117 results found
Cheng R, Mirza KB, Nikolic K, 2020, Neuromorphic robotic platform with visual input, processor and actuator, based on spiking neural networks, Applied System Innovation, Vol: 3, Pages: 1-16, ISSN: 2571-5577
This paper describes the design and modus of operation of a neuromorphic robotic platform based on SpiNNaker, and its implementation on the goalkeeper task. The robotic system utilises an address event representation (AER) type of camera (dynamic vision sensor (DVS)) to capture features of a moving ball, and a servo motor to position the goalkeeper to intercept the incoming ball. At the backbone of the system is a microcontroller (Arduino Due) which facilitates communication and control between different robot parts. A spiking neuronal network (SNN), which is running on SpiNNaker, predicts the location of arrival of the moving ball and decides where to place the goalkeeper. In our setup, the maximum data transmission speed of the closed-loop system is approximately 3000 packets per second for both uplink and downlink, and the robot can intercept balls whose speed is up to 1 m/s starting from the distance of about 0.8 m. The interception accuracy is up to 85%, the response latency is 6.5 ms and the maximum power consumption is 7.15 W. This is better than previous implementations based on PC. Here, a simplified version of an SNN has been developed for the ‘interception of a moving object’ task, for the purpose of demonstrating the platform, however a generalised SNN for this problem is a nontrivial problem. A demo video of the robot goalie is available on YouTube.
Wildner K, Mirza KB, De La Franier B, et al., 2020, Iridium oxide based potassium sensitive microprobe with anti-fouling properties, IEEE Sensors Journal, ISSN: 1530-437X
Here, we present a new type of potassium sensor which possesses a combination of potassium sensing and anti-biofouling properties. Two major advancements were required to be developed with respect to the current technology; Firstly, design of surface linkers for this type of coating that would allow deposition of the potassiumselective coating on Iridium (Ir) wire or micro-spike surface for chronic monitoring for the first time. As this has never been done before, even for flat Ir surfaces, the material’s small dimensions and surface area render this challenging. Secondly, the task of transformation of the coated wire into a sensor. Here we develop and bench-test the electrode sensitivity to potassium and determine its specificity to potassium versus sodium interference. For this purpose we also present a novel characterisation platform which enables dynamic characterization of the sensor including step and sinusoidal response to analyte changes. The developed sensor shows good sensitivity (<1 mM concentrations of K+ ions) and selectivity (up to approximately 10 times more sensitive to K+ than Na+ concentration changes, depending on concentrations and ionic environment). In addition, the sensor displays very good mechanical properties for the small diameter involved (sub 150 μm), which in combination with anti-biofouling properties, renders it an excellent potential tool for the chemical monitoring of neural and other physiological activities using implantable devices.
Roever P, Mirza KB, Nikolic K, et al., 2020, Convolutional neural network for classification of nerve activity based on action potential induced neurochemical Signatures, IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, ISSN: 0271-4302
Neural activity results in chemical changes in theextracellular environment such as variation in pH or potassium/sodium ion concentration. Higher signal to noise ratio makeneurochemical signals an interesting biomarker for closed-loopneuromodulation systems. For such applications, it is importantto reliably classify pH signatures to control stimulationtiming and possibly dosage. For example, the activity of thesubdiaphragmatic vagus nerve (sVN) branch can be monitoredby measuring extracellular neural pH. More importantly, guthormone cholecystokinin (CCK)-specific activity on the sVN canbe used for controllably activating sVN, in order to mimic thegut-brain neural response to food intake. In this paper, we presenta convolutional neural network (CNN) based classification systemto identify CCK-specific neurochemical changes on the sVN,from non-linear background activity. Here we present a novelfeature engineering approach which enables, after training, ahigh accuracy classification of neurochemical signals using CNN.
