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  • Journal article
    Navajas J, Barsakcioglu D, Eftekhar A, Jackson A, Constandinou TG, Quian Quiroga Ret al., 2014,

    Minimum Requirements for Accurate and Efficient Real-Time On-Chip Spike Sorting

    , Journal of Neuroscience Methods, Pages: 51-64
  • Journal article
    Barsakcioglu D, Liu Y, Bhunjun P, Navajas J, Eftekhar A, Jackson A, Quian Quiroga R, Constandinou TGet al., 2014,

    An Analogue Front-End Model for Developing Neural Spike Sorting Systems

    , IEEE Transactions on Biomedical Circuits and Systems, Vol: 8, Pages: 216-227
  • Conference paper
    Yang Y, Boling S, Eftekhar A, Paraskevopoulou SE, Constandinou TG, Mason AJet al., 2014,

    Computationally efficient feature denoising filter and selection of optimal features for noise insensitive spike sorting

    , IEEE Annual Meeting of the Engineering in Biology and Medicine Society (EMBC), Publisher: IEEE
  • Conference paper
    Zheng L, Leene L, Liu Y, Constandinou TGet al., 2014,

    An Adaptive 16/64 kHz, 9-bit SAR ADC with Peak-Aligned Sampling for Neural Spike Recording

    , IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, Pages: 2385-2388
  • Conference paper
    Williams I, Constandinou TG, 2013,

    Modelling muscle spindle dynamics for a proprioceptive prosthesis

    , Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), Publisher: IEEE

    Muscle spindles are found throughout our skeletalmuscle tissue and continuously provide us with a sense of our limbs position and motion (proprioception). This paper advances a model for generating artificial muscle spindle signalsfor a prosthetic limb, with the aim of one day providing amputees with a sense of feeling in their artificial limb. By utilising the Opensim biomechanical modelling package the relationship between a joints angle and the length of surrounding muscles is estimated for a prosthetic limb. This is then applied to the established Mileusnic model to determine the associated muscle spindle firing pattern. This complete system model is then reduced to allow for a computationallyefficient hardware implementation. This reduction is achieved with minimal impact on accuracy by selecting key monoarticular muscles and fitting equations to relate joint angle to muscle length. Parameter values fitting the Mileusnic modelto human spindles are then proposed and validated against previously published human neural recordings. Finally, a model for fusimotor signals is also proposed based on data previously recorded from reduced animal experiments.

  • Conference paper
    Barsakcioglu DY, Eftekhar A, Constandinou TG, 2013,

    Design Optimisation of Front-End Neural Interfaces for Spike Sorting Systems

    , IEEE International Symposium on Circuits and Systems (ISCAS)

    This work investigates the impact of the analoguefront-end design (pre-amplifier, filter and converter) on spike sorting performance in neural interfaces. By examining key design parameters including the signal-to-noise ratio, bandwidth,filter type/order, data converter resolution and sampling rate, their sensitivity to spike sorting accuracy is assessed. This is applied to commonly used spike sorting methods such as template matching, 2nd derivative-features, and principle component analysis. The results reveal a near optimum set of parameters to increase performance given the hardware-constraints. Finally, the relative costs of these design parameters on resource efficiency (silicon area and power requirements) are quantified through reviewing the state-of-the-art.

  • Conference paper
    Koutsos E, Paraskevopoulou SE, Constandinou TG, 2013,

    A 1.5μW NEO-based Spike Detector with Adaptive-Threshold for Calibration-free Multichannel Neural Interfaces

    , IEEE International Symposium on Circuits and Systems (ISCAS)

    This paper presents a novel front-end circuit for detecting action potentials in extracellular neural recordings. By implementing a real-time, adaptive algorithm to determine an effective threshold for robustly detecting a spike, the need for calibration and/or external monitoring is eliminated. The input signal is first pre-processed by utilising a non-linear energy operator (NEO) to effectively boost the signal-to-noise ratio (SNR) of the spike feature of interest. The spike detection threshold is then determined by tracking the peak NEO response and applying a non-linear gain to realise an adaptive response to different spike amplitudes and background noise levels. The proposed algorithm and its implementation is shown to achieve both accurate and robust spike detection, by minimising falsely detected spikes and/or missed spikes. The system has been implemented in a commercially available 0.18μm technology requiring a total power consumption of 1.5μW from a 1.8V supply and occupying a compact footprint of only 0.03$\,$mm$^2$ silicon area. The proposed circuit is thus ideally suited for high-channel count, calibration-free, neural interfaces.

