Modeling intrinsic electrophysiology of AII amacrine cells: preliminary results

In patients who have lost their photoreceptors due to retinal degenerative diseases, it is possible to restore rudimentary vision by electrically stimulating surviving neurons. AII amacrine cells, which reside in the inner plexiform layer, split the signal from rod bipolar cells into ON and OFF cone pathways. As a result, it is of interest to develop a computational model to aid in the understanding of how these cells respond to the electrical stimulation delivered by a prosthetic implant. The aim of this work is to develop and constrain parameters in a single-compartment model of an AII amacrine cell using data from whole-cell patch clamp recordings. This model will be used to explore responses of AII amacrine cells to electrical stimulation. Single-compartment Hodgkin-Huxley-type neural models are simulated in the NEURON environment. Simulations showed successful reproduction of the potassium currentvoltage relationship and some of the spiking properties observed in vitro.

Broadband onset inhibition can suppress spectral splatter in the auditory brainstem

In vivo intracellular responses to auditory stimuli revealed that, in a particular population of cells of the ventral nucleus of the lateral lemniscus (VNLL) of rats, fast inhibition occurred before the first action potential. These experimental data were used to constrain a leaky integrate-and-fire (LIF) model of the neurons in this circuit. The post-synaptic potentials of the VNLL cell population were characterized using a method of triggered averaging. Analysis suggested that these inhibited VNLL cells produce action potentials in response to a particular magnitude of the rate of change of their membrane potential. The LIF model was modified to incorporate the VNLL cells’ distinctive action potential production mechanism. The model was used to explore the response of the population of VNLL cells to simple speech-like sounds. These sounds consisted of a simple tone modulated by a saw tooth with exponential decays, similar to glottal pulses that are the repeated impulses seen in vocalizations. It was found that the harmonic component of the sound was enhanced in the VNLL cell population when compared to a population of auditory nerve fibers. This was because the broadband onset noise, also termed spectral splatter, was suppressed by the fast onset inhibition. This mechanism has the potential to greatly improve the clarity of the representation of the harmonic content of certain kinds of natural sounds.

Spike history neural response model

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.

Feedback stimulation strategy: control of retinal ganglion cells activation

It is possible to cause a sensation of light in patients who have lost photoreceptors due to degenerative eye diseases by targeting surviving neurons with electrical stimulation by means of visual prosthetic devices. All stimulation strategies in currently used visual prostheses are open-loop, that is, the stimulation parameters do not depend on the level of activation of neurons surrounding stimulating electrodes. In this paper, we investigate a closed-loop stimulation strategy using computer simulations of previously constrained models of ON and OFF retinal ganglion cells. Using a proportional-integral-type controller we show that it is possible to control activation level of both types of retinal ganglion cells. We also demonstrate that the controller tuned for a particular combination of synaptic currents continues to work during retina degeneration when excitatory currents are reduced by 20%.

First-in-human trial of a novel suprachoroidal retinal prosthesis

Retinal visual prostheses (“bionic eyes”) have the potential to restore vision to blind or profoundly vision-impaired patients. The medical bionic technology used to design, manufacture and implant such prostheses is still in its relative infancy, with various technologies and surgical approaches being evaluated. We hypothesised that a suprachoroidal implant location (between the sclera and choroid of the eye) would provide significant surgical and safety benefits for patients, allowing them to maintain preoperative residual vision as well as gaining prosthetic vision input from the device. This report details the first-in-human Phase 1 trial to investigate the use of retinal implants in the suprachoroidal space in three human subjects with end-stage retinitis pigmentosa. The success of the suprachoroidal surgical approach and its associated safety benefits, coupled with twelve-month post-operative efficacy data, holds promise for the field of vision restoration.

Trial Registration

Clinicaltrials.gov NCT01603576

Modelling extracellular electrical stimulation: part 4. Effect of the cellular composition of neural tissue on its spatio-temporal filtering properties

OBJECTIVE

The objective of this paper is to present a concrete application of the cellular composite model for calculating the membrane potential, described in an accompanying paper.

APPROACH

A composite model that is used to determine the membrane potential for both longitudinal and transverse modes of stimulation is demonstrated.

