Publications by authors named "Tatiana Kameneva"

40 Publications

Response of primary auditory neurons to stimulation with infrared light in vitro.

J Neural Eng 2021 Mar 16;18(4):046003. Epub 2021 Mar 16.

Faculty of Science, Engineering and Technology, Swinburne University of Technology, John Street, Hawthorn VIC 3122, Australia.

Objective: Infrared light can be used to modulate the activity of neuronal cells through thermally-evoked capacitive currents and thermosensitive ion channel modulation. The infrared power threshold for action potentials has previously been found to be far lower in the in vivo cochlea when compared with other neuronal targets, implicating spiral ganglion neurons (SGNs) as a potential target for infrared auditory prostheses. However, conflicting experimental evidence suggests that this low threshold may arise from an intermediary mechanism other than direct SGN stimulation, potentially involving residual hair cell activity.

Approach: Patch-clamp recordings from cultured SGNs were used to explicitly quantify the capacitive and ion channel currents in an environment devoid of hair cells. Neurons were irradiated by a 1870 nm laser with pulse durations of 0.2-5.0 ms and powers up to 1.5 W. A Hodgkin-Huxley-type model was established by first characterising the voltage dependent currents, and then incorporating laser-evoked currents separated into temperature-dependent and temperature-gradient-dependent components. This model was found to accurately simulate neuronal responses and allowed the results to be extrapolated to stimulation parameter spaces not accessible during this study.

Main Results: The previously-reported low in vivo SGN stimulation threshold was not observed, and only subthreshold depolarisation was achieved, even at high light exposures. Extrapolating these results with our Hodgkin-Huxley-type model predicts an action potential threshold which does not deviate significantly from other neuronal types.

Significance: This suggests that the low-threshold response that is commonly reported in vivo may arise from an alternative mechanism, and calls into question the potential usefulness of the effect for auditory prostheses. The step-wise approach to modelling optically-evoked currents described here may prove useful for analysing a wider range of cell types where capacitive currents and conductance modulation are dominant.
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http://dx.doi.org/10.1088/1741-2552/abe7b8DOI Listing
March 2021

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

Analysis of extracellular spike waveforms and associated receptive fields of neurons in cat primary visual cortex.

J Physiol 2021 Jan 27. Epub 2021 Jan 27.

National Vision Research Institute, Australian College of Optometry, Carlton, Victoria, 3053, Australia.

Key Points: Extracellular spikes recorded in the visual cortex (Area 17/18, V1) are commonly classified into either regular-spiking (RS) or fast-spiking (FS). Using multi-electrode arrays positioned in cat V1 and a broadband stimulus, we show that there is also a distinct class with positive-spiking (PS) waveforms. PS units were associated mainly with non-oriented receptive fields while RS and FS units had orientation-selective receptive fields. We suggest that PS units are recordings of axons originating from the thalamus. This conclusion was reinforced by our finding that we could record PS units after cortical silencing, but not record RS and FS units. The importance of our findings is that we were able to correlate spike shapes with receptive field characteristics with high precision using multi-electrode extracellular recording techniques. This allows considerable increases in the amount of information that can be extracted from future cortical experiments.

Abstract: Extracellular spike waveforms from recordings in the visual cortex have been classified into either regular-spiking (RS) or fast-spiking (FS) units. While both these types of spike waveforms are negative-dominant, we show that there are also distinct classes of spike waveforms in visual Area 17/18 (V1) of anaesthetised cats with positive-dominant waveforms, which are not regularly reported. The spatial receptive fields (RFs) of these different spike waveform types were estimated, which objectively revealed the existence of oriented and non-oriented RFs. We found that units with positive-dominant spikes, which have been associated with recordings from axons in the literature, had mostly non-oriented RFs (84%), which are similar to the centre-surround RFs observed in the dorsal lateral geniculate nucleus (dLGN). Thus, we hypothesise that these positive-dominant waveforms may be recordings from dLGN afferents. We recorded from V1 before and after the application of muscimol (a cortical silencer) and found that the positive-dominant spikes (PS) remained while the RS and FS cells did not. We also noted that the PS units had spiking characteristics normally associated with dLGN units (i.e. higher response spike rates, lower response latencies and higher proportion of burst spikes). Our findings show quantitatively that it is possible to correlate the RF properties of cortical neurons with particular spike waveforms. This has implications for how extracellular recordings should be interpreted and complex experiments can now be contemplated that would have been very challenging previously, such as assessing the feedforward connectivity between brain areas in the same location of cortical tissue.
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http://dx.doi.org/10.1113/JP280844DOI Listing
January 2021

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

Critical Review of Transcutaneous Vagus Nerve Stimulation: Challenges for Translation to Clinical Practice.