Najafizadeh L, Zhang M, Nikolic K, 2019, Guest Editorial: Special Issue on Selected Papers From IEEE BioCAS 2018, IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, Vol: 13, Pages: 793-794, ISSN: 1932-4545
Mirza KB, Golden C, Nikolic K, et al., 2019, Closed-loop implantable therapeutic neuromodulation systems based on neurochemical monitoring, Frontiers in Neuroscience, Vol: 13, ISSN: 1662-4548
Closed-loop or intelligent neuromodulation allows adjustable, personalised neuromodulation which usually incorporates the recording of a biomarker, followed by implementation of an algori5 thm which decides the timing (when ?) and strength (how much ?) of stimulation. Closed-loop neuromodulation has been shown to have greater benefits compared to open-loop neuromodu lation, particularly for therapeutic applications such as pharmacoresistant epilepsy, movement disorders and potentially for psychological disorders such as depression or drug addiction. How ever, an important aspect of the technique is selection of an appropriate, preferably neural biomarker. Neurochemical sensing can provide high resolution biomarker monitoring for various neurological disorders as well as offer deeper insight into neurological mechanisms. The chemicals of interest being measured, could be ions such as potassium (K+), sodium (Na+ 12 ), calcium(Ca2+), chloride (Cl−), hydrogen (H+ 13 ) or neurotransmitters such as dopamine, serotonin and glutamate. This review focusses on the different building blocks necessary for a neurochemi cal, closed-loop neuromodulation system including biomarkers, sensors and data processing algorithms. Furthermore, it also highlights the merits and drawbacks of using this biomarker modality.
Mirza KB, Kulasekeram N, Liu Y, et al., 2019, System on chip for closed loop neuromodulation based on dual mode biosignals, 2019 IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: Institute of Electrical and Electronics Engineers (IEEE), ISSN: 2158-1525
Closed loop neuromodulation, where the stimulation is controlled autonomously based on physiological events, has been more effective than open loop techniques. In the few existing closed loop implementations which have a feedback, indirect non-neurophysiological biomarkers have been typically used (e.g. heart rate, stomach distension). Although these biomarkers enable automatic initiation of neural stimulation, they do not enable intelligent control of stimulation dosage. In this paper, we present a novel closed loop neuromodulation System-on-Chip (SoC) based on a dual signal mode that is detecting both electrical and chemical signatures of neural activity. We use vagus nerve stimulation (VNS) as a design case here. Vagal chemical (pH) signal is detected and used for initiating VNS and vagal compound nerve action potential (CNAP) signals are used to determine the stimulation dosage and pattern. Although we used the paradigm of appetite control and neurometabolic therapies for developing the algorithms for neurostimulation control, the SoC described here can be utilised for other types of closed loop neuromodulation implants.
Troiani F, Nikolic K, Constandinou TG, 2019, Correction: Simulating optical coherence tomography for observing nerve activity: a finite difference time domain bi-dimensional model, PLoS ONE, Vol: 14, ISSN: 1932-6203
[This corrects the article DOI: 10.1371/journal.pone.0200392.].
Luo JW, Nikolic K, Degenaar P, 2019, Modelling Optogenetic Subthreshold Effects, 2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), Pages: 6136-6140, ISSN: 1557-170X
Haci D, Liu Y, Nikolic K, et al., 2018, Thermally controlled lab-on-PCB for biomedical applications, IEEE Biomedical Circuits and Systems (BioCAS) Conference, Publisher: IEEE, Pages: 655-658
This paper reports on the implementation andcharacterisation of a thermally controlled device forin vitrobiomedical applications, based on standard Printed Circuit Board(PCB) technology. This is proposed as a low cost alternativeto state-of-the-art microfluidic devices and Lab-on-Chip (LoC)platforms, which we refer to as the thermal Lab-on-PCB concept.In total, six different prototype boards have been manufacturedto implement as many mini-hotplate arrays. 3D multiphysicssoftware simulations show the thermal response of the modelledmini-hotplate boards to electrical current stimulation, highlight-ing their versatile heating capability. A comparison with theresults obtained by the characterisation of the fabricated PCBsdemonstrates the dual temperature sensing/heating property ofthe mini-hotplate, exploitable in a larger range of temperaturewith respect to the typical operating range of LoC devices. Thethermal system is controllable by means of external off-the-shelfcircuitry designed and implemented on a single-channel controlboard prototype.