MAIN RESULTS

Two extreme limits of the model, near-field and far-field for an electrode close to or distant from a neuron, respectively, are derived in this paper. Results for typical neural tissue are compared using the composite, near-field and far-field models as well as the standard isotropic volume conductor model. The self-consistency of the composite model, its spatial profile response and the extracellular potential time behaviour are presented. The magnitudes of the longitudinal and transverse components for different values of electrode-neurite separations are compared.

SIGNIFICANCE

The unique features of the composite model and its simplified versions can be used to accurately estimate the spatio-temporal response of neural tissue to extracellular electrical stimulation.

Improved visual performance in letter perception through edge orientation encoding in a retinal prosthesis simulation

Objective. Stimulation strategies for retinal prostheses predominately seek to directly encode image brightness values rather than edge orientations. Recent work suggests that the generation of oriented elliptical phosphenes may be possible by controlling interactions between neighboring electrodes. Based on this, we propose a novel stimulation strategy for prosthetic vision that extracts edge orientation information from the intensity image and encodes it as oriented elliptical phosphenes. We test the hypothesis that encoding edge orientation via oriented elliptical phosphenes leads to better alphabetic letter recognition than standard intensity-based encoding. Approach. We conduct a psychophysical study with simulated phosphene vision with 12 normal-sighted volunteers. The two stimulation strategies were compared with variations of letter size, electrode drop-out and spatial offsets of phosphenes. Main results. Mean letter recognition accuracy was significantly better with the new proposed stimulation strategy (65%) compared to direct grayscale encoding (47%). All examined parameters-stimulus size, phosphene dropout, and location shift-were found to influence the performance, with significant two-way interactions between phosphene dropout and stimulus size as well as between phosphene dropout and phosphene location shift. The analysis delivers a model of perception performance. Significance. Displaying available directional information to an implant user may improve their visual performance. We present a model for designing a stimulation strategy under the constraints of existing retinal prostheses that can be exploited by retinal implant developers to strategically employ oriented phosphenes.

Goal-directed control with cortical units that are gated by both top-down feedback and oscillatory coherence

The brain is able to flexibly select behaviors that adapt to both its environment and its present goals. This cognitive control is understood to occur within the hierarchy of the cortex and relies strongly on the prefrontal and premotor cortices, which sit at the top of this hierarchy. Pyramidal neurons, the principal neurons in the cortex, have been observed to exhibit much stronger responses when they receive inputs at their soma/basal dendrites that are coincident with inputs at their apical dendrites. This corresponds to inputs from both lower-order regions (feedforward) and higher-order regions (feedback), respectively. In addition to this, coherence between oscillations, such as gamma oscillations, in different neuronal groups has been proposed to modulate and route communication in the brain. In this paper, we develop a simple, but novel, neural mass model in which cortical units (or ensembles) exhibit gamma oscillations when they receive coherent oscillatory inputs from both feedforward and feedback connections. By forming these units into circuits that can perform logic operations, we identify the different ways in which operations can be initiated and manipulated by top-down feedback. We demonstrate that more sophisticated and flexible top-down control is possible when the gain of units is modulated by not only top-down feedback but by coherence between the activities of the oscillating units. With these types of units, it is possible to not only add units to, or remove units from, a higher-level unit's logic operation using top-down feedback, but also to modify the type of role that a unit plays in the operation. Finally, we explore how different network properties affect top-down control and processing in large networks. Based on this, we make predictions about the likely connectivities between certain brain regions that have been experimentally observed to be involved in goal-directed behavior and top-down attention.

Interplay of intrinsic and synaptic conductances in the generation of high-frequency oscillations in interneuronal networks with irregular spiking

