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The human brain has the capacity to rapidly change state, and in epilepsy these state changes can be catastrophic, resulting in loss of consciousness, injury and even death. Theoretical interpretations considering the brain as a dynamical system suggest that prior to a seizure, recorded brain signals may exhibit critical slowing down, a warning signal preceding many critical transitions in dynamical systems. Using long-term intracranial electroencephalography (iEEG) recordings from fourteen patients with focal epilepsy, we monitored key signatures of critical slowing down prior to seizures. The metrics used to detect critical slowing down fluctuated over temporally long scales (hours to days), longer than would be detectable in standard clinical evaluation settings. Seizure risk was associated with a combination of these signals together with epileptiform discharges. These results provide strong validation of theoretical models and demonstrate that critical slowing down is a reliable indicator that could be used in seizure forecasting algorithms., Critical slowing (associated with increased variance and autocorrelation) can precede critical state transitions. Here, the authors show critical slowing can be used as a marker in seizure forecasting algorithms.

BACKGROUND: Circadian and multiday rhythms are found across many biological systems, including cardiology, endocrinology, neurology, and immunology. In people with epilepsy, epileptic brain activity and seizure occurrence have been found to follow circadian, weekly, and monthly rhythms. Understanding the relationship between these cycles of brain excitability and other physiological systems can provide new insight into the causes of multiday cycles. The brain-heart link has previously been considered in epilepsy research, with potential implications for seizure forecasting, therapy, and mortality (i.e., sudden unexpected death in epilepsy). METHODS: We report the results from a non-interventional, observational cohort study, Tracking Seizure Cycles. This study sought to examine multiday cycles of heart rate and seizures in adults with diagnosed uncontrolled epilepsy (N=31) and healthy adult controls (N=15) using wearable smartwatches and mobile seizure diaries over at least four months (M=12.0, SD=5.9; control M=10.6, SD=6.4). Cycles in heart rate were detected using a continuous wavelet transform. Relationships between heart rate cycles and seizure occurrence were measured from the distributions of seizure likelihood with respect to underlying cycle phase. FINDINGS: Heart rate cycles were found in all 46 participants (people with epilepsy and healthy controls), with circadian (N=46), about-weekly (N=25) and about-monthly (N=13) rhythms being the most prevalent. Of the participants with epilepsy, 19 people had at least 20 reported seizures, and 10 of these had seizures significantly phase locked to their multiday heart rate cycles. INTERPRETATION: Heart rate cycles showed similarities to multiday epileptic rhythms and may be comodulated with seizure likelihood. The relationship between heart rate and seizures is relevant for epilepsy therapy, including seizure forecasting, and may also have implications for cardiovascular disease. More broadly, understanding the link between multiday cycles in the heart and brain can shed new light on endogenous physiological rhythms in humans. FUNDING: This research received funding from the Australian Government National Health and Medical Research Council (investigator grant 1178220), the Australian Government BioMedTech Horizons program, and the Epilepsy Foundation of America's 'My Seizure Gauge' grant. ispartof: EBIOMEDICINE vol:72 ispartof: location:Netherlands status: published

BackgroundImplantable brain–computer interfaces (BCIs), functioning as motor neuroprostheses, have the potential to restore voluntary motor impulses to control digital devices and improve functional independence in patients with severe paralysis due to brain, spinal cord, peripheral nerve or muscle dysfunction. However, reports to date have had limited clinical translation.MethodsTwo participants with amyotrophic lateral sclerosis (ALS) underwent implant in a single-arm, open-label, prospective, early feasibility study. Using a minimally invasive neurointervention procedure, a novel endovascular Stentrode BCI was implanted in the superior sagittal sinus adjacent to primary motor cortex. The participants undertook machine-learning-assisted training to use wirelessly transmitted electrocorticography signal associated with attempted movements to control multiple mouse-click actions, including zoom and left-click. Used in combination with an eye-tracker for cursor navigation, participants achieved Windows 10 operating system control to conduct instrumental activities of daily living (IADL) tasks.ResultsUnsupervised home use commenced from day 86 onwards for participant 1, and day 71 for participant 2. Participant 1 achieved a typing task average click selection accuracy of 92.63% (100.00%, 87.50%–100.00%) (trial mean (median, Q1–Q3)) at a rate of 13.81 (13.44, 10.96–16.09) correct characters per minute (CCPM) with predictive text disabled. Participant 2 achieved an average click selection accuracy of 93.18% (100.00%, 88.19%–100.00%) at 20.10 (17.73, 12.27–26.50) CCPM. Completion of IADL tasks including text messaging, online shopping and managing finances independently was demonstrated in both participants.ConclusionWe describe the first-in-human experience of a minimally invasive, fully implanted, wireless, ambulatory motor neuroprosthesis using an endovascular stent-electrode array to transmit electrocorticography signals from the motor cortex for multiple command control of digital devices in two participants with flaccid upper limb paralysis.

