Publications by authors named "Anthony N Burkitt"

93 Publications

Neural activity shaping utilizing a partitioned target pattern.

J Neural Eng 2021 Mar 8. Epub 2021 Mar 8.

Australian College of Optometry, Parkville, Carlton, Victoria, 3010, AUSTRALIA.

Electrical stimulation of neural tissue is used in both clinical and experimental devices to evoke a desired spatiotemporal pattern of neural activity. These devices induce a local field that drives neural activation, referred to as an activating function or generator signal. In visual prostheses, the spread of generator signal from each electrode within the neural tissue results in a spread of visual perception, referred to as a phosphene. In cases where neighboring phosphenes overlap, it is desirable to use current steering or neural activity shaping strategies to manipulate the generator signal between the electrodes to provide greater control over the total pattern of neural activity. Applying opposite generator signal polarities in neighboring regions of the retina forces the generator signal to pass through zero at an intermediate point, thus inducing low neural activity that may be perceived as a high-contrast line. This approach provides a form of high contrast visual perception, but it requires partitioning of the target pattern into those regions that use positive or negative generator signals. This discrete optimization is an NP-hard problem that is subject to being trapped in detrimental local minima. This investigation proposes a new partitioning method using image segmentation to determine the most beneficial positive and negative generator signal regions. Utilizing a database of 1000 natural images, the method is compared to alternative approaches based upon the mean squared error of the outcome. Under nominal conditions and with a set computation limit, partitioning provided improvement for 32% of these images. This percentage increased to 89% when utilizing image pre-processing to emphasize perceptual features of the images. The percentage of images that were dealt with most effectively with image segmentation increased as lower computation limits were imposed on the algorithms.
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http://dx.doi.org/10.1088/1741-2552/abecc4DOI Listing
March 2021

Learning receptive field properties of complex cells in V1.

PLoS Comput Biol 2021 Mar 2;17(3):e1007957. Epub 2021 Mar 2.

Department of Biomedical Engineering, The University of Melbourne, Melbourne, Australia.

There are two distinct classes of cells in the primary visual cortex (V1): simple cells and complex cells. One defining feature of complex cells is their spatial phase invariance; they respond strongly to oriented grating stimuli with a preferred orientation but with a wide range of spatial phases. A classical model of complete spatial phase invariance in complex cells is the energy model, in which the responses are the sum of the squared outputs of two linear spatially phase-shifted filters. However, recent experimental studies have shown that complex cells have a diverse range of spatial phase invariance and only a subset can be characterized by the energy model. While several models have been proposed to explain how complex cells could learn to be selective to orientation but invariant to spatial phase, most existing models overlook many biologically important details. We propose a biologically plausible model for complex cells that learns to pool inputs from simple cells based on the presentation of natural scene stimuli. The model is a three-layer network with rate-based neurons that describes the activities of LGN cells (layer 1), V1 simple cells (layer 2), and V1 complex cells (layer 3). The first two layers implement a recently proposed simple cell model that is biologically plausible and accounts for many experimental phenomena. The neural dynamics of the complex cells is modeled as the integration of simple cells inputs along with response normalization. Connections between LGN and simple cells are learned using Hebbian and anti-Hebbian plasticity. Connections between simple and complex cells are learned using a modified version of the Bienenstock, Cooper, and Munro (BCM) rule. Our results demonstrate that the learning rule can describe a diversity of complex cells, similar to those observed experimentally.
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http://dx.doi.org/10.1371/journal.pcbi.1007957DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7954310PMC
March 2021

High-Frequency Oscillations in Epilepsy: What Have We Learned and What Needs to be Addressed.

Neurology 2021 03 6;96(9):439-448. Epub 2021 Jan 6.

From the Department of Biomedical Engineering (Z.C., A.N.B., M.J.C., D.B.G.), The University of Melbourne and Department of Medicine (Z.C., M.I.M., M.J.C., D.B.G.), St Vincent's Hospital, The University of Melbourne; Seer Medical (M.I.M.), Melbourne; and Graeme Clark Institute (M.J.C.), The University of Melbourne, VIC, Australia.

For the past 2 decades, high-frequency oscillations (HFOs) have been enthusiastically studied by the epilepsy community. Emerging evidence shows that HFOs harbor great promise to delineate epileptogenic brain areas and possibly predict the likelihood of seizures. Investigations into HFOs in clinical epilepsy have advanced from small retrospective studies relying on visual identification and correlation analysis to larger prospective assessments using automatic detection and prediction strategies. Although most studies have yielded promising results, some have revealed significant obstacles to clinical application of HFOs, thus raising debate about the reliability and practicality of HFOs as clinical biomarkers. In this review, we give an overview of the current state of HFO research and pinpoint the conceptual and methodological issues that have hampered HFO translation. We highlight recent insights gained from long-term data, high-density recordings, and multicenter collaborations and discuss the open questions that need to be addressed in future research.
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http://dx.doi.org/10.1212/WNL.0000000000011465DOI Listing
March 2021

Spatiotemporal Patterns of High-Frequency Activity (80-170 Hz) in Long-Term Intracranial EEG.

Neurology 2021 02 22;96(7):e1070-e1081. Epub 2020 Dec 22.

From the Department of Biomedical Engineering (Z.C., D.B.G., A.N.B., P.J.K, M.J.C.), and Department of Medicine (Z.C., D.B.G., U.S., W.J.D., K.D., M.J.C., M.I.M.), St Vincent's Hospital, Department of Medicine (C.F.), Royal Melbourne Hospital, and Graeme Clark Institute (P.J.K., M.J.C.), The University of Melbourne, VIC, Australia; Cadence Neuroscience (K.L.), Redmond, WA; and 6 Seer Medical (M.I.M.), Melbourne, VIC, Australia.

Objective: To determine the utility of high-frequency activity (HFA) and epileptiform spikes as biomarkers for epilepsy, we examined the variability in their rates and locations using long-term ambulatory intracranial EEG (iEEG) recordings.

