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  • JOURNAL ARTICLE
    Faisal AA, White JA, Laughlin SB,

    Supplemental Data Ion-Channel Noise Places Limits on the Miniaturization of the Brain’s Wiring

  • JOURNAL ARTICLE
    Farina D, Castronovo AM, Vujaklija I, Sturma A, Salminger S, Hofer C, Aszmann OCet al.,

    COMMON SYNAPTIC INPUT TO MOTOR NEURONS AND NEURAL DRIVE TO TARGETED REINNERVATED MUSCLES

    , Journal of Neuroscience, ISSN: 0270-6474
  • JOURNAL ARTICLE
    Farina D, Yao, Sheng, Mrachacz-Kersting, Xiangyang, Ninget al.,

    Decoding covert somatosensory attention by a BCI system calibrated with tactile sensation

    , IEEE Transactions on Biomedical Engineering, ISSN: 0018-9294
  • JOURNAL ARTICLE
    Kirkpatrick J, Pascanu R, Rabinowitz N, Veness J, Desjardins G, Rusu AA, Milan K, Quan J, Ramalho T, Grabska-Barwinska A, Hassabis D, Clopath C, Kumaran D, Hadsell Ret al.,

    Overcoming catastrophic forgetting in neural networks

    , Proceedings of the National Academy of Sciences of the United States of America, ISSN: 1091-6490

    The ability to learn tasks in a sequential fashion is crucial to the developmentof artificial intelligence. Until now neural networks havenot been capable of this and it has been widely thought that catastrophicforgetting is an inevitable feature of connectionist models.We show that it is possible to overcome this limitation and train networksthat can maintain expertise on tasks which they have not experiencedfor a long time. Our approach remembers old tasks by selectivelyslowing down learning on the weights important for thosetasks. We demonstrate our approach is scalable and effective bysolving a set of classification tasks based on the MNIST hand writtendigit dataset and by learning several Atari 2600 games sequentially.

  • JOURNAL ARTICLE
    Liu Y, Luan S, Williams I, Rapeaux A, Constandinou TGet al.,

    A 64-Channel Versatile Neural Recording SoC with Activity Dependant Data Throughput

    , IEEE Transactions on Biomedical Circuits and Systems, Pages: 1-12, ISSN: 1932-4545

    Modern microtechnology is enabling the channel count of neural recording integrated circuits to scale exponentially. However, the raw data bandwidth of these systems is increasing proportionately, presenting major challenges in terms of power consumption and data transmission (especially for wireless systems). This paper presents a system that exploits the sparse nature of neural signals to address these challenges and provides a reconfigurable low-bandwidth event-driven output. Specifically, we present a novel 64-channel low noise (2.1μVrms, low power (23μW per analogue channel) neural recording system-on-chip (SoC). This features individually-configurable channels, 10-bit analogue-to-digital conversion, digital filtering, spike detection, and an event-driven output. Each channel's gain, bandwidth & sampling rate settings can be independently configured to extract Local Field Potentials (LFPs) at a low data-rate and/or Action Potentials (APs) at a higher data rate. The sampled data is streamed through an SRAM buffer that supports additional on-chip processing such as digital filtering and spike detection. Real-time spike detection can achieve ~2 orders of magnitude data reduction, by using a dual polarity simple threshold to enable an event driven output for neural spikes (16-sample window). The SoC additionally features a latency-encoded asynchronous output that is critical if used as part of a closed-loop system. This has been specifically developed to complement a separate on-node spike sorting co-processor to provide a real-time (low latency) output. The system has been implemented in a commercially-available 0.35μm CMOS technology occupying a silicon area of 19.1mm² (0.3mm² gross per channel), demonstrating a low power & efficient architecture which could be further optimised by aggressive technology and supply voltage scaling.