High-frequency oscillations (above 30 Hz) have been observed in sensory and higher-order brain areas, and are believed to constitute a general hallmark of functional neuronal activation. Fast inhibition in interneuronal networks has been suggested as a general mechanism for the generation of high-frequency oscillations. Certain classes of interneurons exhibit subthreshold oscillations, but the effect of this intrinsic neuronal property on the population rhythm is not completely understood. We study the influence of intrinsic damped subthreshold oscillations in the emergence of collective high-frequency oscillations, and elucidate the dynamical mechanisms that underlie this phenomenon. We simulate neuronal networks composed of either Integrate-and-Fire (IF) or Generalized Integrate-and-Fire (GIF) neurons. The IF model displays purely passive subthreshold dynamics, while the GIF model exhibits subthreshold damped oscillations. Individual neurons receive inhibitory synaptic currents mediated by spiking activity in their neighbors as well as noisy synaptic bombardment, and fire irregularly at a lower rate than population frequency. We identify three factors that affect the influence of single-neuron properties on synchronization mediated by inhibition: i) the firing rate response to the noisy background input, ii) the membrane potential distribution, and iii) the shape of Inhibitory Post-Synaptic Potentials (IPSPs). For hyperpolarizing inhibition, the GIF IPSP profile (factor iii)) exhibits post-inhibitory rebound, which induces a coherent spike-mediated depolarization across cells that greatly facilitates synchronous oscillations. This effect dominates the network dynamics, hence GIF networks display stronger oscillations than IF networks. However, the restorative current in the GIF neuron lowers firing rates and narrows the membrane potential distribution (factors i) and ii), respectively), which tend to decrease synchrony. If inhibition is shunting instead of hyperpolarizing, post-inhibitory rebound is not elicited and factors i) and ii) dominate, yielding lower synchrony in GIF networks than in IF networks.

Neurons in the brain engage in collective oscillations at different frequencies. Gamma and high-gamma oscillations (30–100 Hz and higher) have been associated with cognitive functions, and are altered in psychiatric disorders such as schizophrenia and autism. Our understanding of how high-frequency oscillations are orchestrated in the brain is still limited, but it is necessary for the development of effective clinical approaches to the treatment of these disorders. Some neuron types exhibit dynamical properties that can favour synchronization. The theory of weakly coupled oscillators showed how the phase response of individual neurons can predict the patterns of phase relationships that are observed at the network level. However, neurons in vivo do not behave like regular oscillators, but fire irregularly in a regime dominated by fluctuations. Hence, which intrinsic dynamical properties matter for synchronization, and in which regime, is still an open question. Here, we show how single-cell damped subthreshold oscillations enhance synchrony in interneuronal networks by introducing a depolarizing component, mediated by post-inhibitory rebound, that is correlated among neurons due to common inhibitory input.

Multicompartment retinal ganglion cells response to high frequency bi-phasic pulse train stimulation: Simulation results

Retinal ganglion cells (RGCs) are the sole output neurons of the retina that carry information about a visual scene to the brain. By stimulating RGCs with electrical stimulation, it is possible to elicit a sensation of light for people with macular degeneration or retinitis pigmentosa. To investigate the responses of RGCs to high frequency bi-phasic pulse train stimulation, we use previously constrained models of multi-compartment OFF RGCs. The morphologies of mouse RGCs are taken from the Chalupa set of the NeuroMorpho database. The cell models are divided into compartments representing the dendrites, soma and axon that vary between the cells. A total of 132 cells are simulated in the NEURON environment. Results show that the cell morphology plays an important role in the response characteristics of the cell to high frequency bi-phasic pulse train stimulation.

Retinal ganglion cells electrophysiology: the effect of cell morphology on impulse waveform

There are 16 morphologically defined classes of rats retinal ganglion cells (RGCs). Using computer simulation of a realistic anatomically correct A1 mouse RGC, we investigate the effect of the cell's morphology on its impulse waveform, using the first-, and second-order time derivatives as well as the phase plot features. Using whole cell patch clamp recordings, we recorded the impulse waveform for each of the rat RGCs types. While we found some clear differences in many features of the impulse waveforms for A2 and B2 cells compared to other cell classes, many cell types did not show clear differences.

Embracing the irregular: a patient-specific image processing strategy for visual prostheses

We propose a stimulation strategy for retinal prostheses that makes use of irregular shapes of elicited phosphenes. It is patient specific and thus relies on prior psychophysical measurements. Visual perceptions are stored in a phosphene map that relates stimulation parameters to the visual stimulus elicited. Based on this map, stimulation parameters are chosen in such a way that the edges of the target image are optimally represented through the shape of the phosphene. In a psychophysical pilot study, we compare this approach to one in which we choose phosphenes to match the brightness of the target image. We find that participants perform similarly well with both strategies overall. However, the results indicate that each strategy may have advantages for different stimulus sizes. Both of the proposed strategies are novel in using only previously recorded phosphenes rather than a model based on idealized assumptions about the relationship between stimulation parameters and phosphene properties.