Methods: This study used continuous iEEG recordings obtained over an average of 1.4 years from 15 patients with drug-resistant focal epilepsy. HFA was defined as 80- to 170-Hz events with amplitudes clearly larger than the background, which was automatically detected with a custom algorithm. The automatically detected HFA was compared with visually annotated high-frequency oscillations (HFOs). The variations of HFA rates were compared with spikes and seizures on patient-specific and electrode-specific bases.

Results: HFA included manually annotated HFOs and high-amplitude events occurring in the 80- to 170-Hz range without observable oscillatory behavior. HFA and spike rates had high amounts of intrapatient and interpatient variability. Rates of HFA and spikes had large variability after electrode implantation in most of the patients. Locations of HFA and spikes varied up to weeks in more than one-third of the patients. Both HFA and spike rates showed strong circadian rhythms in all patients, and some also showed multiday cycles. Furthermore, the circadian patterns of HFA and spike rates had patient-specific correlations with seizures, which tended to vary across electrodes.

Conclusion: Analysis of HFA and epileptiform spikes should consider postimplantation variability. HFA and epileptiform spikes, like seizures, show circadian rhythms. However, the circadian profiles can vary spatially within patients, and their correlations to seizures are patient-specific.
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http://dx.doi.org/10.1212/WNL.0000000000011408DOI Listing
February 2021

A neurophysiological approach to spatial filter selection for adaptive brain-computer interfaces.

J Neural Eng 2020 Dec 18. Epub 2020 Dec 18.

Department of Biomedical Engineering, The University of Melbourne, 203 Bouverie St, Parkville, Victoria, 3010, AUSTRALIA.

Objective: The Common Spatial Patterns (CSP) algorithm is an effective method to extract discriminatory features from electroencephalography (EEG) to be used by a brain-computer interface (BCI). However, informed selection of CSP filters typically requires oversight from a BCI expert to accept or reject filters based on the neurophysiological plausibility of their activation patterns. Our goal was to identify, analyze and automatically classify prototypical CSP patterns to enhance the prediction of motor imagery states in a BCI.

Approach: A data-driven approach that used four publicly available EEG datasets was adopted. Cluster analysis revealed recurring, visually similar CSP patterns and a convolutional neural network was developed to distinguish between established CSP pattern classes. Furthermore, adaptive spatial filtering schemes that utilize the categorization of CSP patterns were proposed and evaluated.

Main Results: Classes of common neurophysiologically probable and improbable CSP patterns were established. Analysis of the relationship between these categories of CSP patterns and classification performance revealed discarding neurophysiologically improbable filters can decrease decoder performance. Further analysis revealed that the spatial orientation of EEG modulations can evolve over time, and that the features extracted from the original CSP filters can become inseparable. Importantly, it was shown through a novel adaptive CSP technique that adaptation in response to these emerging patterns can restore feature separability.

Significance: These findings highlight the importance of considering and reporting on spatial filter activation patterns in both online and offline studies. They also emphasize to researchers in the field the importance of spatial filter adaptation in BCI decoder design, particularly for online studies with a focus on training users to develop stable and suitable brain patterns.
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http://dx.doi.org/10.1088/1741-2552/abd51fDOI Listing
December 2020

Adaptive Surround Modulation of MT Neurons: A Computational Model.

Front Neural Circuits 2020 26;14:529345. Epub 2020 Oct 26.

National Vision Research Institute, Australian College of Optometry, Carlton, VIC, Australia.

The classical receptive field (CRF) of a spiking visual neuron is defined as the region in the visual field that can generate spikes when stimulated by a visual stimulus. Many visual neurons also have an extra-classical receptive field (ECRF) that surrounds the CRF. The presence of a stimulus in the ECRF does not generate spikes but rather modulates the response to a stimulus in the neuron's CRF. Neurons in the primate Middle Temporal (MT) area, which is a motion specialist region, can have directionally antagonistic or facilitatory surrounds. The surround's effect switches between directionally antagonistic or facilitatory based on the characteristics of the stimulus, with antagonistic effects when there are directional discontinuities but facilitatory effects when there is directional coherence. Here, we present a computational model of neurons in area MT that replicates this observation and uses computational building blocks that correlate with observed cell types in the visual pathways to explain the mechanism of this modulatory effect. The model shows that the categorization of MT neurons based on the effect of their surround depends on the input stimulus rather than being a property of the neurons. Also, in agreement with neurophysiological findings, the ECRFs of the modeled MT neurons alter their center-surround interactions depending on image contrast.
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http://dx.doi.org/10.3389/fncir.2020.529345DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7649322PMC
October 2020

Motor neuroprosthesis implanted with neurointerventional surgery improves capacity for activities of daily living tasks in severe paralysis: first in-human experience.

J Neurointerv Surg 2021 Feb 28;13(2):102-108. Epub 2020 Oct 28.

Vascular Bionics Laboratory, Departments of Medicine, Neurology and Surgery, Melbourne Brain Centre at the Royal Melbourne Hospital, The University of Melbourne, Melbourne, Victoria, Australia.

Background: Implantable brain-computer interfaces (BCIs), functioning as motor neuroprostheses, have the potential to restore voluntary motor impulses to control digital devices and improve functional independence in patients with severe paralysis due to brain, spinal cord, peripheral nerve or muscle dysfunction. However, reports to date have had limited clinical translation.

Methods: Two participants with amyotrophic lateral sclerosis (ALS) underwent implant in a single-arm, open-label, prospective, early feasibility study. Using a minimally invasive neurointervention procedure, a novel endovascular Stentrode BCI was implanted in the superior sagittal sinus adjacent to primary motor cortex. The participants undertook machine-learning-assisted training to use wirelessly transmitted electrocorticography signal associated with attempted movements to control multiple mouse-click actions, including zoom and left-click. Used in combination with an eye-tracker for cursor navigation, participants achieved Windows 10 operating system control to conduct instrumental activities of daily living (IADL) tasks.