Perra E, Rapeaux A, Nikolic K, 2018, The Crucial Role of Nerve Depolarisation in High Frequency Conduction Block in Mammalian Nerves: Simulation Study., Conf Proc IEEE Eng Med Biol Soc, Vol: 2018, Pages: 2214-2217, ISSN: 1557-170X
Neurostimulations which use High Frequency Alternating Current (HFAC) block show great promise for neuromodulatory therapies. Treatments have been developed for various health conditions including obesity and obesity related health risks, and now even stomach cancer treatments are being considered. However the mechanism of the block is still not completely clear, as well as how various neural and electrode parameters affect it. In order to study conduction block during HF stimulation in mammalian axons, we describe a detailed computational model and perform comprehensive simulations. We establish relationships between the blocking frequency and amplitude versus fibre diameter and the distance between the electrode and fibre. We found that only a certain level of depolarisation will universally create a block irrespective of the fibre size, and it is in the range 24-30mV depending on the stimulus frequency. Our study crucially improves our knowledge about this important technique which is rapidly emerging as a commercially available therapy.
Troiani F, Nikolic K, Constandinou TG, 2018, Simulating optical coherence tomography for observing nerve activity: a finite difference time domain bi-dimensional model
We present a finite difference time domain (FDTD) model for computation of Aline scans in time domain optical coherence tomography (OCT). By simulatingonly the end of the two arms of the interferometer and computing theinterference signal in post processing, it is possible to reduce thecomputational time required by the simulations and, thus, to simulate muchbigger environments. Moreover, it is possible to simulate successive A linesand thus obtaining a cross section of the sample considered. In this paper wepresent the model applied to two different samples: a glass rod filled withwater-sucrose solution at different concentrations and a peripheral nerve. Thiswork demonstrates the feasibility of using OCT for non-invasive, direct opticalmonitoring of peripheral nerve activity, which is a long-sought goal ofneuroscience.
Troiani F, Nikolic K, Constandinou TG, 2018, Simulating optical coherence tomography for observing nerve activity: a finite difference time domain bi-dimensional model, PLoS ONE, Vol: 13, Pages: 1-14, ISSN: 1932-6203
We present a finite difference time domain (FDTD) model for computation of A line scans in time domain optical coherence tomography (OCT). The OCT output signal is created using two different simulations for the reference and sample arms, with a successive computation of the interference signal with external software. In this paper we present the model applied to two different samples: a glass rod filled with water-sucrose solution at different concentrations and a peripheral nerve. This work aims to understand to what extent time domain OCT can be used for non-invasive, direct optical monitoring of peripheral nerve activity.
Markar S, Wiggins T, Antonowicz S, et al., 2018, Assessment of a noninvasive exhaled breath test for the diagnosis of oesophagogastric cancer, JAMA Oncology, Vol: 4, Pages: 970-976, ISSN: 2374-2445
Importance Early esophagogastric cancer (OGC) stage presents with nonspecific symptoms.Objective The aim of this study was to determine the accuracy of a breath test for the diagnosis of OGC in a multicenter validation study.Design, Setting, and Participants Patient recruitment for this diagnostic validation study was conducted at 3 London hospital sites, with breath samples returned to a central laboratory for selected ion flow tube mass spectrometry (SIFT-MS) analysis. Based on a 1:1 cancer:control ratio, and maintaining a sensitivity and specificity of 80%, the sample size required was 325 patients. All patients with cancer were on a curative treatment pathway, and patients were recruited consecutively. Among the 335 patients included; 172 were in the control group and 163 had OGC.Interventions Breath samples were collected using secure 500-mL steel breath bags and analyzed by SIFT-MS. Quality assurance measures included sampling room air, training all researchers in breath sampling, regular instrument calibration, and unambiguous volatile organic compounds (VOCs) identification by gas chromatography mass spectrometry.Main Outcomes and Measures The risk of cancer was identified based on a previously generated 5-VOCs model and compared with histopathology-proven diagnosis.Results Patients in the OGC group were older (median [IQR] age 68 [60-75] vs 55 [41-69] years) and had a greater proportion of men (134 [82.2%]) vs women (81 [47.4%]) compared with the control group. Of the 163 patients with OGC, 123 (69%) had tumor stage T3/4, and 106 (65%) had nodal metastasis on clinical staging. The predictive probabilities generated by this 5-VOCs diagnostic model were used to generate a receiver operator characteristic curve, with good diagnostic accuracy, area under the curve of 0.85. This translated to a sensitivity of 80% and specificity of 81% for the diagnosis of OGC.Conclusions and Relevance This study shows the potential of breath analysis in noninvasive diagn