Internal inconsistencies in models of electrical stimulation in neural tissue

Calculating the membrane potential of a neurite under extracellular electrical stimulation is important in the design of some recent stimulation strategies for neuroprosthetic devices including retinal implants, cochlear implants, deep brain stimulation. A common approach, widely used in the electrical stimulation literature uses a volume conductor model to calculate the electrical potential in the tissue and then extracts the voltage or current density on the surface of a neuron, which is used as input to the cable equation to calculate the neuron's response. However this approach ignores the effect of the neuron itself as well as surrounding neurons on the extracellular potential. Here we highlight that this leads to an internal inconsistency in the overall model because the result depends on whether the voltage or current density is used to calculate the neural response. The magnitude of this discrepancy is calculated for the example of a point source electrode in a homogeneous medium and is shown to be up to several hundred percent under some stimulus conditions. The inconsistency can be resolved by ensuring that the voltage is related to the current density by the transimpedance of the neurite. Deriving a volume conductor model that satisfies this relationship requires further work.

Determining the electrical impedance of the retina from a complex voltage map

A method for determining the electrical impedance of the retina from complex valued voltage measurements is described. This is an extension to our previous work which did not consider the permittivity of the retina, or an exploration of the number of voltage measurements required to form an adequate solution. The model considers inhomogeneity in permittivity and conductivity of the tissue in both the x and y directions. This framework is tested on noisy voltage data solved for a synthetic rectangular retinal section. A synthetic retinal section inclusive of fovea is also considered. Estimates of the conductivity and permittivity maps are solved for using finite element analysis for a range of down sampling factors and signal to noise ratios for the voltage data. This is done to assess the potential accuracy of this method in a physical experiment.

Minimisation of required charge for desired neuronal spike rate

Retinal implants restore limited visual perception to blind implantees by electrical stimulation of surviving neurons. We consider the efficacy of two electrical stimulation parameters, frequency of stimulation and interphase gap between cathodic and anodic phases, on the required charge to reach a desired neuronal spike rate. Using a Hodgkin-Huxley model of a neuron, we find the most efficient means of achieving a desired spike rate for neurons by electrical stimulation is to use a stimulation frequency identical to the desired spike rate, as well as a long interphase gap.

The effect of morphology upon electrophysiological responses of retinal ganglion cells: simulation results

Retinal ganglion cells (RGCs) display differences in their morphology and intrinsic electrophysiology. The goal of this study is to characterize the ionic currents that explain the behavior of ON and OFF RGCs and to explore if all morphological types of RGCs exhibit the phenomena described in electrophysiological data. We extend our previous single compartment cell models of ON and OFF RGCs to more biophysically realistic multicompartment cell models and investigate the effect of cell morphology on intrinsic electrophysiological properties. The membrane dynamics are described using the Hodgkin - Huxley type formalism. A subset of published patch-clamp data from isolated intact mouse retina is used to constrain the model and another subset is used to validate the model. Two hundred morphologically distinct ON and OFF RGCs are simulated with various densities of ionic currents in different morphological neuron compartments. Our model predicts that the differences between ON and OFF cells are explained by the presence of the low voltage activated calcium current in OFF cells and absence of such in ON cells. Our study shows through simulation that particular morphological types of RGCs are capable of exhibiting the full range of phenomena described in recent experiments. Comparisons of outputs from different cells indicate that the RGC morphologies that best describe recent experimental results are ones that have a larger ratio of soma to total surface area.

Delay selection by spike-timing-dependent plasticity in recurrent networks of spiking neurons receiving oscillatory inputs

Learning rules, such as spike-timing-dependent plasticity (STDP), change the structure of networks of neurons based on the firing activity. A network level understanding of these mechanisms can help infer how the brain learns patterns and processes information. Previous studies have shown that STDP selectively potentiates feed-forward connections that have specific axonal delays, and that this underlies behavioral functions such as sound localization in the auditory brainstem of the barn owl. In this study, we investigate how STDP leads to the selective potentiation of recurrent connections with different axonal and dendritic delays during oscillatory activity. We develop analytical models of learning with additive STDP in recurrent networks driven by oscillatory inputs, and support the results using simulations with leaky integrate-and-fire neurons. Our results show selective potentiation of connections with specific axonal delays, which depended on the input frequency. In addition, we demonstrate how this can lead to a network becoming selective in the amplitude of its oscillatory response to this frequency. We extend this model of axonal delay selection within a single recurrent network in two ways. First, we show the selective potentiation of connections with a range of both axonal and dendritic delays. Second, we show axonal delay selection between multiple groups receiving out-of-phase, oscillatory inputs. We discuss the application of these models to the formation and activation of neuronal ensembles or cell assemblies in the cortex, and also to missing fundamental pitch perception in the auditory brainstem.