Results: Unsupervised home use commenced from day 86 onwards for participant 1, and day 71 for participant 2. Participant 1 achieved a typing task average click selection accuracy of 92.63% (100.00%, 87.50%-100.00%) (trial mean (median, Q1-Q3)) at a rate of 13.81 (13.44, 10.96-16.09) correct characters per minute (CCPM) with predictive text disabled. Participant 2 achieved an average click selection accuracy of 93.18% (100.00%, 88.19%-100.00%) at 20.10 (17.73, 12.27-26.50) CCPM. Completion of IADL tasks including text messaging, online shopping and managing finances independently was demonstrated in both participants.

Conclusion: We describe the first-in-human experience of a minimally invasive, fully implanted, wireless, ambulatory motor neuroprosthesis using an endovascular stent-electrode array to transmit electrocorticography signals from the motor cortex for multiple command control of digital devices in two participants with flaccid upper limb paralysis.
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http://dx.doi.org/10.1136/neurintsurg-2020-016862DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7848062PMC
February 2021

In vivo feasibility of epiretinal stimulation using ultrananocrystalline diamond electrodes.

J Neural Eng 2020 08 5;17(4):045014. Epub 2020 Aug 5.

Graduate School of Biomedical Engineering, University of New South Wales, Kensington, NSW 2033, Australia. The Bionics Institute of Australia, East Melbourne, VIC 3002, Australia.

Objective: Due to their increased proximity to retinal ganglion cells (RGCs), epiretinal visual prostheses present the opportunity for eliciting phosphenes with low thresholds through direct RGC activation. This study characterised the in vivo performance of a novel prototype monolithic epiretinal prosthesis, containing Nitrogen incorporated ultrananocrystalline (N-UNCD) diamond electrodes.

Approach: A prototype implant containing up to twenty-five 120 × 120 µm N-UNCD electrodes was implanted into 16 anaesthetised cats and attached to the retina either using a single tack or via magnetic coupling with a suprachoroidally placed magnet. Multiunit responses to retinal stimulation using charge-balanced biphasic current pulses were recorded acutely in the visual cortex using a multichannel planar array. Several stimulus parameters were varied including; the stimulating electrode, stimulus polarity, phase duration, return configuration and the number of electrodes stimulated simultaneously.

Main Results: The rigid nature of the device and its form factor necessitated complex surgical procedures. Surgeries were considered successful in 10/16 animals and cortical responses to single electrode stimulation obtained in eight animals. Clinical imaging and histological outcomes showed severe retinal trauma caused by the device in situ in many instances. Cortical measures were found to significantly depend on the surgical outcomes of individual experiments, phase duration, return configuration and the number of electrodes stimulated simultaneously, but not stimulus polarity. Cortical thresholds were also found to increase over time within an experiment.

Significance: The study successfully demonstrated that an epiretinal prosthesis containing diamond electrodes could produce cortical activity with high precision, albeit only in a small number of cases. Both surgical approaches were highly challenging in terms of reliable and consistent attachment to and stabilisation against the retina, and often resulted in severe retinal trauma. There are key challenges (device form factor and attachment technique) to be resolved for such a device to progress towards clinical application, as current surgical techniques are unable to address these issues.
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http://dx.doi.org/10.1088/1741-2552/aba560DOI Listing
August 2020

Critical slowing down as a biomarker for seizure susceptibility.

Nat Commun 2020 05 1;11(1):2172. Epub 2020 May 1.

Seer Medical, Melbourne, Australia.

The human brain has the capacity to rapidly change state, and in epilepsy these state changes can be catastrophic, resulting in loss of consciousness, injury and even death. Theoretical interpretations considering the brain as a dynamical system suggest that prior to a seizure, recorded brain signals may exhibit critical slowing down, a warning signal preceding many critical transitions in dynamical systems. Using long-term intracranial electroencephalography (iEEG) recordings from fourteen patients with focal epilepsy, we monitored key signatures of critical slowing down prior to seizures. The metrics used to detect critical slowing down fluctuated over temporally long scales (hours to days), longer than would be detectable in standard clinical evaluation settings. Seizure risk was associated with a combination of these signals together with epileptiform discharges. These results provide strong validation of theoretical models and demonstrate that critical slowing down is a reliable indicator that could be used in seizure forecasting algorithms.
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http://dx.doi.org/10.1038/s41467-020-15908-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7195436PMC
May 2020

Pattern Motion Processing by MT Neurons.

Front Neural Circuits 2019 21;13:43. Epub 2019 Jun 21.

National Vision Research Institute, Australian College of Optometry, Carlton, VIC, Australia.

Based on stimulation with plaid patterns, neurons in the Middle Temporal (MT) area of primate visual cortex are divided into two types: pattern and component cells. The prevailing theory suggests that pattern selectivity results from the summation of the outputs of component cells as part of a hierarchical visual pathway. We present a computational model of the visual pathway from primary visual cortex (V1) to MT that suggests an alternate model where the progression from component to pattern selectivity is not required. Using standard orientation-selective V1 cells, end-stopped V1 cells, and V1 cells with extra-classical receptive fields (RFs) as inputs to MT, the model shows that the degree of pattern or component selectivity in MT could arise from the relative strengths of the three V1 input types. Dominance of end-stopped V1 neurons in the model leads to pattern selectivity in MT, while dominance of V1 cells with extra-classical RFs result in component selectivity. This model may assist in designing experiments to further understand motion processing mechanisms in primate MT.
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http://dx.doi.org/10.3389/fncir.2019.00043DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6598444PMC
January 2020

Computational Neural Modeling of Auditory Cortical Receptive Fields.

Front Comput Neurosci 2019 24;13:28. Epub 2019 May 24.

NeuroEngineering Laboratory, Department of Biomedical Engineering, University of Melbourne, Parkville, VIC, Australia.