Kulasekeram N, Wildner K, Mirza KB, et al., 2018, Reconfigurable Low-noise Multichannel Amplifier for Neurochemical Recording, IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, ISSN: 0271-4302
Wildner K, Kulasekeram N, Mirza KB, et al., 2018, Live Demo: Reconfigurable Low-noise Multichannel Amplifier for Neurochemical Recording, IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, ISSN: 0271-4302
Ahmed T, Mirza KB, Nikolic K, 2018, Resource efficient pre-processor for drift removal in neurochemical signals, IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, ISSN: 0271-4302
A necessary requirement for chemometric platforms is pre-processing of the acquired chemical signals to remove baseline drift in the signal. The drift could originate from sensor characteristics or from background chemical activity in the surrounding environment. A recent emerging field is neurochemical monitoring to detect and quantify neural activity. In this paper, a resource efficient pre-processing system is presented to remove drift from the acquired neurochemical signal. The drift removal technique is based on baseline manipulation without requiring window based processing. The target application, for demonstration purposes, is the recording of vagal pH signals to enable closed-loop Vagus Nerve Stimulation (VNS). The final design is multiplier-free and results in an Application Specific Integrated Circuit (ASIC) that is 640 μm by 625 μm in area.
Mirza K, Alenda A, Eftekhar A, et al., 2018, Influence of cholecystokinin-8 on compound nerve action potentials from ventral gastric vagus in rats, International Journal of Neural Systems, Vol: 28, ISSN: 0129-0657
Objective:Vagus Nerve Stimulation (VNS) has shown great promise as a potential therapy for anumber of conditions, such as epilepsy, depression and forNeurometabolic Therapies, especially fortreating obesity. The objective of this study was to characterise the left ventral subdiaphragmaticgastric trunk of vagus nerve (SubDiaGVN) and to analyse the influence of intravenous injection of guthormone cholecystokinin octapeptide (CCK-8) on compound nerve action potential (CNAP) observedon the same branch, with the aim of understanding the impact of hormones on VNS and incorporatingthe methods and results into closed loop implant design.Methods:The cervical region of the left vagus nerve (CerVN) of male Wistar rats was stimulatedwith electric current and the elicited CNAPs were recorded on the SubDiaGVN under four differentconditions:Control(no injection),Saline,CCK1(100 pmol/kg) andCCK2(1000 pmol/kg) injections.Results:We identified the presence of Aδ, B, C1, C2, C3 and C4 fibres with their respective velocityranges. Intravenous administration of CCKin vivoresults in selective, statistically significant reductionof CNAP components originating from A and B fibres, but with no discernible effect on the C fibresinn=7animals. The affected CNAP components exhibit statistically significant (pSaline−CCK1= 0.02andpSaline−CCK2= 0.007) higher normalized stimulation thresholds.Conclusion:This approach of characterising the vagus nerve can be used in closed loop systems todeterminewhento initiate VNS and also to tune the stimulation dose, which is patient specific andchanges over time.
Mirza KB, Wildner K, Kulasekeram N, et al., 2018, Live demo: Platform for closed loop neuromodulation based on dual mode biosignals
© 2017 IEEE. We demonstrate a novel feedback mechanism to implement closed loop neuromodulation based on a combination of electrical and chemical neural signal recording. The platform consists of our custom designed multi-channel amplifier for recording electrical and chemical signals (such as pH), data processing unit and the stimulator IC unit. A novelty of our approach is that the neural chemical signals are used to initiate stimulation and electrical compound action potentials (CAPs) to determine stimulation dosage. The proposed adaptive stimulation platform has been implemented in the context of Vagus Nerve Stimulation (VNS) for obesity therapy. The demonstration is based on externally generated signals derived from our in vivo experiments on physiological stimulation of the gastric branch of vagus nerve by injection of gut hormone cholecystokinin (CCK).
