Technological challenges in the development of optogenetic closed-loop therapy approaches in epilepsy and related network disorders of the brain

Epilepsy is a chronic, neurological disorder affecting millions of people every year. The current available pharmacological and surgical treatments are lacking in overall efficacy and cause side-effects like cognitive impairment, depression, tremor, abnormal liver and kidney function. In recent years, the application of optogenetic implants have shown promise to target aberrant neuronal circuits in epilepsy with the advantage of both high spatial and temporal resolution and high cell-specificity, a feature that could tackle both the efficacy and side-effect problems in epilepsy treatment. Optrodes consist of electrodes to record local field potentials and an optical component to modulate neurons via activation of opsin expressed by these neurons. The goal of optogenetics in epilepsy is to interrupt seizure activity in its earliest state, providing a so-called closed-loop therapeutic intervention. The chronic implantation in vivo poses specific demands for the engineering of therapeutic optrodes. Enzymatic degradation and glial encapsulation of implants may compromise long-term recording and sufficient illumination of the opsin-expressing neural tissue. Engineering efforts for optimal optrode design have to be directed towards limitation of the foreign body reaction by reducing the implant’s elastic modulus and overall size, while still providing stable long-term recording and large-area illumination, and guaranteeing successful intracerebral implantation. This paper presents an overview of the challenges and recent advances in the field of electrode design, neural-tissue illumination, and neural-probe implantation, with the goal of identifying a suitable candidate to be incorporated in a therapeutic approach for long-term treatment of epilepsy patients

Gradient Descent in Materio

Deep learning, a multi-layered neural network approach inspired by the brain, has revolutionized machine learning. One of its key enablers has been backpropagation, an algorithm that computes the gradient of a loss function with respect to the weights in the neural network model, in combination with its use in gradient descent. However, the implementation of deep learning in digital computers is intrinsically wasteful, with energy consumption becoming prohibitively high for many applications. This has stimulated the development of specialized hardware, ranging from neuromorphic CMOS integrated circuits and integrated photonic tensor cores to unconventional, material-based computing systems. The learning process in these material systems, taking place, e.g., by artificial evolution or surrogate neural network modelling, is still a complicated and time-consuming process. Here, we demonstrate an efficient and accurate homodyne gradient extraction method for performing gradient descent on the loss function directly in the material system. We demonstrate the method in our recently developed dopant network processing units, where we readily realize all Boolean gates. This shows that gradient descent can in principle be fully implemented in materio using simple electronics, opening up the way to autonomously learning material systems

brains-py, A framework to support research on energy-efficient unconventional hardware for machine learning

Projections about the limitations of digital computers for deep learning models are leading to a shift towards domain-specific hardware, where novel analogue components are sought after, due to their potential advantages in power consumption. This paper introduces brains-py, a generic framework to facilitate research on different sorts of disordered nano-material networks for natural and energy-efficient analogue computing. Mainly, it has been applied to the concept of dopant network processing units (DNPUs), a novel and promising CMOS-compatible nano-scale tunable system based on doped silicon with potentially very low-power consumption at the inference stage. The framework focuses on two material-learning-based approaches, for training DNPUs to compute supervised learning tasks: evolution-in-matter and surrogate models.While evolution-in-matter focuses on providing a quick exploration of newly manufactured single DNPUs, the surrogate model approach is used for the design and simulation of the interconnection between multiple DNPUs, enabling the exploration of their scalability. All simulation results can be seamlessly validated on hardware, saving time and costs associated with their reproduction. The framework is generic and can be reused for research on various materials with different design aspects, providing support for the most common tasks requiredfor doing experiments with these novel materials.<br/

Etiologies and hearing status in bilateral vestibulopathy: a retrospective study of 315 patients

ImportanceThe development of a vestibular implant has reached milestones and seems to be a promising therapeutic tool for bilateral vestibulopathy (BV). Given the former lack of therapeutic options for BV, the disease has received scant attention in the previous research literature. It is therefore of major importance to gain more insight into the underlying pathology of BV. Furthermore, as some research groups specifically use a combined vestibulo-cochlear implant, the size of the group of BV patients with associated hearing loss is of special interest.ObjectivesThe study aimed to determine the definite and probable etiology in bilateral vestibulopathy (BV) patients and to report on their hearing status.DesignThis study involves multicenter retrospective study design.SettingThe research setting is at tertiary referral centers.ParticipantsConsecutive BV patients diagnosed at the Antwerp University Hospital between 2004 and 2018 at the Maastricht University Medical Center between 2002 and 2015 and at the Geneva University Hospital between 2013 and 2018, who met the BV diagnostic criteria of the Bárány Society.Main outcome measuresPrimary interests were the etiology and hearing status of BV patients. Moreover, the data of vestibular tests were examined (caloric irrigation, rotatory chair tests, and video-head impulse test).ResultsThe authors identified 315 BV patients, of whom 56% were male patients. Mean age at diagnosis was 58.6 ± 15.1 (range 7–91) years. The definite cause was determined in 37% of the patients and the probable cause in 26% of the patients. No cause was identified in 37% of BV patients. The largest subgroup included patients with genetic etiology (31%), most frequently COCH mutation. Only 21% of patients (n = 61) had bilateral normal hearing. Almost half of the patients (45%, n = 134) had profound hearing loss in at least one ear.ConclusionBV is a heterogeneous condition, with over a third of cases remaining idiopathic, and nearly three-quarters affected by hearing loss. COCH mutation is the most common non-idiopathic cause of BV in our population. Only 21% of our BV patients presented with bilateral normal hearing