Previous studies have shown that the auditory cortex can enhance the perception of behaviorally important sounds in the presence of background noise, but the mechanisms by which it does this are not yet elucidated. Rapid plasticity of spectrotemporal receptive fields (STRFs) in the primary (A1) cortical neurons is observed during behavioral tasks that require discrimination of particular sounds. This rapid task-related change is believed to be one of the processing strategies utilized by the auditory cortex to selectively attend to one stream of sound in the presence of mixed sounds. However, the mechanism by which the brain evokes this rapid plasticity in the auditory cortex remains unclear. This paper uses a neural network model to investigate how synaptic transmission within the cortical neuron network can change the receptive fields of individual neurons. A sound signal was used as input to a model of the cochlea and auditory periphery, which activated or inhibited integrate-and-fire neuron models to represent networks in the primary auditory cortex. Each neuron in the network was tuned to a different frequency. All neurons were interconnected with excitatory or inhibitory synapses of varying strengths. Action potentials in one of the model neurons were used to calculate the receptive field using reverse correlation. The results were directly compared to previously recorded electrophysiological data from ferrets performing behavioral tasks that require discrimination of particular sounds. The neural network model could reproduce complex STRFs observed experimentally through optimizing the synaptic weights in the model. The model predicts that altering synaptic drive cortical neurons and/or synaptic drive from the cochlear model to the cortical neurons can account for rapid task-related changes observed experimentally in A1 neurons. By identifying changes in the synaptic drive during behavioral tasks, the model provides insights into the neural mechanisms utilized by the auditory cortex to enhance the perception of behaviorally salient sounds.
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http://dx.doi.org/10.3389/fncom.2019.00028DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6543553PMC
May 2019

Predictive Coding with Neural Transmission Delays: A Real-Time Temporal Alignment Hypothesis.

eNeuro 2019 Mar/Apr;6(2). Epub 2019 May 7.

NeuroEngineering Laboratory, Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria 3010, Australia.

Hierarchical predictive coding is an influential model of cortical organization, in which sequential hierarchical levels are connected by backward connections carrying predictions, as well as forward connections carrying prediction errors. To date, however, predictive coding models have largely neglected to take into account that neural transmission itself takes time. For a time-varying stimulus, such as a moving object, this means that backward predictions become misaligned with new sensory input. We present an extended model implementing both forward and backward extrapolation mechanisms that realigns backward predictions to minimize prediction error. This realignment has the consequence that neural representations across all hierarchical levels become aligned in real time. Using visual motion as an example, we show that the model is neurally plausible, that it is consistent with evidence of extrapolation mechanisms throughout the visual hierarchy, that it predicts several known motion-position illusions in human observers, and that it provides a solution to the temporal binding problem.
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http://dx.doi.org/10.1523/ENEURO.0412-18.2019DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6506824PMC
February 2020

Toward a Biologically Plausible Model of LGN-V1 Pathways Based on Efficient Coding.

Front Neural Circuits 2019 14;13:13. Epub 2019 Mar 14.

Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia.

Increasing evidence supports the hypothesis that the visual system employs a sparse code to represent visual stimuli, where information is encoded in an efficient way by a small population of cells that respond to sensory input at a given time. This includes simple cells in primary visual cortex (V1), which are defined by their linear spatial integration of visual stimuli. Various models of sparse coding have been proposed to explain physiological phenomena observed in simple cells. However, these models have usually made the simplifying assumption that inputs to simple cells already incorporate linear spatial summation. This overlooks the fact that these inputs are known to have strong non-linearities such the separation of ON and OFF pathways, or separation of excitatory and inhibitory neurons. Consequently these models ignore a range of important experimental phenomena that are related to the emergence of linear spatial summation from non-linear inputs, such as segregation of ON and OFF sub-regions of simple cell receptive fields, the push-pull effect of excitation and inhibition, and phase-reversed cortico-thalamic feedback. Here, we demonstrate that a two-layer model of the visual pathway from the lateral geniculate nucleus to V1 that incorporates these biological constraints on the neural circuits and is based on sparse coding can account for the emergence of these experimental phenomena, diverse shapes of receptive fields and contrast invariance of orientation tuning of simple cells when the model is trained on natural images. The model suggests that sparse coding can be implemented by the V1 simple cells using neural circuits with a simple biologically plausible architecture.
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http://dx.doi.org/10.3389/fncir.2019.00013DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6427952PMC
January 2020

Global activity shaping strategies for a retinal implant.

J Neural Eng 2019 04 13;16(2):026008. Epub 2018 Nov 13.

Department of Biomedical Engineering, The University of Melbourne, Parkville, Victoria, Australia.

Objective: Retinal prostheses provide visual perception via electrical stimulation of the retina using an implanted array of electrodes. The retinal activation resulting from each electrode is not point-like; instead each electrode introduces a spread of retinal activation that may overlap with activations from other electrodes. With most conventional stimulation strategies this overlap leads to image blur. Here we propose a 'shaping' algorithm that uses multiple electrodes to manipulate the current between electrodes in a desired way.

Approach: We assume a forward model for the conversion of electrode strengths to retinal activation. Three alternative global shaping algorithms are developed by calculating reverse models under different assumptions: linear inversion using singular value decomposition to produce the pseudoinverse, a linearly constrained quadratic program, and a binary quadratic program to partition the target pattern. The algorithms were assessed using both the mean squared error between the resulting images and desired images, as well as their adherence to the maximum allowed electrode currents.

Main Results: Under wide activation spreads the linear inversion algorithm gave improved solutions but faced two limitations: under low-noise conditions the electrode amplitudes exceeded their set limit; the set of solutions did not include the possibility of using negative local currents to induce retinal activation. The linearly constrained quadratic program and binary quadratic program respectively addressed these problems, but required much greater computation time.

Significance: This provides a framework for improving the resolution of future retinal implants, especially those with high density electrode arrays.
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http://dx.doi.org/10.1088/1741-2552/aaf071DOI Listing
April 2019

Bistability in Hodgkin-Huxley-type equations.