  • CONFERENCE PAPER
    Maimon-Mor RO, Fernandez-Quesada J, Zito GA, Konnaris C, Dziemian S, Faisal AAet al.,

    Towards free 3D end-point control for robotic-assisted human reaching using binocular eye tracking

    , 15th IEEE Conference on Rehabilitation Robotics (ICORR 2017), Publisher: IEEE

    Eye-movements are the only directly observablebehavioural signals that are highly correlated with actions atthe task level, and proactive of body movements and thus reflectaction intentions. Moreover, eye movements are preserved inmany movement disorders leading to paralysis (or amputees)from stroke, spinal cord injury, Parkinson’s disease, multiplesclerosis, and muscular dystrophy among others. Despite thisbenefit, eye tracking is not widely used as control interface forrobotic interfaces in movement impaired patients due to poorhuman-robot interfaces. We demonstrate here how combining3D gaze tracking using our GT3D binocular eye tracker withcustom designed 3D head tracking system and calibrationmethod enables continuous 3D end-point control of a roboticarm support system. The users can move their own hand to anylocation of the workspace by simple looking at the target andwinking once. This purely eye tracking based system enablesthe end-user to retain free head movement and yet achieves highspatial end point accuracy in the order of 6 cm RMSE error ineach dimension and standard deviation of 4 cm. 3D calibrationis achieved by moving the robot along a 3 dimensional spacefilling Peano curve while the user is tracking it with theireyes. This results in a fully automated calibration procedurethat yields several thousand calibration points versus standardapproaches using a dozen points, resulting in beyond state-of-the-art 3D accuracy and precision.

  • JOURNAL ARTICLE
    Makin T, de Vignemont F, Faisal AA,

    Neurocognitive considerations to the embodiment of technology

    , Nature Biomedical Engineering, ISSN: 2157-846X

    By exploiting robotics and information technology, teams of biomedical engineers are enhancing human sensory and motor abilities. Such augmentation technology ― to be worn, implanted or ingested ― aims to both restore and improve existing human capabilities (such as faster running, via exoskeletons), and to add new ones (for example, a ‘radar sense’). The development of augmentation technology is driven by rapid advances in human–machine interfaces, energy storage and mobile computing. Although engineers are embracing body augmentation from a technical perspective, little attention has been devoted to how the human brain might support such technological innovation. In this Comment, we highlight expected neurocognitive bottlenecks imposed by brain plasticity, adaptation and learning that could impact the design and performance of sensory and motor augmentation technology. We call for further consideration of how human–machine integration can be best achieved.

  • JOURNAL ARTICLE
    Nicola W, Clopath C,

    Supervised Learning in Spiking Neural Networks with FORCE Training

    Populations of neurons display an extraordinary diversity in the types ofproblems they solve and behaviors they display. Techniques have recentlyemerged that allow us to create networks of model neurons that solve tasks ofsimilar complexity. Examples include the FORCE method, a novel technique thatharnesses chaos to perform computations. We demonstrate the directapplicability of FORCE training to spiking neurons by training networks tomimic various dynamical systems in addition to reproducing more elaborate taskssuch as input classification, storing sequences, reproducing the singingbehavior of songbirds, and recalling a scene from a movie. Post-trainingnetwork analysis reveals behaviors that are consistent withelectrophysiological data, such as the stereotypical decrease in voltagevariance upon input presentation, reproducing firing rate distributions fromsongbird data, and reproducing locations of incorrect recall in sequencereplay. Finally, we demonstrate that theta oscillations are critical for bothlearning and recall of episodic memories.