Background While the effects of prolonged sleep deprivation (≥24 h) on seizure occurrence has been thoroughly explored, little is known about the effects of day-to-day variations in the duration and quality of sleep on seizure probability. A better understanding of the interaction between sleep and seizures may help to improve seizure management. Methods To explore how sleep and epileptic seizures are associated, we analysed continuous intracranial electroencephalography (EEG) recordings collected from 10 patients with refractory focal epilepsy undergoing ordinary life activities between 2010 and 2012 from three clinical centres (Austin Health, The Royal Melbourne Hospital, and St Vincent's Hospital of the Melbourne University Epilepsy Group). A total of 4340 days of sleep-wake data were analysed (average 434 days per patient). EEG data were sleep scored using a semi-automated machine learning approach into wake, stages one, two, and three non-rapid eye movement sleep, and rapid eye movement sleep categories. Findings Seizure probability changes with day-to-day variations in sleep duration. Logistic regression models revealed that an increase in sleep duration, by 1·66 ± 0·52 h, lowered the odds of seizure by 27% in the following 48 h. Following a seizure, patients slept for longer durations and if a seizure occurred during sleep, then sleep quality was also reduced with increased time spent aroused from sleep and reduced rapid eye movement sleep. Interpretation Our results suggest that day-to-day deviations from regular sleep duration correlates with changes in seizure probability. Sleeping longer, by 1·66 ± 0·52 h, may offer protective effects for patients with refractory focal epilepsy, reducing seizure risk. Furthermore, the occurrence of a seizure may disrupt sleep patterns by elongating sleep and, if the seizure occurs during sleep, reducing its quality.

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., Author summary Many cortical functions originate from the learning ability of the brain. How the properties of cortical cells are learned is vital for understanding how the brain works. There are many models that explain how V1 simple cells can be learned. However, how V1 complex cells are learned still remains unclear. In this paper, we propose a model of learning in complex cells based on the Bienenstock, Cooper, and Munro (BCM) rule. We demonstrate that properties of receptive fields of complex cells can be learned using this biologically plausible learning rule. Quantitative comparisons between the model and experimental data are performed. Results show that model complex cells can account for the diversity of complex cells found in experimental studies. In summary, this study provides a plausible explanation for how complex cells can be learned using biologically plausible plasticity mechanisms. Our findings help us to better understand biological vision processing and provide us with insights into the general signal processing principles that the visual cortex employs to process visual information.

Refereed/Peer-reviewed Despite the development and success of cochlear implants over several decades, wide inter-subject variability in speech perception is reported. This suggests that cochlear implant user-dependent factors limit speech perception at the individual level. Clinical studies have demonstrated the importance of the number, placement, and insertion depths of electrodes on speech recognition abilities. However, these do not account for all inter-subject variability and to what extent these factors affect speech recognition abilities has not been studied. In this paper, an information theoretic method and machine learning technique are unified in a model to investigate the extent to which key factors limit cochlear implant electrode discrimination. The framework uses a neural network classifier to predict which electrode is stimulated for a given simulated activation pattern of the auditory nerve, and mutual information is then estimated between the actual stimulated electrode and predicted ones. We also investigate how and to what extent the choices of parameters affect the performance of the model. The advantages of this framework include i) electrode discrimination ability is quantified using information theory, ii) it provides a flexible framework that may be used to investigate the key factors that limit the performance of cochlear implant users, and iii) it provides insights for future modeling studies of other types of neural prostheses.