Annu Int Conf IEEE Eng Med Biol Soc 2018 Jul;2018:4728-4731

We study how initial conditions of the Hodgkin-Huxley model affect the dynamics of simulated neurons. We systematically vary the amplitudes of depolarization currents in order to bring neuron dynamics to stable equilibrium. Our results demonstrate that simulated neurons can have spontaneous spiking or a silent state, depending on the initial conditions. We propose the methodology to study the circumstances under which Purkinje cells transit between hyperpolarized quiescent state (down state) and a depolarized spiking state (up state). We show that results derived using the Hodgkin-Huxley methodology should be carefully analyzed before suggesting a direct relevance to neuroprosthetic implants.
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http://dx.doi.org/10.1109/EMBC.2018.8513233DOI Listing
July 2018

Feasibility of Nitrogen Doped Ultrananocrystalline Diamond Microelectrodes for Electrophysiological Recording From Neural Tissue.

Front Bioeng Biotechnol 2018 22;6:85. Epub 2018 Jun 22.

Department of Biomedical Engineering, University of Melbourne, Melbourne, VIC, Australia.

Neural prostheses that can monitor the physiological state of a subject are becoming clinically viable through improvements in the capacity to record from neural tissue. However, a significant limitation of current devices is that it is difficult to fabricate electrode arrays that have both high channel counts and the appropriate electrical properties required for neural recordings. In earlier work, we demonstrated nitrogen doped ultrananocrystalline diamond (N-UNCD) can provide efficacious electrical stimulation of neural tissue, with high charge injection capacity, surface stability and biocompatibility. In this work, we expand on this functionality to show that N-UNCD electrodes can also record from neural tissue owing to its low electrochemical impedance. We show that N-UNCD electrodes are highly flexible in their application, with successful recordings of action potentials from single neurons in an retina preparation, as well as local field potential responses from visual cortex tissue. Key properties of N-UNCD films, combined with scalability of electrode array fabrication with custom sizes for recording or stimulation along with integration through vertical interconnects to silicon based integrated circuits, may in future form the basis for the fabrication of versatile closed-loop neural prostheses that can both record and stimulate.
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http://dx.doi.org/10.3389/fbioe.2018.00085DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6024013PMC
June 2018

Compensation for Traveling Wave Delay Through Selection of Dendritic Delays Using Spike-Timing-Dependent Plasticity in a Model of the Auditory Brainstem.

Front Comput Neurosci 2018 5;12:36. Epub 2018 Jun 5.

NeuroEngineering Laboratory, Department of Biomedical Engineering, University of Melbourne, Melbourne, VIC, Australia.

Asynchrony among synaptic inputs may prevent a neuron from responding to behaviorally relevant sensory stimuli. For example, "octopus cells" are monaural neurons in the auditory brainstem of mammals that receive input from auditory nerve fibers (ANFs) representing a broad band of sound frequencies. Octopus cells are known to respond with finely timed action potentials at the onset of sounds despite the fact that due to the traveling wave delay in the cochlea, synaptic input from the auditory nerve is temporally diffuse. This paper provides a proof of principle that the octopus cells' dendritic delay may provide compensation for this input asynchrony, and that synaptic weights may be adjusted by a spike-timing dependent plasticity (STDP) learning rule. This paper used a leaky integrate and fire model of an octopus cell modified to include a "rate threshold," a property that is known to create the appropriate onset response in octopus cells. Repeated audio click stimuli were passed to a realistic auditory nerve model which provided the synaptic input to the octopus cell model. A genetic algorithm was used to find the parameters of the STDP learning rule that reproduced the microscopically observed synaptic connectivity. With these selected parameter values it was shown that the STDP learning rule was capable of adjusting the values of a large number of input synaptic weights, creating a configuration that compensated the traveling wave delay of the cochlea.
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http://dx.doi.org/10.3389/fncom.2018.00036DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5996126PMC
June 2018

Biophysical basis of the linear electrical receptive fields of retinal ganglion cells.

J Neural Eng 2018 10 11;15(5):055001. Epub 2018 Jun 11.

NeuroEngineering Laboratory, Department of Biomedical Engineering, The University of Melbourne, Australia.

Responses of retinal ganglion cells to direct electrical stimulation have been shown experimentally to be well described by linear-nonlinear models. These models rely on the simplifying assumption that retinal ganglion cell responses to stimulation with an array of electrodes are driven by a simple linear weighted sum of stimulus current amplitudes from each electrode, known as the 'electrical receptive field'.

Objective: This paper aims to demonstrate the biophysical basis of the linear-nonlinear model and the electrical receptive field to facilitate the development of improved stimulation strategies for retinal implants.

Approach: We compare the linear-nonlinear model of subretinal electrical stimulation with a multi-layered, biophysical, volume conductor model of retinal stimulation.

Main Results: Our results show that the linear electrical receptive field of the linear-nonlinear model matches the transmembrane currents induced by electrodes (the activating function) at the site of the high-density sodium channel band with only minor discrepancies. The discrepancies are mostly eliminated by including axial current flow originating from adjacent cell compartments. Furthermore, for cells where a single linear electrical receptive field is insufficient, we show that cell responses are likely driven by multiple sites of action potential initiation with multiple distinct receptive fields, each of which can be accurately described by the activating function.

Significance: This result establishes that the biophysical basis of the electrical receptive field of the linear-nonlinear model is the superposition of transmembrane currents induced by different electrodes at and near the site of action potential initiation. Together with existing experimental support for linear-nonlinear models of electrical stimulation, this provides a firm basis for using this much simplified model to generate more optimal stimulation patterns for retinal implants.
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http://dx.doi.org/10.1088/1741-2552/aacbaaDOI Listing
October 2018

A biologically-based computational model of visual cortex that overcomes the X-junction illusion.