  • CONFERENCE PAPER
    Noronha B, Dziemian S, Zito GA, Konnaris C, Faisal AAet al.,

    "Wink to grasp" – comparing Eye, Voice & EMG gesture control ofgrasp with soft-robotic gloves

    , IEEE Conference on Rehabilitation Robotics (ICORR 2017), Publisher: IEEE

    The ability of robotic rehabilitation devices tosupport paralysed end-users is ultimately limited by the degreeto which human-machine-interaction is designed to be effectiveand efficient in translating user intention into robotic action.Specifically, we evaluate the novel possibility of binocular eye-tracking technology to detect voluntary winks from involuntaryblink commands, to establish winks as a novel low-latencycontrol signal to trigger robotic action. By wearing binoculareye-tracking glasses we enable users to directly observe theirenvironment or the actuator and trigger movement actions,without having to interact with a visual display unit or userinterface. We compare our novel approach to two conventionalapproaches for controlling robotic devices based on electromyo-graphy (EMG) and speech-based human-computer interactiontechnology. We present an integrated software framework basedon ROS that allows transparent integration of these multiplemodalities with a robotic system. We use a soft-robotic SEMglove (Bioservo Technologies AB, Sweden) to evaluate how the 3modalities support the performance and subjective experienceof the end-user when movement assisted. All 3 modalitiesare evaluated in streaming, closed-loop control operation forgrasping physical objects. We find that wink control showsthe lowest error rate mean with lowest standard deviation of(0.23±0.07, mean±SEM) followed by speech control (0.35±0.13) and EMG gesture control (using the Myo armband byThalamic Labs), with the highest mean and standard deviation(0.46±0.16). We conclude that with our novel own developedeye-tracking based approach to control assistive technologies isa well suited alternative to conventional approaches, especiallywhen combined with 3D eye-tracking based robotic end-pointcontrol.

  • CONFERENCE PAPER
    Angeles P, Tai Y, Pavese N, Vaidyanathan Ret al., 2017,

    Assessing Parkinson's disease motor symptoms using supervised learning algorithms

    , 21st International Congress of Parkinson's Disease and Movement Disorders, Publisher: WILEY, ISSN: 0885-3185
  • CONFERENCE PAPER
    Angeles P, Tai Y, Pavese N, Wilson S, Vaidyanathan Ret al., 2017,

    Automated assessment of symptom severity changes during deep brain stimulation (DBS) therapy for Parkinson's disease.

    , Pages: 1512-1517

    Deep brain stimulation (DBS) is currently being used as a treatment for symptoms of Parkinson's disease (PD). Tracking symptom severity progression and deciding the optimal stimulation parameters for people with PD is extremely difficult. This study presents a sensor system that can quantify the three cardinal motor symptoms of PD - rigidity, bradykinesia and tremor. The first phase of this study assesses whether data recorded from the system during physical examinations can be used to correlate to clinician's severity score using supervised machine learning (ML) models. The second phase concludes whether the sensor system can distinguish differences before and after DBS optimisation by a clinician when Unified Parkinson's Disease Rating Scale (UPDRS) scores did not change. An average accuracy of 90.9 % was achieved by the best ML models in the first phase, when correlating sensor data to clinician's scores. Adding on to this, in the second phase of the study, the sensor system was able to pick up discernible differences before and after DBS optimisation sessions in instances where UPDRS scores did not change.

  • JOURNAL ARTICLE
    Bass C, Helkkula P, De Paola V, Clopath C, Bharath AAet al., 2017,

    Detection of axonal synapses in 3D two-photon images

    , PLOS ONE, Vol: 12, ISSN: 1932-6203
  • JOURNAL ARTICLE
    Bishop CA, Newbould RD, Lee JSZ, Honeyfield L, Quest R, Colasanti A, Ali R, Mattoscio M, Cortese A, Nicholas R, Matthews PM, Muraro PA, Waldman ADet al., 2017,

    Analysis of ageing-associated grey matter volume in patients with multiple sclerosis shows excess atrophy in subcortical regions

    , NEUROIMAGE-CLINICAL, Vol: 13, Pages: 9-15, ISSN: 2213-1582
  • JOURNAL ARTICLE
    Burridge JH, Lee ACW, Turk R, Stokes M, Whitall J, Vaidyanathan R, Clatworthy P, Hughes A-M, Meagher C, Franco E, Yardley Let al., 2017,

    Telehealth, Wearable Sensors, and the Internet: Will They Improve Stroke Outcomes Through Increased Intensity of Therapy, Motivation, and Adherence to Rehabilitation Programs?