Jarvis S, Nikolic K, Schultz SR, 2018, Neuronal gain modulability is determined by dendritic morphology: a computational optogenetic study, PLoS Computational Biology, Vol: 14, ISSN: 1553-734X
The mechanisms by which the gain of the neuronal input-output function may be modulated have been the subject of much investigation. However, little is known of the role of dendrites in neuronal gain control. New optogenetic experimental paradigms based on spatial profiles or patterns of light stimulation offer the prospect of elucidating many aspects of single cell function, including the role of dendrites in gain control. We thus developed a model to investigate how competing excitatory and inhibitory input within the dendritic arbor alters neuronal gain, incorporating kinetic models of opsins into our modeling to ensure it is experimentally testable. To investigate how different topologies of the neuronal dendritic tree affect the neuron’s input-output characteristics we generate branching geometries which replicate morphological features of most common neurons, but keep the number of branches and overall area of dendrites approximately constant. We found a relationship between a neuron’s gain modulability and its dendritic morphology, with neurons with bipolar dendrites with a moderate degree of branching being most receptive to control of the gain of their input-output relationship. The theory was then tested and confirmed on two examples of realistic neurons: 1) layer V pyramidal cells—confirming their role in neural circuits as a regulator of the gain in the circuit in addition to acting as the primary excitatory neurons, and 2) stellate cells. In addition to providing testable predictions and a novel application of dual-opsins, our model suggests that innervation of all dendritic subdomains is required for full gain modulation, revealing the importance of dendritic targeting in the generation of neuronal gain control and the functions that it subserves. Finally, our study also demonstrates that neurophysiological investigations which use direct current injection into the soma and bypass the dendrites may miss some important neuronal functio
Troiani F, Nikolic K, Constandinou TG, 2017, Optical coherence tomography for compound action potential detection: a computational study, SPIE/OSA European Conferences on Biomedical Optics (ECBO), Publisher: Optical Society of America / SPIE, Pages: 1-3
The feasibility of using time domain optical coherence tomography (TD-OCT) to detect compound action potential in a peripheral nerve and the setup characteristics, are studied through the use of finite-difference time-domain (FDTD) technique.
Cork SC, Eftekhar A, Mirza KB, et al., 2017, Extracellular pH monitoring for use in closed-loop vagus nerve stimulation., Journal of Neural Engineering, Vol: 15, ISSN: 1741-2552
OBJECTIVE: Vagal nerve stimulation (VNS) has shown potential benefits for obesity treatment; however current devices lack physiological feedback, which limit their efficacy. Changes in extracellular pH (pH<sub>e</sub>) have shown to be correlated with neural activity, but have traditionally been measured with glass microelectrodes, which limit their in vivo applicability. APPROACH: Iridium oxide has previously been shown to be sensitive to fluctuations in pH and is biocompatible. Iridium oxide microelectrodes were inserted into the subdiaphragmatic vagus nerve of anaesthetised rats. Introduction of the gut hormone cholecystokinin (CCK) or distension of the stomach was used to elicit vagal nerve activity. MAIN RESULTS: iridium oxide microelectrodes have sufficient pH sensitivity to readily detect changes in pH<sub>e</sub> associated with both CCK and gastric distension. What's more, custom made Matlab script was able to use these changes in pH<sub>e</sub> to automatically trigger an implanted VNS device. SIGNIFICANCE: This is the first study to show pH<sub>e</sub> changes in peripheral nerves in vivo. Furthermore, the demonstration that iridium oxide microelectrodes are sufficiently pH sensitive as to measure changes in pH<sub>e</sub> associated with physiological stimuli means they have the potential to be integrated into closed-loop neurostimulating devices.
Evans BD, Nikolic K, 2017, From bytes to insights with modelling as a service a new paradigm for computational modelling illustrated with PyRhO, 12th IEEE Biomedical Circuits and Systems Conference (BioCAS), Publisher: IEEE, Pages: 316-319, ISSN: 2163-4025
Increasingly large datasets are being made publically available yet the methods and modelling skills to analyze them are lagging behind. In an effort to overcome barriers to analyzing and modelling data we propose the paradigm of Modelling as a Service (MaaS). As a proof-of-concept, we present a case study of the MaaS paradigm with computational tools for optogenetics based upon PyRhO. We demonstrate the benefits it confers in terms of enhanced scope for collaboration, reproducibility and ease of use, especially for scientists with a limited computational background, and discuss directions for future growth. Eventually we aim to grow this project in scope to encompass other modelling and analysis tools, and migrate to JupyterHub for persistent individual user accounts and storage. In the meantime, we hope that this approach will serve to demonstrate how MaaS can substantially increase the appeal and accessibility of modelling.