Front Neurosci 2020 28;14:284. Epub 2020 Apr 28.

Faculty of Science, Engineering and Technology, Swinburne University of Technology, Hawthorn, VIC, Australia.

Several studies have illustrated that transcutaneous vagus nerve stimulation (tVNS) can elicit therapeutic effects that are similar to those produced by its invasive counterpart, vagus nerve stimulation (VNS). VNS is an FDA-approved therapy for the treatment of both depression and epilepsy, but it is limited to the management of more severe, intervention-resistant cases as a second or third-line treatment option due to perioperative risks involved with device implantation. In contrast, tVNS is a non-invasive technique that involves the application of electrical currents through surface electrodes at select locations, most commonly targeting the auricular branch of the vagus nerve (ABVN) and the cervical branch of the vagus nerve in the neck. Although it has been shown that tVNS elicits hypo- and hyperactivation in various regions of the brain associated with anxiety and mood regulation, the mechanism of action and influence of stimulation parameters on clinical outcomes remains predominantly hypothetical. Suppositions are largely based on correlations between the neurobiology of the vagus nerve and its effects on neural activity. However, tVNS has also been investigated for several other disorders, including tinnitus, migraine and pain, by targeting the vagus nerve at sites in both the ear and the neck. As most of the described methods differ in the parameters and protocols applied, there is currently no firm evidence on the optimal location for tVNS or the stimulation parameters that provide the greatest therapeutic effects for a specific condition. This review presents the current status of tVNS with a focus on stimulation parameters, stimulation sites, and available devices. For tVNS to reach its full potential as a non-invasive and clinically relevant therapy, it is imperative that systematic studies be undertaken to reveal the mechanism of action and optimal stimulation modalities.
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http://dx.doi.org/10.3389/fnins.2020.00284DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7199464PMC
April 2020

Thermal damage threshold of neurons during infrared stimulation.

Biomed Opt Express 2020 Apr 27;11(4):2224-2234. Epub 2020 Mar 27.

Faculty of Science, Engineering and Technology, Swinburne University of Technology, John Street, Hawthorn, VIC 3122, Australia.

In infrared neural stimulation (INS), laser-evoked thermal transients are used to generate small depolarising currents in neurons. The laser exposure poses a moderate risk of thermal damage to the target neuron. Indeed, exogenous methods of neural stimulation often place the target neurons under stressful non-physiological conditions, which can hinder ordinary neuronal function and hasten cell death. Therefore, quantifying the exposure-dependent probability of neuronal damage is essential for identifying safe operating limits of INS and other interventions for therapeutic and prosthetic use. Using patch-clamp recordings in isolated spiral ganglion neurons, we describe a method for determining the dose-dependent damage probabilities of individual neurons in response to both acute and cumulative infrared exposure parameters based on changes in injection current. The results identify a local thermal damage threshold at approximately 60 C, which is in keeping with previous literature and supports the claim that damage during INS is a purely thermal phenomenon. In principle this method can be applied to any potentially injurious stimuli, allowing for the calculation of a wide range of dose-dependent neural damage probabilities. Unlike histological analyses, the technique is well-suited to quantifying gradual neuronal damage, and critical threshold behaviour is not required.
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http://dx.doi.org/10.1364/BOE.383165DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7173919PMC
April 2020

Deep reinforcement learning for task-based feature learning in prosthetic vision.

Annu Int Conf IEEE Eng Med Biol Soc 2019 Jul;2019:2809-2812

Developing hand-crafted visual features to enhance perception with prosthetic vision devices can often miss important aspects of a given task. Retinal implants suffer from the need to create low-dimensional features for elaborate tasks such as navigation in complex environments. Using Deep Reinforcement Learning (DRL), visual features are learnt through task-based simulations that remove the ambiguity of inferring the visual information most crucial to a specific activity. Learning task-based features ensures that the visual information is salient to the tasks an implant recipient may be undertaking and eliminates potentially redundant features. In this paper, we focus specifically on basic orientation and mobility, and the methods for feature learning and visualisation in structured 3D environments. We propose a new model for learning visual features through task-based simulations and show that learnt features can be transferred directly to real RGB-D images. We demonstrate this new scalable approach for feature learning in simulation and open the possibility for more complex simulations of more complex tasks in the future.
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http://dx.doi.org/10.1109/EMBC.2019.8856541DOI Listing
July 2019

Combined optogenetic and electrical stimulation of auditory neurons increases effective stimulation frequency-an in vitro study.