Neural Netw 2018 Jun 16;102:10-20. Epub 2018 Feb 16.

NeuroEngineering Laboratory, Department of Biomedical Engineering, The University of Melbourne, Parkville, Australia.

The end-points of a moving bar (intrinsic terminators) contain unambiguous information that can be used to extract the bar's correct direction of motion, regardless of the orientation of the bar. However, extrinsic terminators, formed at the intersection of two overlapping bars, can result in motion signals with conflicting directions compared to those of the intrinsic terminators. Using a computational model, we propose that interactions between form and motion information may assist neurons in the motion-specific regions of primate cortex to differentiate intrinsic from extrinsic terminators. The motion processing model has two stages. The first stage is a model of V1 complex neurons, including end-stopped neurons. The resulting first stage motion signals are transmitted to the second stage, which is a model of MT neurons. In the proposed model, MT neurons additionally receive form information from neurons in V1 that are not orientation or direction selective but respond strongly to the contrast of the stimulus. These neurons have polarity-dependent center-surround receptive fields, as found in layer 4 of V1 in primates. As the inhibitory surrounds of these neurons are less activated at the intrinsic terminators, the signals generated by the end-points of the objects are stronger than the signals from the extrinsic terminators, which are inhibited by strong suppression from the surround. Therefore, the excitatory inputs received by integration MT neurons from center-surround V1 neurons enhance the unambiguous motion signals at the intrinsic terminators, which therefore dominate over the local motion signals generated at X-junctions. The results show that, despite the inability of V1 end-stopped neurons to distinguish between the two different types of terminators, center-surround V1 neurons provide the capacity for the second stage of the model to preferentially respond to the intrinsic terminators and, therefore, predict the true directions of the crossing bars.
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http://dx.doi.org/10.1016/j.neunet.2018.02.008DOI Listing
June 2018

Minimizing activation of overlying axons with epiretinal stimulation: The role of fiber orientation and electrode configuration.

PLoS One 2018 1;13(3):e0193598. Epub 2018 Mar 1.

Department of Biomedical Engineering, The University of Melbourne, Parkville, Victoria, Australia.

Currently, a challenge in electrical stimulation of the retina with a visual prosthesis (bionic eye) is to excite only the cells lying directly under the electrode in the ganglion cell layer, while avoiding excitation of axon bundles that pass over the surface of the retina in the nerve fiber layer. Stimulation of overlying axons results in irregular visual percepts, limiting perceptual efficacy. This research explores how differences in fiber orientation between the nerve fiber layer and ganglion cell layer leads to differences in the electrical activation of the axon initial segment and axons of passage.

Approach: Axons of passage of retinal ganglion cells in the nerve fiber layer are characterized by a narrow distribution of fiber orientations, causing highly anisotropic spread of applied current. In contrast, proximal axons in the ganglion cell layer have a wider distribution of orientations. A four-layer computational model of epiretinal extracellular stimulation that captures the effect of neurite orientation in anisotropic tissue has been developed using a volume conductor model known as the cellular composite model. Simulations are conducted to investigate the interaction of neural tissue orientation, stimulating electrode configuration, and stimulation pulse duration and amplitude.

Main Results: Our model shows that simultaneous stimulation with multiple electrodes aligned with the nerve fiber layer can be used to achieve selective activation of axon initial segments rather than passing fibers. This result can be achieved while reducing required stimulus charge density and with only modest increases in the spread of activation in the ganglion cell layer, and is shown to extend to the general case of arbitrary electrode array positioning and arbitrary target volume.

Significance: These results elucidate a strategy for more targeted stimulation of retinal ganglion cells with experimentally-relevant multi-electrode geometries and achievable stimulation requirements.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0193598PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5833203PMC
June 2018

Electrical receptive fields of retinal ganglion cells: Influence of presynaptic neurons.

PLoS Comput Biol 2018 02 12;14(2):e1005997. Epub 2018 Feb 12.

National Vision Research Institute, Australian College of Optometry, Carlton, Victoria, Australia.

Implantable retinal stimulators activate surviving neurons to restore a sense of vision in people who have lost their photoreceptors through degenerative diseases. Complex spatial and temporal interactions occur in the retina during multi-electrode stimulation. Due to these complexities, most existing implants activate only a few electrodes at a time, limiting the repertoire of available stimulation patterns. Measuring the spatiotemporal interactions between electrodes and retinal cells, and incorporating them into a model may lead to improved stimulation algorithms that exploit the interactions. Here, we present a computational model that accurately predicts both the spatial and temporal nonlinear interactions of multi-electrode stimulation of rat retinal ganglion cells (RGCs). The model was verified using in vitro recordings of ON, OFF, and ON-OFF RGCs in response to subretinal multi-electrode stimulation with biphasic pulses at three stimulation frequencies (10, 20, 30 Hz). The model gives an estimate of each cell's spatiotemporal electrical receptive fields (ERFs); i.e., the pattern of stimulation leading to excitation or suppression in the neuron. All cells had excitatory ERFs and many also had suppressive sub-regions of their ERFs. We show that the nonlinearities in observed responses arise largely from activation of presynaptic interneurons. When synaptic transmission was blocked, the number of sub-regions of the ERF was reduced, usually to a single excitatory ERF. This suggests that direct cell activation can be modeled accurately by a one-dimensional model with linear interactions between electrodes, whereas indirect stimulation due to summated presynaptic responses is nonlinear.
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http://dx.doi.org/10.1371/journal.pcbi.1005997DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5825175PMC
February 2018

Predictive coding of visual object position ahead of moving objects revealed by time-resolved EEG decoding.

Neuroimage 2018 05 24;171:55-61. Epub 2017 Dec 24.

Department of Biomedical Engineering, The University of Melbourne, Australia.