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.

Summary Spurred in part by literature published in the immediate aftermath of the severe acute respiratory syndrome epidemic in 2003, powered air‐purifying respirators have seen increased use worldwide during the COVID‐19 pandemic. Whereas these devices provide excellent protection of the user, there is an added element of risk during doffing and cleaning of the device. An additional layer of barrier protection, in the form of a polypropylene gown, to be worn over the hood and motor belt, can be used to minimise this risk. However, the device entrains air perpendicular to the lie of the gown, resulting in the impermeable material being sucked into the air intake, and partial occlusion of flow. In this report, we describe a clinical‐academic partnership whereby a bespoke filter guard was designed to disrupt airflow and prevent gown entrainment, thereby enabling full barrier protection of both the device and user. This intervention was simple, cheap, scalable and able to be mass produced.

SUMMARYObjectiveSeizure unpredictability is rated as one of the most challenging aspects of living with epilepsy. Seizure likelihood can be influenced by a range of environmental and physiological factors that are difficult to measure and quantify. However, some generalizable patterns have been demonstrated in seizure onset. A majority of people with epilepsy exhibit circadian rhythms in their seizure times and many also show slower, multiday patterns. Seizure cycles can be measured using a range of recording modalities, including self-reported electronic seizure diaries. This study aimed to develop personalized forecasts from a mobile seizure diary app.MethodsForecasts based on circadian and multiday seizure cycles were tested pseudo-prospectively using data from 33 app users (mean of 103 seizures per subject). Individual’s strongest cycles were estimated from their reported seizure times and used to derive the likelihood of future seizures. The forecasting approach was validated using self-reported events and electrographic seizures from the Neurovista dataset, an existing database of long-term electroencephalography that has been widely used to develop forecasting algorithms.ResultsThe validation dataset showed that forecasts of seizure likelihood based on self-reported cycles were predictive of electrographic seizures. Forecasts using only mobile app diaries allowed users to spend an average of 62.8% of their time in a low-risk state, with 16.6% of their time in a high-risk warning state. On average, 64.5% of seizures occurred during high-risk states and less than 10% of seizures occurred in low-risk states.SignificanceSeizure diary apps can provide personalized forecasts of seizure likelihood that are accurate and clinically relevant for electrographic seizures. These results have immediate potential for translation to a prospective seizure forecasting trial using a mobile diary app. It is our hope that seizure forecasting apps will one day give people with epilepsy greater confidence in managing their daily activities.

Background Seizure prediction can increase independence and allow preventative treatment for patients with epilepsy. We present a proof-of-concept for a seizure prediction system that is accurate, fully automated, patient-specific, and tunable to an individual's needs. Methods Intracranial electroencephalography (iEEG) data of ten patients obtained from a seizure advisory system were analyzed as part of a pseudoprospective seizure prediction study. First, a deep learning classifier was trained to distinguish between preictal and interictal signals. Second, classifier performance was tested on held-out iEEG data from all patients and benchmarked against the performance of a random predictor. Third, the prediction system was tuned so sensitivity or time in warning could be prioritized by the patient. Finally, a demonstration of the feasibility of deployment of the prediction system onto an ultra-low power neuromorphic chip for autonomous operation on a wearable device is provided. Results The prediction system achieved mean sensitivity of 69% and mean time in warning of 27%, significantly surpassing an equivalent random predictor for all patients by 42%. Conclusion This study demonstrates that deep learning in combination with neuromorphic hardware can provide the basis for a wearable, real-time, always-on, patient-specific seizure warning system with low power consumption and reliable long-term performance., Highlights • We use deep learning and long-term neural data to develop an automated, patient-tunable epileptic seizure prediction system. • We deploy our prediction system on a low-power neuromorphic chip to form the basis of a wearable device. Predicting and treating the debilitating seizures suffered by epileptic patients has challenged medical researchers for over fifty years. A new way forward was opened when Cook and colleagues, in 2013, collected a large longitudinal and continuous dataset recorded directly from patients' brains for one to three years. Harnessing the recent breakthroughs in deep learning techniques and in building specialized processing chips, we have demonstrated that seizures can now be predicted by a portable device. Our system automatically learns patient-specific pre-seizure signatures, and, in real time, warns of oncoming seizures.