Our brain's ability to perform cognitive processes, such as object identification, problem solving, and decision making, comes from the specific connections between neurons. The neurons carry information as spikes that are transmitted to other neurons via connections with different strengths and propagation delays. Experimentally observed learning rules can modify the strengths of connections between neurons based on the timing of their spikes. The learning that occurs in neuronal networks due to these rules is thought to be vital to creating the structures necessary for different cognitive processes as well as for memory. The spiking rate of populations of neurons has been observed to oscillate at particular frequencies in various brain regions, and there is evidence that these oscillations play a role in cognition. Here, we use analytical and numerical methods to investigate the changes to the network structure caused by a specific learning rule during oscillatory neural activity. We find the conditions under which connections with propagation delays that resonate with the oscillations are strengthened relative to the other connections. We demonstrate that networks learn to oscillate more strongly to oscillations at the frequency they were presented with during learning. We discuss the possible application of these results to specific areas of the brain.

Information theoretic inference of the optimal number of electrodes for future cochlear implants using a spiral cochlea model

Contemporary cochlear implants stimulate the auditory nerve with an array of up to 22 electrodes. More electrodes do not typically provide improved hearing performance. Given that this limitation is primarily due to current spread, and that newly developing kinds of electrodes may enable more focused stimulation, we recently proposed an information theoretic modeling framework for estimating how many electrodes might achieve optimal hearing performance under a range of assumptions about electrodes and their placement relative to the nerve. Here, we extend this approach by introducing more realistic three-dimensional spiral geometries for the cochlea and array, and comparing the optimal number of electrodes predicted by our model for this case with that in our original model, which used a linear geometry.

Optimized single pulse stimulation strategy for retinal implants

Retinal implants offer prospects of vision restoration for some blind patients by eliciting visual percepts of spots of light called 'phosphenes'. Recently, a mathematical model has been developed that predicts patients' perception of phosphene brightness for current-driven electrical stimulation of the retina. This model is explored for different stimulation parameters on a single electrode, including safety and hardware limitations, to produce phosphenes of specified brightness. We describe a procedure to derive stimulation parameters to account for such constraints, and describe methods to construct optimal stimuli in terms of producing maximal perceived brightness and efficient generation of phosphenes of a given brightness by employing minimal energy. In both cases, it is found that the resulting optimized stimulation waveforms consist of a long stimulation period, and interphase delays between initial and charge-balancing phases.

Modeling extracellular electrical stimulation: II. Computational validation and numerical results

The validity of approximate equations describing the membrane potential under extracellular electrical stimulation (Meffin et al 2012 J. Neural Eng. 9 065005) is investigated through finite element analysis in this paper. To this end, the finite element method is used to simulate a cylindrical neurite under extracellular stimulation. Laplace's equations with appropriate boundary conditions are solved numerically in three dimensions and the results are compared to the approximate analytic solutions. Simulation results are in agreement with the approximate analytic expressions for longitudinal and transverse modes of stimulation. The range of validity of the equations describing the membrane potential for different values of stimulation and neurite parameters are presented as well. The results indicate that the analytic approach can be used to model extracellular electrical stimulation for realistic physiological parameters with a high level of accuracy.

Retinal prosthesis safety: alterations in microglia morphology due to thermal damage and retinal implant contact

PURPOSE

In order to develop retinal implants with a large number of electrodes, it is necessary to ensure that they do not cause damage to the neural tissue by the heat that the electrical circuits generate. Knowledge about the threshold of the amount of power that induces damage will assist in developing power budgets for retinal implants.