Due to the delays inherent in neuronal transmission, our awareness of sensory events necessarily lags behind the occurrence of those events in the world. If the visual system did not compensate for these delays, we would consistently mislocalize moving objects behind their actual position. Anticipatory mechanisms that might compensate for these delays have been reported in animals, and such mechanisms have also been hypothesized to underlie perceptual effects in humans such as the Flash-Lag Effect. However, to date no direct physiological evidence for anticipatory mechanisms has been found in humans. Here, we apply multivariate pattern classification to time-resolved EEG data to investigate anticipatory coding of object position in humans. By comparing the time-course of neural position representation for objects in both random and predictable apparent motion, we isolated anticipatory mechanisms that could compensate for neural delays when motion trajectories were predictable. As well as revealing an early neural position representation (lag 80-90 ms) that was unaffected by the predictability of the object's trajectory, we demonstrate a second neural position representation at 140-150 ms that was distinct from the first, and that was pre-activated ahead of the moving object when it moved on a predictable trajectory. The latency advantage for predictable motion was approximately 16 ± 2 ms. To our knowledge, this provides the first direct experimental neurophysiological evidence of anticipatory coding in human vision, revealing the time-course of predictive mechanisms without using a spatial proxy for time. The results are numerically consistent with earlier animal work, and suggest that current models of spatial predictive coding in visual cortex can be effectively extended into the temporal domain.
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http://dx.doi.org/10.1016/j.neuroimage.2017.12.063DOI Listing
May 2018

Neural Responses to Multielectrode Stimulation of Healthy and Degenerate Retina.

Invest Ophthalmol Vis Sci 2017 07;58(9):3770-3784

Bionics Institute, East Melbourne, Victoria, Australia 10Department of Medical Bionics, The University of Melbourne, Victoria, Australia.

Purpose: Simultaneous stimulation of multiple retinal electrodes in normally sighted animals shows promise in improving the resolution of retinal prostheses. However, the effects of simultaneous stimulation on degenerate retinae remain unknown. Therefore, we investigated the characteristics of cortical responses to multielectrode stimulation of the degenerate retina.

Methods: Four adult cats were bilaterally implanted with retinal electrode arrays in the suprachoroidal space after unilateral adenosine triphosphate (ATP)-induced retinal photoreceptor degeneration. Functional and structural changes were characterized by using electroretinogram a-wave amplitude and optical coherence tomography. Multiunit activity was recorded from both hemispheres of the visual cortex. Responses to single- and multielectrode stimulation of the ATP-injected and fellow control eyes were characterized and compared.

Results: The retinae of ATP-injected eyes displayed structural and functional changes consistent with mid- to late-stage photoreceptor degeneration and remodeling. Responses to multielectrode stimulation of the ATP-injected eyes exhibited shortened latencies, lower saturated spike counts, and higher thresholds, compared to stimulation of the fellow control eyes. Electrical receptive field sizes were significantly larger in the ATP-injected eye than in the control eye, and positively correlated with the extent of degeneration.

Conclusions: Significant differences exist between cortical responses to stimulation of healthy and degenerate retinae. Our results highlight the importance of using a retinal degeneration model when evaluating the efficacy of novel stimulation paradigms.
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http://dx.doi.org/10.1167/iovs.16-21290DOI Listing
July 2017

A computational model of orientation-dependent activation of retinal ganglion cells.

Annu Int Conf IEEE Eng Med Biol Soc 2016 Aug;2016:5447-5450

Currently, a challenge in electrical stimulation for epiretinal prostheses is the avoidance of stimulation of axons of passage in the nerve fiber layer that originate from distant regions of the ganglion cell layer. A computational model of extracellular stimulation that captures the effect of neurite orientation in anisotropic tissue is developed using a modified version of the standard volume conductor model, known as the cellular composite model, embedded in a four layer model of the retina. Simulations are conducted to investigate the interaction of neural tissue orientation, electrode placement, and stimulation pulse duration and amplitude. Using appropriate multiple electrode configurations and higher frequency stimulation, preferential activation of the axon initial segment is shown to be possible for a range of realistic electrode-retina separation distances. These results establish a quantitative relationship between the time-course of stimulation and physical properties of the tissue, such as fiber orientation.
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http://dx.doi.org/10.1109/EMBC.2016.7591959DOI Listing
August 2016

Feasibility of a chronic, minimally invasive endovascular neural interface.

Annu Int Conf IEEE Eng Med Biol Soc 2016 Aug;2016:4455-4458

Development of a neural interface that can be implanted without risky, open brain surgery will increase the safety and viability of chronic neural recording arrays. We have developed a minimally invasive surgical procedure and an endovascular electrode-array that can be delivered to overlie the cortex through blood vessels. Here, we describe feasibility of the endovascular interface through electrode viability, recording potential and safety. Electrochemical impedance spectroscopy demonstrated that electrode impedance was stable over 91 days and low frequency phase could be used to infer electrode incorporation into the vessel wall. Baseline neural recording were used to identify the maximum bandwidth of the neural interface, which remained stable around 193 Hz for six months. Cross-sectional areas of the implanted vessels were non-destructively measured using the Australian Synchrotron. There was no case of occlusion observed in any of the implanted animals. This work demonstrates the feasibility of an endovascular neural interface to safely and efficaciously record neural information over a chronic time course.
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http://dx.doi.org/10.1109/EMBC.2016.7591716DOI Listing
August 2016

Diamond Devices for High Acuity Prosthetic Vision.

Adv Biosyst 2017 Feb 5;1(1-2):e1600003. Epub 2016 Dec 5.

School of Physics, University of Melbourne, Victoria, 3010, Australia.

Retinal implants restore a sense of vision, for a growing number of users worldwide. Nevertheless, visual acuities provided by the current generation of devices are low. The quantity of information transferable to the retina using existing implant technologies is limited, far below receptor cells' capabilities. Many agree that increasing the information density deliverable by a retinal prosthesis requires devices with stimulation electrodes that are both dense and numerous. This work describes a new generation of retinal prostheses capable of upscaling the information density conveyable to the retina. Centered on engineered diamond materials, the implant is very well tolerated and long-term stable in the eye's unique physiological environment and capable of delivering highly versatile stimulation waveforms - both key attributes in providing useful vision. Delivery of high-density information, close to the retina with the flexibility to alter stimulation parameters in situ provides the best chance for success in providing high acuity prosthetic vision.
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http://dx.doi.org/10.1002/adbi.201600003DOI Listing
February 2017

Single-compartment models of retinal ganglion cells with different electrophysiologies.