We present the results of a model inversion algorithm for electrocorticography (ECoG) data recorded during epileptic seizures. The states and parameters of neural mass models were tracked during a total of over 3000 seizures from twelve patients with focal epilepsy. These models provide an estimate of the effective connectivity within intracortical circuits over the time course of seizures. Observing the dynamics of effective connectivity provides insight into mechanisms of seizures. Estimation of patients seizure dynamics revealed: 1) a highly stereotyped pattern of evolution for each patient, 2) distinct sub-groups of onset mechanisms amongst patients, and 3) different offset mechanisms for long and short seizures. Stereotypical dynamics suggest that, once initiated, seizures follow a deterministic path through the parameter space of a neural model. Furthermore, distinct sub-populations of patients were identified based on characteristic motifs in the dynamics at seizure onset. There were also distinct patterns between long and short duration seizures that were related to seizure offset. Understanding how these different patterns of seizure evolution arise may provide new insights into brain function and guide treatment for epilepsy, since specific therapies may have preferential effects on the various parameters that could potentially be individualized. Methods that unite computational models with data provide a powerful means to generate testable hypotheses for further experimental research. This work provides a demonstration that the hidden connectivity parameters of a neural mass model can be dynamically inferred from data. Our results underscore the power of theoretical models to inform epilepsy management. It is our hope that this work guides further efforts to apply computational models to clinical data., Author summary A fundamental question in clinical neuroscience is how and why the brain generates epileptic seizures. To address this problem it is important to unify theoretical models of seizure mechanisms with clinical data. This study investigated a large database of human epileptic seizure recordings. Model inversion was used to track seizure dynamics through the lens of a mathematical model for cortical regions. These models can reveal the relative activity and coupling between excitatory, inhibitory and pyramidal neural populations that cannot be directly measured. Measuring cortical dynamics during seizures can provide insight into epilepsy, and facilitate new treatment strategies. Our analysis of connection strengths revealed important aspects of seizure onset and seizure termination. Our findings have implications for understanding seizure mechanisms and treating epilepsy.

The mechanisms underlying how activity in the visual pathway may give rise through neural plasticity to many of the features observed experimentally in the early stages of visual processing was provided by Linkser in a seminal, three-paper series. Owing to the complexity of multi-layer models, an implicit assumption in Linsker's and subsequent papers has been that propagation delay is homogeneous and plays little functional role in neural behaviour. We relax this assumption to examine the impact of distance-dependent axonal propagation delay on neural learning. We show that propagation delay induces low-pass filtering by dispersing the arrival times of spikes from presynaptic neurons, providing a natural correlation cancellation mechanism for distal connections. The cut-off frequency decreases as the radial propagation delay within a layer increases relative to propagation delay between the layers, introducing an upper limit on temporal resolution. Given that the PSP also acts as a low-pass filter, we show that the effective time constant of each should enable the processing of similar scales of temporal information. This result has implications for the visual system, in which receptive field size and, thus, radial propagation delay, increases with eccentricity. Furthermore, the network response is frequency dependent since higher frequencies require increased input amplitude to compensate for attenuation. This concords with frequency-dependent contrast sensitivity in the visual system, which changes with eccentricity and receptive field size. We further show that the proportion of inhibition relative to excitation is larger where radial propagation delay is long relative to inter-laminar propagation delay. We show that the addition of propagation delay reduces the range in the cell's on-center size, providing stability to variations in homeostatic parameters., 27 pages, 6 figures

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., Author summary Implantable neural stimulation devices are being widely used and clinically tested for the restoration of lost function (e.g. cochlear implants) and the treatment of neurological disorders. Smart devices that can combine sensing and stimulation will dramatically improve future patient outcomes. To this end, mathematical models that can accurately predict neural responses to electrical stimulation will be critical for the development of smart stimulation devices. Here, we demonstrate a model that predicts neural responses to simultaneous stimulation across multiple electrodes in the retina. We show that the activation of presynaptic neurons leads to nonlinearities in the responses of postsynaptic retinal ganglion cells. The model is accurate and is applicable to a wide range of neural stimulation devices.