J Neural Eng 2020 02 19;17(1):016069. Epub 2020 Feb 19.

ARC Training Centre in Biodevices, Swinburne University of Technology, Hawthorn, VIC 3122, Australia.

Objective: The performance of neuroprostheses, including cochlear and retinal implants, is currently constrained by the spatial resolution of electrical stimulation. Optogenetics has improved the spatial control of neurons in vivo but lacks the fast-temporal dynamics required for auditory and retinal signalling. The objective of this study is to demonstrate that combining optical and electrical stimulation in vitro could address some of the limitations associated with each of the stimulus modes when used independently.

Approach: The response of murine auditory neurons expressing ChR2-H134 to combined optical and electrical stimulation was characterised using whole cell patch clamp electrophysiology.

Main Results: Optogenetic costimulation produces a three-fold increase in peak firing rate compared to optical stimulation alone and allows spikes to be evoked by combined subthreshold optical and electrical inputs. Subthreshold optical depolarisation also facilitated spiking in auditory neurons for periods of up to 30 ms without evidence of wide-scale Na inactivation.

Significance: These findings may contribute to the development of spatially and temporally selective optogenetic-based neuroprosthetics and complement recent developments in 'fast opsins'.
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http://dx.doi.org/10.1088/1741-2552/ab6a68DOI Listing
February 2020

Acute effects of resonance frequency breathing on cardiovascular regulation.

Physiol Rep 2019 11;7(22):e14295

School of Health Sciences, Department of Health and Medical Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia.

Acute slow breathing may have beneficial effects on cardiovascular regulation by affecting hemodynamics and the autonomic nervous system. Whether breathing at the resonance frequency (RF), a breathing rate that maximizes heart rate oscillations, induces differential effects to that of slow breathing is unknown. We compared the acute effects of breathing at either RF and RF + 1 breaths per minute on muscle sympathetic nervous activity (MSNA) and baroreflex function. Ten healthy men underwent MSNA, blood pressure (BP), and heart rate (HR) recordings while breathing for 10 min at their spontaneous breathing (SB) rate followed by 10 min at both RF and RF + 1 randomly assigned and separated by a 10-min recovery. Breathing at either RF or RF + 1 induced similar changes in HR and HR variability, with increased low frequency and decreased high frequency oscillations (p < .001 for both). Both respiration rates decreased MSNA (-5.6 and -7.3 bursts per min for RF and RF + 1 p < .05), with the sympathetic bursts occurring more often during mid-inspiration to early expiration (+57% and + 80%) and longer periods of silence between bursts were seen (p < .05 for RF + 1). Systolic BP was decreased only during RF (-4.6 mmHg, p < .05) but the decrease did not differ to that seen during RF + 1 (-3.1 mmHg). The sympathetic baroreflex function remained unchanged at either breathing rates. The slope of the cardiac baroreflex function was unaltered but the cardiac baroreflex efficiency was improved during both RF and RF + 1. Acute breathing at either RF or RF + 1 has similar hemodynamic and sympatho-inhibitory effects in healthy men.
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http://dx.doi.org/10.14814/phy2.14295DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6882954PMC
November 2019

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

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

Differences between morphological and electrophysiological retinal ganglion cell classes.

Annu Int Conf IEEE Eng Med Biol Soc 2018 Jul;2018:3056-3059

Retinal prostheses work by delivering electrical pulses to the surviving retinal neurons. A pattern of electrical stimulation can generate a perception of vision in blind patients. To improve efficacy of retinal implants, it is important to understand how different classes of retinal neurons respond to electrical stimulation and if a classification can be made based on the electrophysiological properties of neurons. We use previously recorded patch clamp data from retinal ganglion cells classified into morphological classes (A,B,C, D) and functional types (ON, OFF, ON-OFF). We use a machine learning technique to separate data based on the recorded electrophysiological parameters. Results show that the clusters discovered using the machine learning technique do not correspond to the morphological or functional classes used by neuroscientists.
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http://dx.doi.org/10.1109/EMBC.2018.8512899DOI Listing
July 2018

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

Neuroprostheses: method to evaluate the information content of stimulation strategies.