METHODS

Heat-induced retinal damage was evaluated by measuring changes in the morphology of the resident immune cells, the microglia, which are the first cells to respond to retinal trauma. Microglial soma and arbor areas were assessed in rat retinal tissues in vitro to determine the effects of increasing temperatures, implant contact, and heating and implant contact combined.

RESULTS

In response to increasing incubation temperatures (no implant), microglial somas enlarged and arbor areas retracted, indicative of retinal stress. Thermal damage thresholds, defined as a significant change in microglial morphology from that observed at the upper limit of normal body temperature, occurred at a temperature of 38.7 °C. Implant contact, induced when a passive implant was placed on the retina, also caused similar morphological alterations in microglia, indicating retinal damage. Heated-implant contact exacerbated the effects of temperature alone but still resulted in a thermal damage threshold of 38.7 °C, the same as with heating alone.

CONCLUSIONS

Our conservative recommendations are that implanted retinal electronics keep power dissipations to less than 19 mW/mm(2) to stay below the microglial thermal damage threshold (2.1 °C) and to comply with international standards for implantable devices (2 °C).

Modeling extracellular electrical stimulation: I. Derivation and interpretation of neurite equations

Neuroprosthetic devices, such as cochlear and retinal implants, work by directly stimulating neurons with extracellular electrodes. This is commonly modeled using the cable equation with an applied extracellular voltage. In this paper a framework for modeling extracellular electrical stimulation is presented. To this end, a cylindrical neurite with confined extracellular space in the subthreshold regime is modeled in three-dimensional space. Through cylindrical harmonic expansion of Laplace's equation, we derive the spatio-temporal equations governing different modes of stimulation, referred to as longitudinal and transverse modes, under types of boundary conditions. The longitudinal mode is described by the well-known cable equation, however, the transverse modes are described by a novel ordinary differential equation. For the longitudinal mode, we find that different electrotonic length constants apply under the two different boundary conditions. Equations connecting current density to voltage boundary conditions are derived that are used to calculate the trans-impedance of the neurite-plus-thin-extracellular-sheath. A detailed explanation on depolarization mechanisms and the dominant current pathway under different modes of stimulation is provided. The analytic results derived here enable the estimation of a neurite's membrane potential under extracellular stimulation, hence bypassing the heavy computational cost of using numerical methods.

An investigation of dendritic delay in octopus cells of the mammalian cochlear nucleus

Octopus cells, located in the mammalian auditory brainstem, receive their excitatory synaptic input exclusively from auditory nerve fibers (ANFs). They respond with accurately timed spikes but are broadly tuned for sound frequency. Since the representation of information in the auditory nerve is well understood, it is possible to pose a number of questions about the relationship between the intrinsic electrophysiology, dendritic morphology, synaptic connectivity, and the ultimate functional role of octopus cells in the brainstem. This study employed a multi-compartmental Hodgkin-Huxley model to determine whether dendritic delay in octopus cells improves synaptic input coincidence detection in octopus cells by compensating for the cochlear traveling wave delay. The propagation time of post-synaptic potentials from synapse to soma was investigated. We found that the total dendritic delay was approximately 0.275 ms. It was observed that low-threshold potassium channels in the dendrites reduce the amplitude dependence of the dendritic delay of post-synaptic potentials. As our hypothesis predicted, the model was most sensitive to acoustic onset events, such as the glottal pulses in speech when the synaptic inputs were arranged such that the model's dendritic delay compensated for the cochlear traveling wave delay across the ANFs. The range of sound frequency input from ANFs was also investigated. The results suggested that input to octopus cells is dominated by high frequency ANFs.

Frequency selectivity emerging from spike-timing-dependent plasticity

Periodic neuronal activity has been observed in various areas of the brain, from lower sensory to higher cortical levels. Specific frequency components contained in this periodic activity can be identified by a neuronal circuit that behaves as a bandpass filter with given preferred frequency, or best modulation frequency (BMF). For BMFs typically ranging from 10 to 200 Hz, a plausible and minimal configuration consists of a single neuron with adjusted excitatory and inhibitory synaptic connections. The emergence, however, of such a neuronal circuitry is still unclear. In this letter, we demonstrate how spike-timing-dependent plasticity (STDP) can give rise to frequency-dependent learning, thus leading to an input selectivity that enables frequency identification. We use an in-depth mathematical analysis of the learning dynamics in a population of plastic inhibitory connections. These provide inhomogeneous postsynaptic responses that depend on their dendritic location. We find that synaptic delays play a crucial role in organizing the weight specialization induced by STDP. Under suitable conditions on the synaptic delays and postsynaptic potentials (PSPs), the BMF of a neuron after learning can match the training frequency. In particular, proximal (distal) synapses with shorter (longer) dendritic delay and somatically measured PSP time constants respond better to higher (lower) frequencies. As a result, the neuron will respond maximally to any stimulating frequency (in a given range) with which it has been trained in an unsupervised manner. The model predicts that synapses responding to a given BMF form clusters on dendritic branches.