Objective. Currently, a challenge in electrical stimulation of the retina is to excite only the cells lying directly under the electrode in the ganglion cell layer, while avoiding excitation of the axons that pass over the surface of the retina in the nerve fiber layer. Since these passing fibers may originate from distant regions of the ganglion cell layer. Stimulation of both target retinal ganglion cells and overlying axons results in irregular visual percepts, significantly limiting perceptual efficacy. This research explores how differences in fiber orientation between the nerve fiber layer and ganglion cell layer leads to differences in the activation of the axon initial segment and axons of passage. Approach. Axons of passage of retinal ganglion cells in the nerve fiber layer are characterized by a narrow distribution of fiber orientations, causing highly anisotropic spread of applied current. In contrast, proximal axons in the ganglion cell layer have a wider distribution of orientations. A four-layer computational model of epiretinal extracellular stimulation that captures the effect of neurite orientation in anisotropic tissue has been developed using a modified version of the standard volume conductor model, known as the cellular composite model. Simulations are conducted to investigate the interaction of neural tissue orientation, stimulating electrode configuration, and stimulation pulse duration and amplitude. Main results. The dependence of fiber activation on the anisotropic nature of the nerve fiber layer is first established. Via a comprehensive search of key parameters, our model shows that the simultaneous stimulation with multiple electrodes aligned with the nerve fiber layer can be used to achieve selective activation of axon initial segments rather than passing fibers. This result can be achieved with only a slight increase in total stimulus current and modest increases in the spread of activation in the ganglion cell layer, and is shown to extend to the general case of arbitrary electrode array positioning and arbitrary target neural volume. Significance. These results elucidate a strategy for more targeted stimulation of retinal ganglion cells with experimentally-relevant multi-electrode geometries and readily achievable stimulation requirements.

Tinnitus (ringing in the ears) is a common auditory sensation that can become a chronic debilitating health condition with pervasive effects on health and wellbeing, substantive economic burden, and no known cure. Here we investigate if impaired functioning of the cognitive control network that directs attentional focus is a mechanism erroneously maintaining the tinnitus sensation. Fifteen people with chronic tinnitus and 15 healthy controls matched for age and gender from the community performed a cognitively demanding task known to activate the cognitive control network in this functional magnetic resonance imaging study. We identify attenuated activation of a core node of the cognitive control network (the right middle frontal gyrus), and altered baseline connectivity between this node and nodes of the salience and autobiographical memory networks. Our findings indicate that in addition to auditory dysfunction, altered interactions between non-auditory neurocognitive networks maintain chronic tinnitus awareness, revealing new avenues for the identification of effective treatments.

Objective: By modeling the cochlear implant (CI) electrode-to-nerve interface and quantifying electrode discriminability in the model, we address the questions of how many individual channels can be distinguished by CI recipients and the extent to which performance might be improved by inserting electrodes deeper into the cochlea. Method: We adapt an artificial neural network to model electrode discrimination as well as a commonly used psychophysical measure (four-interval forced-choice) in CI stimulation and predict how well the locations of the stimulating electrodes can be inferred from simulated auditory nerve spiking patterns. Results: We show that a longer electrode leads to better electrode place discrimination in our model. For a simulated four-interval forced-choice procedure, correct classification rates significantly reduce with decreasing distance between the test electrodes and the reference electrodes, and higher correct classification rates may be achieved by the basal electrodes than apical electrodes. Conclusion: Our results suggest that enhanced electrode discriminability results from a longer CI electrode array, and the locations where the errors occur along the electrode array are not only affected by the distance between electrodes but also the twirling angle between electrodes. Significance: Our models and simulations provide theoretical insights into several important clinically relevant problems that will inform future designs of CI electrode arrays and stimulation strategies. Refereed/Peer-reviewed