Annu Int Conf IEEE Eng Med Biol Soc 2018 Jul;2018:4724-4727

We propose a framework to evaluate the information content of different stimulation strategies used in neuroprosthetic implants. We analyze the responses of retinal ganglion cells to electrical stimulation using an information theory framework. This methodology allows us to calculate the information content by looking at the consistency of neural responses generated across multiple repetitions of the same stimulation protocol.
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http://dx.doi.org/10.1109/EMBC.2018.8513122DOI Listing
July 2018

Modeling experimental recordings of vagal afferent signaling of intestinal inflammation for neuromodulation.

J Neural Eng 2018 10 10;15(5):056032. Epub 2018 Aug 10.

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

Objective: Artificial modulation of peripheral nerve signals (neuromodulation) by electrical stimulation is an innovation with potential to develop treatments that replace or supplement drugs. One function of the nervous system that can be exploited by neuromodulation is regulation of disease intensity. Optimal interfacing of devices with the nervous system requires suitable models of peripheral nerve systems so that closed-loop control can be utilized for therapeutic benefit.

Approach: We use physiological data to model afferent signaling in the vagus nerve that carries information about inflammation in the small intestine to the brain.

Main Results: The vagal nerve signaling system is distributed and complex; however, we propose a class of reductive models using a state-space formalism that can be tuned in a patient-specific manner.

Significance: These models provide excellent fits to a large range of nerve recording data but are computationally simple enough for feedback control in implantable neuromodulation devices.
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http://dx.doi.org/10.1088/1741-2552/aad96dDOI Listing
October 2018

In vitro assessment of the differences in retinal ganglion cell responses to intra- and extracellular electrical stimulation.

J Neural Eng 2018 08 8;15(4):046022. Epub 2018 May 8.

National Vision Research Institute, Australian College of Optometry, Melbourne, Australia.

Objective: To compare responses of retinal ganglion cells (RGCs) to intracellular and extracellular electrical stimulation of varying frequency and amplitude.

Approach: In vitro patch clamp was used to record the responses of RGCs to sinusoidal current stimulation of varying frequency and amplitude. The results were simulated using the Neuron software package.

Main Results: The stimulation frequency yielding the greatest response was higher for extracellular stimulation compared to intracellular stimulation in the same cells (256 Hz versus 64 Hz). In fact, at the high end of the frequency range, where extracellular stimulation was highly efficacious, no responses could be generated using intracellular stimulation. A region in the amplitude-frequency stimulation space was identified where OFF-RGCs could be preferentially stimulated over ON-RGCs. We found that the inability of RGCs to respond at high frequencies of intracellular stimulation is likely the result of the axon acting as a low pass filter.

Significance: There is no direct translation of the results obtained with intracellular stimulation to those that employ extracellular stimulation.
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http://dx.doi.org/10.1088/1741-2552/aac2f7DOI Listing
August 2018

Upper stimulation threshold for retinal ganglion cell activation.

J Neural Eng 2018 08 4;15(4):046012. Epub 2018 Apr 4.

National Vision Research Institute, Australian College of Optometry, Australia. Department of Biomedical Engineering, The University of Melbourne, Australia.

Objective: The existence of an upper threshold in electrically stimulated retinal ganglion cells (RGCs) is of interest because of its relevance to the development of visual prosthetic devices, which are designed to restore partial sight to blind patients. The upper threshold is defined as the stimulation level above which no action potentials (direct spikes) can be elicited in electrically stimulated retina.

Approach: We collected and analyzed in vitro recordings from rat RGCs in response to extracellular biphasic (anodic-cathodic) pulse stimulation of varying amplitudes and pulse durations. Such responses were also simulated using a multicompartment model.

Main Results: We identified the individual cell variability in response to stimulation and the phenomenon known as upper threshold in all but one of the recorded cells (n  =  20/21). We found that the latencies of spike responses relative to stimulus amplitude had a characteristic U-shape. In silico, we showed that the upper threshold phenomenon was observed only in the soma. For all tested biphasic pulse durations, electrode positions, and pulse amplitudes above lower threshold, a propagating action potential was observed in the distal axon. For amplitudes above the somatic upper threshold, the axonal action potential back-propagated in the direction of the soma, but the soma's low level of hyperpolarization prevented action potential generation in the soma itself.

Significance: An upper threshold observed in the soma does not prevent spike conductance in the axon.
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http://dx.doi.org/10.1088/1741-2552/aabb7dDOI Listing
August 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

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

Neural mass models as a tool to investigate neural dynamics during seizures.

J Comput Neurosci 2017 Apr 19;42(2):203-215. Epub 2017 Jan 19.