Probing for cortical excitability

This paper introduces a new method for measuring cortical excitability using an electrical probing stimulus via intracranial electroencephalography (iEEG). Stimuli consisted of 100 single bi-phasic pulses, delivered every 10 minutes. Neural excitability is estimated by extracting a feature from the iEEG responses to the stimuli, which we dub the mean phase variance (PV). We show that the mean PV increases with the rate of inter-ictal discharges in one patient. In another patient, we show that the mean PV changes with sleep and an epileptic seizure. The results demonstrate a proof-of-principal for the method to be applied in a seizure anticipation framework.

Predicting phosphene elicitation in patients with retinal implants: a mathematical study

Single pulse waveforms were considered in a recent model for phosphene elicitation in patients with a retinal prosthesis. Waveforms are constrained to charge-balanced stimuli consisting of a single cathodic and anodic pulse pair. Mathematical models of such stimuli have been constructed and presented based upon patient testimonials. In this work, we derive analytic expressions that may be employed to determine equibrightness levels for different waveforms. We provide an example calculation to show quantitative improvements in stimulation efficiency that are consistent with qualitative findings on waveform effects.

Feature accentuation in phosphenated images

We present and evaluate different approaches to feature accentuation in phosphenated images for different image resolutions. The goal of this study is to find methods to attract an implantee's visual attention to important image content like faces, obstacles or road signs. We do this by defining an important region in the image and accentuating it by either increasing the brightness of outlining phosphenes or by using elliptical phosphenes to circumscribe the feature. While we only see limited benefit of ellipse phosphenes for a high-resolution prosthesis, the use of elliptical phosphenes of different orientations is a promising way to highlight features in a low-resolution phosphene representation of an image.

Electrical probing of cortical excitability in patients with epilepsy

Standard methods for seizure prediction involve passive monitoring of intracranial electroencephalography (iEEG) in order to track the 'state' of the brain. This paper introduces a new method for measuring cortical excitability using an electrical probing stimulus. Electrical probing enables feature extraction in a more robust and controlled manner compared to passively tracking features of iEEG signals. The probing stimuli consist of 100 bi-phasic pulses, delivered every 10 min. Features representing neural excitability are estimated from the iEEG responses to the stimuli. These features include the amplitude of the electrically evoked potential, the mean phase variance (univariate), and the phase-locking value (bivariate). In one patient, it is shown how the features vary over time in relation to the sleep-wake cycle and an epileptic seizure. For a second patient, it is demonstrated how the features vary with the rate of interictal discharges. In addition, the spatial pattern of increases and decreases in phase synchrony is explored when comparing periods of low and high interictal discharge rates, or sleep and awake states. The results demonstrate a proof-of-principle for the method to be applied in a seizure anticipation framework. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.

Heating of the eye by a retinal prosthesis: modeling, cadaver and in vivo study

In order to develop retinal implants with a large number of electrodes, it is necessary to ensure that they do not cause damage to the neural tissue by the heat that the electrical circuits generate. Knowledge of the amount of power that induces thermal damage will assist in development of power budgets for implants, which has a significant effect upon the design of the prostheses circuitry. In this study, temperatures were measured at multiple locations on the retina while the retina was heated in cadaver and in vivo preparations using a variety of prosthesis implantation sites. A finite element thermal model of the cat eye was also created and validated by the cadaver and in vivo tests, allowing for a much larger spectrum of thermal influences to be evaluated without additional animal experimentation. To ensure that retinal tissue temperatures are not increased by more than 2 °C, a 5 mm × 5 mm, suprachoroidally implanted heating element must not dissipate more than 135 mW (5.4 mW/mm (2)).