Gaspar N, Sondhi A, Evans B, et al., 2017, A Low-power Neuromorphic System for Retinal Implants and Sensory Substitution, 12th IEEE Biomedical Circuits and Systems Conference (BioCAS), Publisher: IEEE, Pages: 78-81, ISSN: 2163-4025
This paper describes the design and operation of a system which can be used as a Visual to Auditory Sensory Substitution Device (SSD), as well as the front-end of a real-time retinal prosthesis (RP) or Vision Augmentation (VA) system. Such systems consist of three components: a sensory block to capture the visual scene, a processing block to manage the collected data and generate stimulus patterns, and an output block. For the sensory block we use a Dynamic Vision Sensor (DVS) instead of a conventional camera. A microcontroller is used as the processing block, which receives asynchronous inputs from the DVS in the form of ON/OFF events and treats them like post-synaptic potentials. A simple algorithm based on an Integrate & Fire neuron model is used to emulate temporal contrast sensitive Retinal Ganglion Cells (RGCs). For an RP system the output would be an implanted electrode array, whereas for the SSD a sound is activated based on a certain mapping algorithm. The results are shown in the form of ON or OFF events on the LED matrix (equivalent to the stimulation pattern on an electrode array in the case of an RP), and in the form of a stereo sound output.
Luo J, Nikolic K, Evans BD, et al., 2016, Optogenetics in silicon: a neural processor for predicting optically active neural networks, IEEE Transactions on Biomedical Circuits and Systems, Vol: 11, Pages: 15-27, ISSN: 1940-9990
We present a reconfigurable neural processor for real-time simulation and prediction of opto-neural behaviour. We combined a detailed Hodgkin–Huxley CA3 neuron integrated with a four-state Channelrhodopsin-2 (ChR2) model into reconfigurable silicon hardware. Our architecture consists of a Field Programmable Gated Array (FPGA) with a custom-built computing data-path, a separate data management system and a memory approach based router. Advancements over previous work include the incorporation of short and long-term calcium and light-dependent ion channels in reconfigurable hardware. Also, the developed processor is computationally efficient, requiring only 0.03 ms processing time per sub-frame for a single neuron and 9.7 ms for a fully connected network of 500 neurons with a given FPGA frequency of 56.7 MHz. It can therefore be utilized for exploration of closed loop processing and tuning of biologically realistic optogenetic circuitry.
Benjamin E, Konstantin N, 2016, Raining fire upon modelling difficulties: PyRhO in the cloud
Nikolic K, Evans B, 2016, Identifying optimal feature transforms for classification and prediction in biological systems: recovering receptive field vectors from sparse recordings, International Conference on Machine Learning (ICML), Workshop on Computational Biology
With biological systems it is often hard to adequately sample the entire input space. With sensoryneural systems this can be a particularly acute problem, with very high dimensional natural inputs andtypically sparse spiking outputs. Here we present an information theory based approach to analysespiking data of an early sensory pathway, demonstrated on retinal ganglion cells (RGC) responding tonatural visual scene stimuli (Katz et al., 2016). We used a non-parametric technique based on theconcept of mutual information (MI), in particular, Quadratic Mutual Information (QMI). The QMIallowed us to very efficiently search the high dimensional space formed by the visual input for a muchsmaller dimensional subspace of Receptive Field Vectors (RFV). RFVs give the most informationabout the response of the cell to natural stimuli. This approach allows us to identify the RFVs far moreefficiently using limited data as we can search the complete stimulus space for multiple vectorssimultaneously. The RFVs were also used to predict the RGCs’ responses to any natural stimuli.Another suitable area of application of this algorithm is in diagnostic inference. Currently we areadapting the method to be used for identifying the cancer markers in the volatile organic compoundspresent in exhaled breath. Once the maximally informative features are established they can be usedfor diagnostic predictions on new breath samples. Preliminary results of the breathomics analysis willbe discussed at the conference.There are several other potential applications such as multiclass categorisation for bacterial strainsusing ISFET arrays for DNA sequencing. This algorithm can be part of a rapid point-of-care device foridentifying the specific infectious agents and recommending appropriate antibiotics.Here we will focus on presenting the algorithm using the example of RFVs of RGCs.