Department of Medicine, St. Vincent's Hospital, The University of Melbourne, Melbourne, Australia.

Epilepsy is one of the most common neurological disorders and is characterized by recurrent seizures. We use theoretical neuroscience tools to study brain dynamics during seizures. We derive and simulate a computational model of a network of hippocampal neuronal populations. Each population within the network is based on a model that has been shown to replicate the electrophysiological dynamics observed during seizures. The results provide insights into possible mechanisms for seizure spread. We observe that epileptiform activity remains localized to a pathological region when a global connectivity parameter is less than a critical value. After establishing the critical value for seizure spread, we explored how to correct the effect by altering particular synaptic gains. The spreading of seizures is quantified using numerical methods for seizure detection. The results from this study provide a new avenue of exploration for seizure control.
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http://dx.doi.org/10.1007/s10827-017-0636-xDOI Listing
April 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

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

Frequency Responses of Rat Retinal Ganglion Cells.

PLoS One 2016 24;11(6):e0157676. Epub 2016 Jun 24.

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

There are 15-20 different types of retinal ganglion cells (RGC) in the mammalian retina, each encoding different aspects of the visual scene. The mechanism by which post-synaptic signals from the retinal network generate spikes is determined by each cell's intrinsic electrical properties. Here we investigate the frequency responses of morphologically identified rat RGCs using intracellular injection of sinusoidal current waveforms, to assess their intrinsic capabilities with minimal contributions from the retinal network. Recorded cells were classified according to their morphological characteristics (A, B, C or D-type) and their stratification (inner (i), outer (o) or bistratified) in the inner plexiform layer (IPL). Most cell types had low- or band-pass frequency responses. A2, C1 and C4o cells were band-pass with peaks of 15-30 Hz and low-pass cutoffs above 56 Hz (A2 cells) and ~42 Hz (C1 and C4o cells). A1 and C2i/o cells were low-pass with peaks of 10-15 Hz (cutoffs 19-25 Hz). Bistratified D1 and D2 cells were also low-pass with peaks of 5-10 Hz (cutoffs ~16 Hz). The least responsive cells were the B2 and C3 types (peaks: 2-5 Hz, cutoffs: 8-11 Hz). We found no difference between cells stratifying in the inner and outer IPL (i.e., ON and OFF cells) or between cells with large and small somas or dendritic fields. Intrinsic physiological properties (input resistance, spike width and sag) had little impact on frequency response at low frequencies, but account for 30-40% of response variability at frequencies >30 Hz.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0157676PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4920367PMC
July 2017

A Simple and Accurate Model to Predict Responses to Multi-electrode Stimulation in the Retina.

PLoS Comput Biol 2016 Apr 1;12(4):e1004849. Epub 2016 Apr 1.

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

Implantable electrode arrays are widely used in therapeutic stimulation of the nervous system (e.g. cochlear, retinal, and cortical implants). Currently, most neural prostheses use serial stimulation (i.e. one electrode at a time) despite this severely limiting the repertoire of stimuli that can be applied. Methods to reliably predict the outcome of multi-electrode stimulation have not been available. Here, we demonstrate that a linear-nonlinear model accurately predicts neural responses to arbitrary patterns of stimulation using in vitro recordings from single retinal ganglion cells (RGCs) stimulated with a subretinal multi-electrode array. In the model, the stimulus is projected onto a low-dimensional subspace and then undergoes a nonlinear transformation to produce an estimate of spiking probability. The low-dimensional subspace is estimated using principal components analysis, which gives the neuron's electrical receptive field (ERF), i.e. the electrodes to which the neuron is most sensitive. Our model suggests that stimulation proportional to the ERF yields a higher efficacy given a fixed amount of power when compared to equal amplitude stimulation on up to three electrodes. We find that the model captures the responses of all the cells recorded in the study, suggesting that it will generalize to most cell types in the retina. The model is computationally efficient to evaluate and, therefore, appropriate for future real-time applications including stimulation strategies that make use of recorded neural activity to improve the stimulation strategy.
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http://dx.doi.org/10.1371/journal.pcbi.1004849DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4818105PMC
April 2016

Spectral distribution of local field potential responses to electrical stimulation of the retina.

J Neural Eng 2016 06 30;13(3):036003. Epub 2016 Mar 30.

NeuroEngineering Laboratory, Electrical and Electronic Engineering, The University of Melbourne, VIC 3010, Australia. National Vision Research Institute, Australian College of Optometry, Carlton, VIC 3053, Australia.