Pitch perception is important for understanding speech prosody, music perception, recognizing tones in tonal languages, and perceiving speech in noisy environments. The two principal pitch perception theories consider the place of maximum neural excitation along the auditory nerve and the temporal pattern of the auditory neurons’ action potentials (spikes) as pitch cues. This paper describes a biophysical mechanism by which fine-structure temporal information can be extracted from the spikes generated at the auditory periphery. Deriving meaningful pitch-related information from spike times requires neural structures specialized in capturing synchronous or correlated activity from amongst neural events. The emergence of such pitch-processing neural mechanisms is described through a computational model of auditory processing. Simulation results show that a correlation-based, unsupervised, spike-based form of Hebbian learning can explain the development of neural structures required for recognizing the pitch of simple and complex tones, with or without the fundamental frequency. The temporal code is robust to variations in the spectral shape of the signal and thus can explain the phenomenon of pitch constancy., Author Summary Pitch is the perceptual correlate of sound frequency. Our auditory system has a sophisticated mechanism to process and perceive the neural information corresponding to pitch. This mechanism employs both the place and the temporal pattern of pitch-evoked neural events. Based on the known functions of the auditory system, we develop a computational model of pitch perception using a network of neurons with modifiable connections. We demonstrate that a well-known neural learning rule that is based on the timing of the neural events can identify and strengthen the neuronal connections that are most effective for the extraction of pitch. By providing an insight into how the auditory system interprets pitch information, the results of our study can be used to develop improved sound processing strategies for cochlear implants. In cochlear implant hearing, auditory percept is generated by stimulating the auditory neurons with controlled electrical impulses, enhancing which with the help of the model would lead to a better representation of pitch and would subsequently improve music perception and speech understanding in noisy environments in cochlear implant users.

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., Author Summary Implantable multi-electrode arrays (MEAs) are used to record neurological signals and stimulate the nervous system to restore lost function (e.g. cochlear implants). MEAs that can combine both sensing and stimulation will revolutionize the development of the next generation of devices. Simple models that can accurately characterize neural responses to electrical stimulation are necessary for the development of future neuroprostheses controlled by neural feedback. We demonstrate a model that accurately predicts neural responses to concurrent stimulation across multiple electrodes. The model is simple to evaluate, making it an appropriate model for use with neural feedback. The methods described are applicable to a wide range of neural prostheses, thus greatly assisting future device development.

This paper provides a new method for model-based estimation of intra-cortical connectivity from electrophysiological measurements. A novel closed-form solution for the connectivity function of the Amari neural field equations is derived as a function of electrophysiological observations. The resultant intra-cortical connectivity estimate is driven from experimental data, but constrained by the mesoscopic neurodynamics that are encoded in the computational model. A demonstration is provided to show how the method can be used to image physiological mechanisms that govern cortical dynamics, which are normally hidden in clinical data from epilepsy patients. Accurate estimation performance is demonstrated using synthetic data. Following the computational testing, results from patient data are obtained that indicate a dominant increase in surround inhibition prior to seizure onset that subsides in the cases when the seizures spread.

International audience; Over the past three decades, a number of seizure prediction, or forecasting, methods have been developed. Although major achievements were accomplished regarding the statistical evaluation of proposed algorithms, it is recognized that further progress is still necessary for clinical application in patients. The lack of physiological motivation can partly explain this limitation. Therefore, a natural question is raised: can computational models of epilepsy be used to improve these methods? Here, we review the literature on the multiple-scale neural modeling of epilepsy and the use of such models to infer physiologic changes underlying epilepsy and epileptic seizures. The authors argue how these methods can be applied to advance the state-of-the-art in seizure forecasting

