Biology subjects

Rueckauer, B.

Publications and source records attributed to Rueckauer, B..

3 recordsLinked to original sources

bioRxiv2022

Optimal Control of Neural Systems

Brain-machine interfaces have reached an unprecedented capacity to measure and drive activity in the brain, allowing restoration of impaired sensory, cognitive or motor function. Classical control theory is pushed to its limit when aiming to design control laws that are suitable for large-scale, complex neural systems. This work proposes a scalable, data-driven, unified approach to study brain-machine-environment interaction using established tools from dynamical systems, optimal control theory, and deep learning. To unify the methodology, we define the environment, neural system, and prosthesis in terms of differential equations with learnable parameters, which effectively reduce to recurrent neural networks in the discrete-time case. Drawing on tools from optimal control, we describe three ways to train the system: Direct optimization of an objective function, oracle-based learning, and reinforcement learning. These approaches are adapted to different assumptions about knowledge of system equations, linearity, differentiability, and observability. We apply the proposed framework to train an in-silico neural system to perform tasks in a linear and a nonlinear environment, namely particle stabilization and pole balancing. After training, this model is perturbed to simulate impairment of sensor and motor function. We show how a prosthetic controller can be trained to restore the behavior of the neural system under increasing levels of perturbation. We expect that the proposed framework will enable rapid and flexible synthesis of control algorithms for neural prostheses that reduce the need for in-vivo testing. We further highlight implications for sparse placement of prosthetic sensor and actuator components.

bioengineering↗

bioRxiv2022

Biologically plausible phosphene simulation forthe differentiable optimization of visual corticalprostheses

Blindness affects millions of people around the world, and is expected to become increasingly prevalent in the years to come. For some blind individuals, a promising solution to restore a form of vision are cortical visual prostheses, which convert camera input to electrical stimulation of the cortex to bypass part of the impaired visual system. Due to the constrained number of electrodes that can be implanted, the artificially induced visual percept (a pattern of localized light flashes, or phosphenes) is of limited resolution, and a great portion of the fields research attention is devoted to optimizing the efficacy, efficiency, and practical usefulness of the encoding of visual information. A commonly exploited method is the non-invasive functional evaluation in sighted subjects or with computational models by making use of simulated prosthetic vision (SPV) pipelines. Although the SPV literature has provided us with some fundamental insights, an important drawback that researchers and clinicians may encounter is the lack of realism in the simulation of cortical prosthetic vision, which limits the validity for real-life applications. Moreover, none of the existing simulators address the specific practical requirements for the electrical stimulation parameters. In this study, we developed a PyTorch-based, fast and fully differentiable phosphene simulator. Our simulator transforms specific electrode stimulation patterns into biologically plausible representations of the artificial visual percepts that the prosthesis wearer is expected to see. The simulator integrates a wide range of both classical and recent clinical results with neurophysiological evidence in humans and non-human primates. The implemented pipeline includes a model of the retinotopic organisation and cortical magnification of the visual cortex. Moreover, the quantitative effect of stimulation strength, duration, and frequency on phosphene size and brightness as well as the temporal characteristics of phosphenes are incorporated in the simulator. Our results demonstrate the suitability of the simulator for both computational applications such as end-to-end deep learning-based prosthetic vision optimization as well as behavioural experiments. The modular approach of our work makes it ideal for further integrating new insights in artificial vision as well as for hypothesis testing. In summary, we present an open-source, fully differentiable, biologically plausible phosphene simulator as a tool for computational, clinical and behavioural neuroscientists working on visual neuroprosthetics.

neuroscience↗

bioRxiv2022

Optimization of Neuroprosthetic Vision via End-to-end Deep Reinforcement Learning

Visual neuroprostheses are a promising approach to restore basic sight in visually impaired people. A major challenge is to condense the sensory information contained in a complex environment into meaningful stimulation patterns at low spatial and temporal resolution. Previous approaches considered task-agnostic feature extractors such as edge detectors or semantic segmentation, which are likely suboptimal for specific tasks in complex dynamic environments. As an alternative approach, we propose to optimize stimulation patterns by end-to-end training of a feature extractor using deep reinforcement learning agents in virtual environments. We present a task-oriented evaluation framework to compare different stimulus generation mechanisms, such as static edge-based and adaptive end-to-end approaches like the one introduced here. Our experiments in Atari games show that stimulation patterns obtained via task-dependent end-to-end optimized reinforcement learning result in equivalent or improved performance compared to fixed feature extractors on high difficulty levels. These findings signify the relevance of adaptive reinforcement learning for neuroprosthetic vision in complex environments.

bioengineering↗