Troiani F, Nikolic K, Constandinou TG, 2016, Optical Coherence Tomography for detection of compound action potential in Xenopus Laevis sciatic nerve, SPIE Photonics West (BIOS)
Due to optical coherence tomography (OCT) high spatial and temporal resolution, this technique could be used to observe the quick changes in the refractive index that accompany action potential. In this study we explorethe use of time domain Optical Coherence Tomography (TD-OCT) for real time action potential detection in ex vivo Xenopus Laevis sciatic nerve. TD-OCT is the easiest and less expensive OCT technique and, if successful indetecting real time action potential, it could be used for low cost monitoring devices. A theoretical investigation into the order of magnitude of the signals detected by a TD-OCT setup is provided by this work. A lineardependence between the refractive index and the intensity changes is observed and the minimum SNR for which the setup could work is found to be SNR = 2 x10⁴.
Evans B, Jarvis S, Schultz S, et al., 2016, PyRhO: A Multiscale Optogenetics Simulation Platform, Frontiers in Neuroinformatics, Vol: 10, ISSN: 1662-5196
Optogenetics has become a key tool for understanding the function of neural circuits and controlling their behavior. An array of directly light driven opsins have been genetically isolated from several families of organisms, with a wide range of temporal and spectral properties. In order to characterize, understand and apply these opsins, we present an integrated suite of open-source, multi-scale computational tools called PyRhO. The purpose of developing PyRhO is three-fold: (i) to characterize new (and existing) opsins by automatically fitting a minimal set of experimental data to three-, four-, or six-state kinetic models, (ii) to simulate these models at the channel, neuron and network levels, and (iii) provide functional insights through model selection and virtual experiments in silico. The module is written in Python with an additional IPython/Jupyter notebook based GUI, allowing models to be fit, simulations to be run and results to be shared through simply interacting with a webpage. The seamless integration of model fitting algorithms with simulation environments (including NEURON and Brian2) for these virtual opsins will enable neuroscientists to gain a comprehensive understanding of their behavior and rapidly identify the most suitable variant for application in a particular biological system. This process may thereby guide not only experimental design and opsin choice but also alterations of the opsin genetic code in a neuro-engineering feed-back loop. In this way, we expect PyRhO will help to significantly advance optogenetics as a tool for transforming biological sciences.
Katz ML, Viney TJ, Nikolic K, 2016, Receptive field vectors of genetically-identified retinal ganglion cells reveal cell-type-dependent visual functions, PLOS One, Vol: 11, ISSN: 1932-6203
Sensory stimuli are encoded by diverse kinds of neurons but the identities of the recorded neurons that are studied are often unknown. We explored in detail the firing patterns of eight previously defined genetically-identified retinal ganglion cell (RGC) types from a single transgenic mouse line. We first introduce a new technique of deriving receptive field vectors (RFVs) which utilises a modified form of mutual information (“Quadratic Mutual Information”). We analysed the firing patterns of RGCs during presentation of short duration (~10 second) complex visual scenes (natural movies). We probed the high dimensional space formed by the visual input for a much smaller dimensional subspace of RFVs that give the most information about the response of each cell. The new technique is very efficient and fast and the derivation of novel types of RFVs formed by the natural scene visual input was possible even with limited numbers of spikes per cell. This approach enabled us to estimate the 'visual memory' of each cell type and the corresponding receptive field area by calculating Mutual Information as a function of the number of frames and radius. Finally, we made predictions of biologically relevant functions based on the RFVs of each cell type. RGC class analysis was complemented with results for the cells’ response to simple visual input in the form of black and white spot stimulation, and their classification on several key physiological metrics. Thus RFVs lead to predictions of biological roles based on limited data and facilitate analysis of sensory-evoked spiking data from defined cell types.
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.