Cochlear implants provide functional hearing to people who are profoundly deaf or hearing impaired by replacing the function of missing inner hair cells with an array of stimulating electrodes. Previous studies developed a modeling framework for predicting the optimal number of electrodes, as well as the optimal locations and usage probabilities of electrodes, from an information theoretic perspective. However, the information theoretic method does not quantify the performance of electrode place discrimination. In this paper, we apply a so-called `extreme-learning machine' to the cochlear implant model to calculate the electrode classification error rates. We also investigate the locations along the electrode array where errors are most likely to occur. We conclude based on our model that i) the classification error rate increases with increasing number of electrodes and the classification errors occur predominantly between adjacent electrodes, ii) by inserting the electrode array deeper into the cochlea, more electrode locations can be distinguished and the electrodes for which most errors occur are determined by the distance and spiral twirling angle between adjacent electrodes. Refereed/Peer-reviewed

This work describes a generalized method for classifying motor-related neural signals for a brain-computer interface (BCI), based on a stochastic machine learning method. The method differs from the various feature extraction and selection techniques employed in many other BCI systems. The classifier does not use extensive a-priori information, resulting in reduced reliance on highly specific domain knowledge. Instead of pre-defining features, the time-domain signal is input to a population of multi-layer perceptrons (MLPs) in order to perform a stochastic search for the best structure. The results showed that the average performance of the new algorithm outperformed other published methods using the Berlin BCI IV (2008) competition dataset and was comparable to the best results in the Berlin BCI II (2002–3) competition dataset. The new method was also applied to electroencephalography (EEG) data recorded from five subjects undertaking a hand squeeze task and demonstrated high levels of accuracy with a mean classification accuracy of 78.9% after five-fold cross-validation. Our new approach has been shown to give accurate results across different motor tasks and signal types as well as between subjects.

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., Author Summary 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.

Cochlear implants are neural prostheses that can restore hearing. Contemporary cochlear implant devices consist of up to 22 electrodes. An open question for future cochlear implants is whether new electrode designs that enable less current spread may provide improved hearing performance through more precise control of neural activation, more electrodes, or more precise positioning. Here we use a recently proposed information theoretic model for the electro-neural interface that enables estimates to be made of the optimal number of electrodes for different amounts of current spread. We apply information theoretic approaches for finding the channel capacity in the model to enable estimates of optimal electrode usage probabilities and positions. We also compare the performance in the model when auditory nerve fibers are assumed to be heterogenous, with a random distribution of firing thresholds and relative spreads, versus an assumption that they are all identical. Refereed/Peer-reviewed

Octopus cells, located in the mammalian auditory brainstem, receive their excitatory synaptic input ex-clusively from auditory nerve fi□bers. They respond with accurately timed spikes but are broadly tunedfor sound frequency. Since the representation of information in the auditory nerve is well understood, itis 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 thebrainstem. This study employed a multi-compartmental Hodgkin-Huxley model to determine whetherdendritic delay in octopus cells improves synaptic input coincidence detection in octopus cells by com-pensating for the cochlear traveling wave delay. The propagation time of post-synaptic potentials fromsynapse to soma was investigated. We found that the total dendritic delay was approximately 0.275ms. It was observed that low-threshold potassium channels in the dendrites reduce the amplitude de-pendence of the dendritic delay of post-synaptic potentials. As our hypothesis predicted, the model wasmost sensitive to acoustic onset events, such as the glottal pulses in speech when the synaptic inputswere arranged such that the model's dendritic delay compensated for the cochlear traveling wave delayacross the auditory nerve □fibers. The range of sound frequency input from auditory nerve □fibers was alsoinvestigated. The results suggested that input to octopus cells is dominated by high frequency auditorynerve □fibers.