    , JOURNAL OF NEUROLOGIC PHYSICAL THERAPY, Vol: 41, Pages: S32-S38, ISSN: 1557-0576
  • CONFERENCE PAPER
    Castronovo M, Mrachacz-Kersting N, Landi F, Jørgensen HR, Severinsen K, Farina Det al., 2017,

    Motor Unit Coherence at Low Frequencies Increases Together with Cortical Excitability Following a Brain-Computer Interface Intervention in Acute Stroke Patients

    , Pages: 1001-1005, ISSN: 2195-3562

    © Springer International Publishing AG 2017. This study aims at investigating the neurophysiological correlates of increased cortical excitability following a Brain-Computer interface based intervention in three acute stroke survivors. The analysis was performed on high-density EMG signals recorded from the Tibialis Anterior muscle. All patients showed an increased excitability in the motor cortex area of interest following the BCI intervention. Moreover, coherence between motor unit spike trains increased in the frequency band 1–5, Hz, suggesting an increase in the common oscillatory drive to the target muscle.

  • JOURNAL ARTICLE
    Caze RD, Jarvis S, Foust AJ, Schultz SRet al., 2017,

    Dendrites Enable a Robust Mechanism for Neuronal Stimulus Selectivity

    , NEURAL COMPUTATION, Vol: 29, Pages: 2511-2527, ISSN: 0899-7667
  • CONFERENCE PAPER
    Dávila-Montero S, Barsakcioglu DY, Jackson A, Constandinou TG, Mason AJet al., 2017,

    Real-time clustering algorithm that adapts to dynamic changes in neural recordings

    , IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, Pages: 690-693

    This work presents a computationally efficient real-time adaptive clustering algorithm that recognizes and adapts to dynamic changes observed in neural recordings. The algorithm consists of an off-line training phase that determines initial cluster positions, and an on-line operation phase that continuously tracks drifts in clusters and periodically verifies acute changes in cluster composition. Analysis of chronic recordings from non-human primates shows that adaptive clustering achieves an improvement of 14% in classification accuracy and demonstrates an ability to recognize acute changes with 78% accuracy, with up to 29% computational efficiency compared to the state-of-the-art. The presented algorithm is suitable for long-term chronic monitoring of neural activity in various applications such as neuroscience research and control of neural prosthetics and assistive devices.

  • JOURNAL ARTICLE
    Datta G, Violante IR, Scott G, Zimmerman K, Santos-Ribeiro A, Rabiner EA, Gunn RN, Malik O, Ciccarelli O, Nicholas R, Matthews PMet al., 2017,

    Translocator positron-emission tomography and magnetic resonance spectroscopic imaging of brain glial cell activation in multiple sclerosis

    , MULTIPLE SCLEROSIS JOURNAL, Vol: 23, Pages: 1469-1478, ISSN: 1352-4585
  • CONFERENCE PAPER
    De Marcellis A, Palange E, Faccio M, Stanchieri GDP, Constandinou TGet al., 2017,