  • Journal article
    Williams I, Constandinou TG, 2013,

    An Energy-Efficient, Dynamic Voltage Scaling Neural Stimulator for a Proprioceptive Prosthesis

    , IEEE Transactions on Biomedical Circuits and Systems, Vol: 7, Pages: 129-139

    This paper presents an 8 channel energy-efficient neural stimulator for generating charge-balanced asymmetric pulses. Power consumption is reduced by implementing a fully integrated DC-DC converter that uses a reconfigurable switched capacitor topology to provide 4 output voltages for Dynamic Voltage Scaling (DVS). DC conversion efficiencies of up to 82% are achieved using integrated capacitances of under 1 nF and the DVS approach offers power savings of up to 50% compared to the front end of a typical current controlled neural stimulator. A novel charge balancing method is implemented which has a low level of accuracy on a single pulse and a much higher accuracy over a series of pulses. The method used is robust to process and component variation and does not require any initial or ongoing calibration. Measured results indicate that the charge imbalance is typically between 0.05% - 0.15% of charge injected for a series of pulses. Ex-vivo experiments demonstrate the viability in using this circuit for neural activation. The circuit has been implemented in a commercially-available 0.18μm HV CMOS technology and occupies a core die area of approximately 2.8mm² for an 8 channel implementation.

  • Journal article
    Paraskevopoulou SE, Barsakcioglu D, Saberi M, Eftekhar A, Constandinou TGet al., 2013,

    Feature Extraction using First and Second Derivative Extrema (FSDE), for Real-time and Hardware-Efficient Spike Sorting

    , Journal of Neuroscience Methods, Vol: 215, Pages: 29-37, ISSN: 0165-0270

    Next generation neural interfaces aspire to achieve real-time multi-channel systems by integrating spike sorting on chip to overcome limitations in communication channel capacity. The feasibility of this approach relies on developing highly-efficient algorithms for feature extraction and clustering with the potential of low-power hardware implementation. We are proposing a feature extraction method, not requiring any calibration, based on first and second derivative features of the spike waveform. The accuracy and computational complexity of the proposed method are quantified and compared against commonly used feature extraction methods, through simulation across four datasets (with different single units) at multiple noise levels (ranging from 5 to 20% of the signal amplitude). The average classification error is shown to be below 7% with a computational complexity of 2N-3, where N is the number of sample points of each spike. Overall, this method presents a good trade-off between accuracy and computational complexity and is thus particularly well-suited for hardware-efficient implementation.

  • Journal article
    武藤 弘, Knopfel T, 2011,

    Engineering voltage-sensitive fluorescent protein and monitoring neuronal activities

    , Bio industry, Vol: 28, Pages: 18-25, ISSN: 0910-6545
  • Journal article
    Hellyer PJ, Clopath C, Kehagia AA, Turkheimer FE, Leech Ret al.,

    Balanced activation in a simple embodied neural simulation

    In recent years, there have been many computational simulations ofspontaneous neural dynamics. Here, we explore a model of spontaneous neuraldynamics and allow it to control a virtual agent moving in a simpleenvironment. This setup generates interesting brain-environment feedbackinteractions that rapidly destabilize neural and behavioral dynamics andsuggest the need for homeostatic mechanisms. We investigate roles for bothlocal homeostatic plasticity (local inhibition adjusting over time to balanceexcitatory input) as well as macroscopic task negative activity (thatcompensates for task positive, sensory input) in regulating both neuralactivity and resulting behavior (trajectories through the environment). Ourresults suggest complementary functional roles for both local homeostaticplasticity and balanced activity across brain regions in maintaining neural andbehavioral dynamics. These findings suggest important functional roles forhomeostatic systems in maintaining neural and behavioral dynamics and suggest anovel functional role for frequently reported macroscopic task-negativepatterns of activity (e.g., the default mode network).

  • Journal article
    Faisal AA, White JA, Laughlin SB,

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

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