Spike-timing-dependent plasticity (STDP) is a learning rule that updates synaptic strengths based on the relative timing of pre- and post-synaptic spikes. Unlike rate-based Hebbian learning, STDP can potentially encode fast temporal correlations in neuronal activity, such as oscillations, in the functional structure of networks of neurons that have axonal and dendritic propagation delays. The motivation behind this study was to understand the different ways that spatiotemporal patterns can be learnt by the recurrent connections in a network of neurons with STDP present. This understanding is vital to uncovering the mechanisms by which basic learning and information processing tasks are performed throughout the brain. A specific example in which these mechanisms may contribute is in explaining how the brain can perceive the pitch of complex sounds up to 300Hz. This work employs and builds upon the analytical framework for learning with STDP used in a previous study [1]. In this study, the changes made by additive STDP to synaptic strengths in recurrent networks with axonal delays receiving oscillatory inputs were investigated analytically with the Poisson neuron model and verified through simulations with leaky integrate-and-fire (LIF) neurons. Frequencies between 100-300Hz were considered, which correspond to the modulation frequencies found in the auditory brainstem representing the fundamental frequency of different natural sounds. The analysis and simulations found that connections were selectively potentiated and depressed based on their axonal delay in such a way that the delays of the strong connections in the network “resonated” with the input frequency (Figure ​(Figure1A).1A). The trained networks were found to respond selectively to the frequency they were trained with (Figure ​(Figure1B).1B). Higher frequencies (e.g. 240Hz as shown here) would always be learnt by the network, but in order to show a selective response after learning they needed faster neuronal and synaptic time constants (see details in Figure ​Figure11 caption). Figure 1 Results of simulations with networks of 10,000 LIF neurons. A: The mean synaptic weight with axonal delay of a network before learning (dashed: all weights initially set to 0.0025 with an even spread of delays over 1-10ms) and after learning for 20,000s ...

Bayesian spiking neurons (BSNs) provide a probablisitic and intuitive interpretation of how spiking neurons could work and have been shown to be equivalent to leaky integrate-and-fire neurons under certain conditions [1]. The study of BSNs has been restricted mainly to small networks because online learning, which currently involves a maximum-likelihood-expectation-maximisation (ML-EM) approach [2,3], is quite slow. Here a new approach to estimating the parameters of Bayesian spiking neurons, referred to as fast learning (FL), is presented and compared to online ML-EM learning. Learning in a BSN is local to the neuron and involves estimation of the transition rate and observation rate parameters of an underlying implicit hidden Markov model (HMM), the hidden state of which the BSN output encodes [1]. Rather than estimating the parameters by maximizing the log-likelihood of the hidden states and the synaptic observations given the parameters as is done in ML-EM [2,3], the FL algorithm directly calculates statistics upon which the parameters depend. This is achieved by taking advantage of the relationship between the log-odds ratio of the hidden state computed by the BSN and the probability that the hidden state is ‘on’ given the past synaptic observations, . Online learning in a two BSN neuron hierarchy is explored, where the first neuron receives N=20 synapses driven by Poisson processes and the second neuron receives input from only the first neuron. Simulations were performed for a fixed set of transition rates and observation rates for 10 different perturbations of the initial transition and observation rate estimates: ±0-20%, ±20-40%,..., ±180-200%. Initial rates were not allowed to go below 10-6. Each perturbation condition was simulated 100 times by randomly selecting the initial parameter values. Although the FL algorithm is not as exact as the ML-EM at estimating the true parameter values for small perturbations of the initial rate estimates relative to the true rates, the FL algorithm is able to reliably estimate the parameters for initial perturbations of up to 200%, whereas the ML-EM algorithm estimates begin to deviate after perturbations of about 40-60%. Moreover, the simplicity of the FL algorithm means that it runs on the order of 25 times faster than the ML-EM implementation considered. These results hold true for both the first and second neurons in the two BSN neuron hierarchy. For the first neuron in the hierarchy, the RMS difference in the time series of the probability calculated for the estimated and the true parameter values, follows a similar pattern to the parameter estimates when the FL and ML-EM algorithms are compared, with average RMS errors of 0.2% obtained for the FL algorithm across the range of perturbations studied. Although we do not have a formal proof of convergence of the FL algorithm, we conclude that the FL algorithm can stably estimate the parameters over a large range of initial perturbations and it can do this very quickly. Thus the FL algorithm makes online learning in networks of BSNs much more tractable.