    A 250Mbps 24pJ/bit UWB-inspired Optical Communication System for Bioimplants

    , Turin, Italy, IEEE Biomedical Circuits and Systems (BioCAS) Conference, Pages: 1-4
  • JOURNAL ARTICLE
    Dreier JP, Fabricius M, Ayata C, Sakowitz OW, Shuttleworth CW, Dohmen C, Graf R, Vajkoczy P, Helbok R, Suzuki M, Schiefecker AJ, Major S, Winkler MKL, Kang E-J, Milakara D, Oliveira-Ferreira AI, Reiffurth C, Revankar GS, Sugimoto K, Dengler NF, Hecht N, Foreman B, Feyen B, Kondziella D, Friberg CK, Piilgaard H, Rosenthal ES, Westover MB, Maslarova A, Santos E, Hertle D, Sanchez-Porras R, Jewell SL, Balanca B, Platz J, Hinzman JM, Lueckl J, Schoknecht K, Schoell M, Drenckhahn C, Feuerstein D, Eriksen N, Horst V, Bretz JS, Jahnke P, Scheel M, Bohner G, Rostrup E, Pakkenberg B, Heinemann U, Claassen J, Carlson AP, Kowoll CM, Lublinsky S, Chassidim Y, Shelef I, Friedman A, Brinker G, Reiner M, Kirov SA, Andrew RD, Farkas E, Gueresir E, Vatter H, Chung LS, Brennan KC, Lieutaud T, Marinesco S, Maas AIR, Sahuquillo J, Dahlem MA, Richter F, Herreras O, Boutelle MG, Okonkwo DO, Bullock MR, Witte OW, Martus P, van den Maagdenberg AMJM, Ferrari MD, Dijkhuizen RM, Shutter LA, Andaluz N, Schulte AP, MacVicar B, Watanabe T, Woitzik J, Lauritzen M, Strong AJ, Hartings JAet al., 2017,

    Recording, analysis, and interpretation of spreading depolarizations in neurointensive care: Review and recommendations of the COSBID research group

    , JOURNAL OF CEREBRAL BLOOD FLOW AND METABOLISM, Vol: 37, Pages: 1595-1625, ISSN: 0271-678X
  • CONFERENCE PAPER
    Feng P, Constandinou TG, Yeon P, Ghovanloo Met al., 2017,

    Millimeter-Scale Integrated and Wirewound Coils for Powering Implantable Neural Microsystems

    , IEEE Biomedical Circuits and Systems (BioCAS) Conference, Pages: 1-4
  • CONFERENCE PAPER
    Gao C, Ghoreishizadeh S, Liu Y, Constandinou TGet al., 2017,

    On-chip ID generation for multi-node implantable devices using SA-PUF

    , IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, Pages: 678-681

    This paper presents a 64-bit on-chip identification system featuring low power consumption and randomness compensation for multi-node bio-implantable devices. A sense amplifier based bit-cell is proposed to realize the silicon physical unclonable function, providing a unique value whose probability has a uniform distribution and minimized influence from the temperature and supply variation. The entire system is designed and implemented in a typical 0.35 m CMOS technology, including an array of 64 bit-cells, readout circuits, and digital controllers for data interfaces. Simulated results show that the proposed bit-cell design achieved a uniformity of 50.24% and a uniqueness of 50.03% for generated IDs. The system achieved an energy consumption of 6.0 pJ per bit with parallel outputs and 17.3 pJ per bit with serial outputs.

  • JOURNAL ARTICLE
    Ghajari M, Hellyer PJ, Sharp DJ, 2017,

    Computational modelling of traumatic brain injury predicts the location of chronic traumatic encephalopathy pathology

    , BRAIN, Vol: 140, Pages: 333-343, ISSN: 0006-8950
  • CONFERENCE PAPER
    Ghoreishizadeh SS, Haci D, Liu Y, Constandinou TGet al., 2017,

    A 4-Wire Interface SoC for Shared Multi- Implant Power Transfer and Full-duplex Communication

    , 8th IEEE Latin American Symposium on Circuits & Systems (LASCAS), Publisher: IEEE
  • JOURNAL ARTICLE
    Ghoreishizadeh SS, Haci D, Liu Y, Donaldson N, Constandinou TGet al., 2017,

    Four-Wire Interface ASIC for a Multi-Implant Link

    , IEEE Transactions on Circuits and Systems I: Regular Papers, Pages: 1-12, ISSN: 1549-8328
  • CONFERENCE PAPER
    Haci D, Liu Y, Constandinou TG, 2017,

    32-channel ultra-low-noise arbitrary signal generation platform for biopotential emulation

    , IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, Pages: 698-701

    This paper presents a multichannel, ultra-low-noise arbitrary signal generation platform for emulating a wide range of different biopotential signals (e.g. ECG, EEG, etc). This is intended for use in the test, measurement and demonstration of bioinstrumentation and medical devices that interface to electrode inputs. The system is organized in 3 key blocks for generating, processing and converting the digital data into a parallel high performance analogue output. These blocks consist of: (1) a Raspberry Pi 3 (RPi3) board; (2) a custom Field Programmable Gate Array (FPGA) board with low-power IGLOO Nano device; and (3) analogue board including the Digital-to-Analogue Converters (DACs) and output circuits. By implementing the system this way, good isolation can be achieved between the different power and signal domains. This mixed-signal architecture takes in a high bitrate SDIO (Secure Digital Input Output) stream, recodes and packetizes this to drive two multichannel DACs, with parallel analogue outputs that are then attenuated and filtered. The system achieves 32-parallel output channels each sampled at 48kS/s, with a 10kHz bandwidth, 110dB dynamic range and uV-level output noise.

  • JOURNAL ARTICLE
    Hartings JA, Shuttleworth CW, Kirov SA, Ayata C, Hinzman JM, Foreman B, Andrew RD, Boutelle MG, Brennan KC, Carlson AP, Dahlem MA, Drenckhahn C, Dohmen C, Fabricius M, Farkas E, Feuerstein D, Graf R, Helbok R, Lauritzen M, Major S, Oliveira-Ferreira AI, Richter F, Rosenthal ES, Sakowitz OW, Sanchez-Porras R, Santos E, Scholl M, Strong AJ, Urbach A, Westover MB, Winkler MKL, Witte OW, Woitzik J, Dreier JPet al., 2017,

    The continuum of spreading depolarizations in acute cortical lesion development: Examining Le(a)over-tilde-$o's legacy

    , JOURNAL OF CEREBRAL BLOOD FLOW AND METABOLISM, Vol: 37, Pages: 1571-1594, ISSN: 0271-678X
  • CONFERENCE PAPER
    Huang H-Y, Farkhatdinov I, Arami A, Burdet Eet al., 2017,

    Modelling Neuromuscular Function of SCI Patients in Balancing

    , 3rd International Conference on NeuroRehabilitation (ICNR), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 355-359, ISSN: 2195-3562
  • JOURNAL ARTICLE
    Huntley JD, Hampshire A, Bor D, Owen AM, Howard RJet al., 2017,

    The importance of sustained attention in early Alzheimer's disease

    , International Journal of Geriatric Psychiatry, Vol: 32, Pages: 860-867, ISSN: 0885-6230

    Copyright © 2016 John Wiley & Sons, Ltd. Introduction: There is conflicting evidence regarding impairment of sustained attention in early Alzheimer's disease (AD). We examine whether sustained attention is impaired and predicts deficits in other cognitive domains in early AD. Methods: Fifty-one patients with early AD (MMSE > 18) and 15 healthy elderly controls were recruited. The sustained attention to response task (SART) was used to assess sustained attention. A subset of 25 patients also performed tasks assessing general cognitive function (ADAS-Cog), episodic memory (Logical memory scale, Paired Associates Learning), executive function (verbal fluency, grammatical reasoning) and working memory (digit and spatial span). Results: AD patients were significantly impaired on the SART compared to healthy controls (total error β = 19.75, p = 0.027). SART errors significantly correlated with MMSE score (Spearman's rho = −0.338, p = 0.015) and significantly predicted deficits in ADAS-Cog (β = 0.14, p = 0.004). Discussions: Patients with early AD have significant deficits in sustained attention, as measured using the SART. This may impair performance on general cognitive testing, and therefore should be taken into account during clinical assessment, and everyday management of individuals with early AD. Copyright © 2016 John Wiley & Sons, Ltd.

  • JOURNAL ARTICLE
    Leene LB, Constandinou TG, 2017,

    Time Domain Processing Techniques Using Ring Oscillator-Based Filter Structures

    , IEEE Transactions on Circuits and Systems I: Regular Papers, Pages: 1-10, ISSN: 1549-8328

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