Objective: Different frequency bands of the local field potential (LFP) have been shown to reflect neuronal activity occurring at varying cortical scales. As such, recordings of the LFP may offer a novel way to test the efficacy of neural prostheses and allow improvement of stimulation strategies via neural feedback. Here we use LFP measurements from visual cortex to characterize neural responses to electrical stimulation of the retina. We aim to show that the LFP is a viable signal that contains sufficient information to optimize the performance of sensory neural prostheses.

Approach: Clinically relevant electrode arrays were implanted in the suprachoroidal space of one eye in four felines. LFPs were simultaneously recorded in response to stimulation of individual electrodes using penetrating microelectrode arrays from the visual cortex. The frequency response of each electrode was extracted using multi-taper spectral analysis and the uniqueness of the responses was determined via a linear decoder.

Main Results: We found that cortical LFPs are reliably modulated by electrical stimulation of the retina and that the responses are spatially localized. We further characterized the spectral distribution of responses, with maximum information being contained in the low and high gamma bands. Finally, we found that LFP responses are unique to a large range of stimulus parameters (∼40) with a maximum conveyable information rate of 6.1 bits.

Significance: These results show that the LFP can be used to validate responses to electrical stimulation of the retina and we provide the first steps towards using these responses to provide more efficacious stimulation strategies.
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http://dx.doi.org/10.1088/1741-2560/13/3/036003DOI Listing
June 2016

Electrical activity of ON and OFF retinal ganglion cells: a modelling study.

J Neural Eng 2016 Apr 23;13(2):025005. Epub 2016 Feb 23.

Graduate School of Biomedical Engineering, UNSW Australia, Sydney, NSW 2052, Australia.

Objective: Retinal ganglion cells (RGCs) demonstrate a large range of variation in their ionic channel properties and morphologies. Cell-specific properties are responsible for the unique way RGCs process synaptic inputs, as well as artificial electrical signals such as that from a visual prosthesis. A cell-specific computational modelling approach allows us to examine the functional significance of regional membrane channel expression and cell morphology.

Approach: In this study, an existing RGC ionic model was extended by including a hyperpolarization activated non-selective cationic current as well as a T-type calcium current identified in recent experimental findings. Biophysically-defined model parameters were simultaneously optimized against multiple experimental recordings from ON and OFF RGCs.

Main Results: With well-defined cell-specific model parameters and the incorporation of detailed cell morphologies, these models were able to closely reconstruct and predict ON and OFF RGC response properties recorded experimentally.

Significance: The resulting models were used to study the contribution of different ion channel properties and spatial structure of neurons to RGC activation. The techniques of this study are generally applicable to other excitable cell models, increasing the utility of theoretical models in accurately predicting the response of real biological neurons.
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http://dx.doi.org/10.1088/1741-2560/13/2/025005DOI Listing
April 2016

The effects of temperature changes on retinal ganglion cell responses to electrical stimulation.

Annu Int Conf IEEE Eng Med Biol Soc 2015 ;2015:7506-9

Little is known about how the retina's response to electrical stimulation is modified by temperatures. In vitro experiments are often used to inform in vivo studies, hence it is important to understand what changes occur at physiological temperature. To investigate this, we recorded from eight RGCs in vitro at three temperatures; room temperature (24°C), 30°C and 34°C. Results show that response latencies and thresholds are reduced, bursting spike rates in response to stimulation increases, and the spiking becomes more consistently locked to the stimulus at higher temperatures.
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http://dx.doi.org/10.1109/EMBC.2015.7320128DOI Listing
August 2016

Spike history neural response model.

J Comput Neurosci 2015 Jun 12;38(3):463-81. Epub 2015 Apr 12.

Department of Electrical and Electronic Engineering, University of Melbourne, Melbourne, Australia,

There is a potential for improved efficacy of neural stimulation if stimulation levels can be modified dynamically based on the responses of neural tissue in real time. A neural model is developed that describes the response of neurons to electrical stimulation and that is suitable for feedback control neuroprosthetic stimulation. Experimental data from NZ white rabbit retinae is used with a data-driven technique to model neural dynamics. The linear-nonlinear approach is adapted to incorporate spike history and to predict the neural response of ganglion cells to electrical stimulation. To validate the fitness of the model, the penalty term is calculated based on the time difference between each simulated spike and the closest spike in time in the experimentally recorded train. The proposed model is able to robustly predict experimentally observed spike trains.
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http://dx.doi.org/10.1007/s10827-015-0549-5DOI Listing
June 2015