chains, which are feedforward chains embedded in random networks. Although synfire waves have proved to be effective quantification for network activity with clear relations to network structure, their utilities are largely limited to feedforward networks with low background activity. To overcome these shortcomings, we describe a novel generalisation of synfire waves, and define 'synconset wave' as a cascade of first spikes within a synchronisation event. Synconset waves would occur in 'synconset chains', which are feedforward chains embedded in possibly heavily recurrent networks with heavy background activity. We probed the utility of synconset waves using simulation of single compartment neuron network models with biophysically realistic conductances, and demonstrated that the spread of synconset waves directly follows from the network connectivity matrix and is modulated by top-down inputs and the resultant oscillations. Such synconset profiles lend intuitive insights into network organisation in terms of connection probabilities between various network regions rather than an adjacency matrix. To test this intuition, we develop a Bayesian likelihood function that quantifies the probability that an observed synfire wave was caused by a given network. Further, we demonstrate it's utility in the inverse problem of identifying the network that caused a given synfire wave. This method was effective even in highly subsampled networks where only a small subset of neurons were accessible, thus showing it's utility in experimental estimation of connectomes in real neuronal-networks. Together, we propose synconset chains/waves as an effective framework for understanding the impact of network structure on function, and as a step towards developing physiology-driven network identification methods. Finally, as synconset chains extend the utilities of synfire chains to arbitrary networks, we suggest utilities of our framework to several aspects of network physiology including cell assemblies, population codes, and oscillatory synchrony.

A study of epileptogenic network structures in rat hippocampal cultures using first spike latencies during synchronization events.

Study of hypersynchronous activity is of prime importance for combating epilepsy. Studies on network structure typically reconstruct the network by measuring various aspects of the interaction between neurons and subsequently measure the properties of the reconstructed network. In sub-sampled networks such methods lead to significant errors in reconstruction. Using rat hippocampal neurons cultured on a multi-electrode array dish and a glutamate injury model of epilepsy in vitro, we studied synchronous activity in neuronal networks. Using the first spike latencies in various neurons during a network burst, we extract various recurring spatio-temporal onset patterns in the networks. Comparing the patterns seen in control and injured networks, we observe that injured networks express a wide diversity in their foci (origin) and activation pattern, while control networks show limited diversity. Furthermore, we note that onset patterns in glutamate injured networks show a positive correlation between synchronization delay and physical distance between neurons, while control networks do not.