Network 2017 ;28(2-4):74-93

a Department of Biomedical Engineering , The University of Melbourne , Melbourne , Australia.

There are more than 15 different types of retinal ganglion cells (RGCs) in the mammalian retina. To model responses of RGCs to electrical stimulation, we use single-compartment Hodgkin-Huxley-type models and run simulations in the Neuron environment. We use our recently published in vitro data of different morphological cell types to constrain the model, and study the effects of electrophysiology on the cell responses separately from the effects of morphology. We find simple models that can match the spike patterns of different types of RGCs. These models, with different input-output properties, may be used in a network to study retinal network dynamics and interactions.
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http://dx.doi.org/10.1080/0954898X.2018.1455993DOI Listing
January 2019

Prediction of cortical responses to simultaneous electrical stimulation of the retina.

J Neural Eng 2017 02 30;14(1):016006. Epub 2016 Nov 30.

NeuroEngineering Laboratory, Department of Electrical & Electronic Engineering, The University of Melbourne, VIC 3010, Australia. National ICT Australia, Victoria Research Lab, The University of Melbourne, VIC 3010, Australia. Bionics Institute, 384-388 Albert St, East Melbourne, VIC 3002, Australia.

Objective: Simultaneous electrical stimulation of multiple electrodes has shown promise in diversifying the responses that can be elicited by retinal prostheses compared to interleaved single electrode stimulation. However, the effects of interactions between electrodes are not well understood and clinical trials with simultaneous stimulation have produced inconsistent results. We investigated the effects of multiple electrode stimulation of the retina by developing a model of cortical responses to retinal stimulation.

Approach: Electrical stimuli consisting of temporally sparse, biphasic current pulses, with amplitudes sampled from a bi-dimensional Gaussian distribution, were simultaneously delivered to the retina across a 42-channel electrode array implanted in the suprachoroidal space of anesthetized cats. Visual cortex activity was recorded using penetrating microelectrode arrays. These data were used to identify a linear-nonlinear model of cortical responses to retinal stimulation. The ability of the model to generalize was tested by predicting responses to non-white patterned stimuli.

Main Results: The model accurately predicted two cortical activity measures: multi-unit neural responses and evoked potential responses to white noise stimuli. The model also provides information about electrical receptive fields, including the relative effects of each stimulating electrode on every recording site.

Significance: We have demonstrated a simple model that accurately describes cortical responses to simultaneous stimulation of a suprachoroidal retinal prosthesis. Overall, our results demonstrate that cortical responses to simultaneous multi-electrode stimulation of the retina are repeatable and predictable, and that interactions between electrodes during simultaneous stimulation are predominantly linear. The model shows promise for determining optimal stimulation paradigms for exploiting interactions between electrodes to shape neural activity, thereby improving outcomes for patients with retinal prostheses.
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http://dx.doi.org/10.1088/1741-2560/14/1/016006DOI Listing
February 2017

An integrated model of pitch perception incorporating place and temporal pitch codes with application to cochlear implant research.

Hear Res 2017 02 12;344:135-147. Epub 2016 Nov 12.

NeuroEngineering Laboratory, Dept. of Electrical & Electronic Engineering, University of Melbourne, Australia; Centre for Neural Engineering, University of Melbourne, Australia; The Bionics Institute, East Melbourne, Australia.

Although the neural mechanisms underlying pitch perception are not yet fully understood, there is general agreement that place and temporal representations of pitch are both used by the auditory system. This paper describes a neural network model of pitch perception that integrates both codes of pitch and explores the contributions of, and the interactions between, the two representations in simulated pitch ranking trials in normal and cochlear implant hearing. The model can replicate various psychophysical observations including the perception of the missing fundamental pitch and sensitivity to pitch interval sizes. As a case study, the model was used to investigate the efficiency of pitch perception cues in a novel sound processing scheme, Stimulation based on Auditory Modelling (SAM), that aims to improve pitch perception in cochlear implant hearing. Results showed that enhancement of the pitch perception cues would lead to better pitch ranking scores in the integrated model only if the place and temporal pitch cues were consistent.
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http://dx.doi.org/10.1016/j.heares.2016.11.005DOI Listing
February 2017

A Possible Role for End-Stopped V1 Neurons in the Perception of Motion: A Computational Model.

PLoS One 2016 14;11(10):e0164813. Epub 2016 Oct 14.

NeuroEngineering Laboratory, Dept Electrical & Electronic Engineering, The University of Melbourne, Parkville, VIC 3010, Australia.

We present a model of the early stages of processing in the visual cortex, in particular V1 and MT, to investigate the potential role of end-stopped V1 neurons in solving the aperture problem. A hierarchical network is used in which the incoming motion signals provided by complex V1 neurons and end-stopped V1 neurons proceed to MT neurons at the next stage. MT neurons are categorized into two types based on their function: integration and segmentation. The role of integration neurons is to propagate unambiguous motion signals arriving from those V1 neurons that emphasize object terminators (e.g. corners). Segmentation neurons detect the discontinuities in the input stimulus to control the activity of integration neurons. Although the activity of the complex V1 neurons at the terminators of the object accurately represents the direction of the motion, their level of activity is less than the activity of the neurons along the edges. Therefore, a model incorporating end-stopped neurons is essential to suppress ambiguous motion signals along the edges of the stimulus. It is shown that the unambiguous motion signals at terminators propagate over the rest of the object to achieve an accurate representation of motion.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0164813PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5065146PMC
May 2017