Biology subjects

Soo, L.

Publications and source records attributed to Soo, L..

3 recordsLinked to original sources

bioRxiv2025

Deep Learning-Based Control of Electrically Evoked Activity in Human Visual Cortex

Visual cortical prostheses offer a promising path to sight restoration, but current systems elicit crude, variable percepts and rely on manual electrode-by-electrode calibration that does not scale. This work introduces an automated data-driven neural control method for a visual neuroprosthesis using a deep learning framework to generate optimal multi-electrode stimulation patterns that evoke targeted neural responses. Using a 96-channel Utah electrode array implanted in the occipital cortex of a blind participant, we trained a deep neural network to predict single-trial evoked responses. The network was used in two complementary control strategies: a learned inverse network for real-time stimulation synthesis and a gradient-based optimizer for precise targeting of desired neural responses. Both approaches significantly outperformed conventional methods in controlling neural activity, required lower stimulation currents, and adapted stimulation parameters to resting state data, reliably evoking more stable percepts. Crucially, recorded neural responses better predicted perceptual outcomes than stimulation parameters alone, underscoring the value of our neural population control framework. This work demonstrates the feasibility of data-driven neural control in a human implant and offers a foundation for next-generation, model-driven neuroprosthetic systems, capable of enhancing sensory restoration across a range of clinical applications.

neuroscience↗

bioRxiv2025

Bayesian optimization of cortical neuroprosthetic vision using perceptual feedback

The challenge in cortical neuroprosthetic vision is determining the optimal, safe stimulation patterns for the visual cortex in order to evoke the desired perception in blind individuals--specifically, light perceptions known as phosphenes. Currently, clinical studies gain insights into the perceptual characteristics of the perceived phosphenes by asking for descriptions of provided stimulation protocols. However, the huge parameter space for multi-electrode stimulation settings makes it difficult to draw conclusions about the optimality of the stimulation patterns that lead to well-perceived phosphenes. A systematic search in the parameter space of the electrical stimulation is needed to achieve good perception. Bayesian optimization (BO) is a framework for finding optimal parameters efficiently. Using the patients scoring of the perception as feedback, a model of the patients response based on iteratively generated stimulation protocols can be built to maximize perception quality. A patient implanted with an intracortical 96-channel microelectrode array in their visual cortex was tested by iteratively presenting stimulation protocols, generated via BO for the first and random generation (RG) for the second experiment. Whereas standard BO methods do not scale well to problems with over a dozen inputs, we propose to optimize a set of 40 electrode currents using trust region-based BO. The generated protocols determine which electrodes are concurrently stimulated from the set and with how much current from a range of 0-50 {micro}A, on a maximum total current constraint of 500 {micro}A. The patient provided feedback for each stimulation based on their liking of the perception quality on a Likert scale, where a score of 7 indicated the highest quality and 0 no perception. In the BO experiment, the patient perception quality ratings gradually converged on higher values compared to the RG experiment. Similarly, gradually higher total current values were chosen by BO, in line with the observed preference of patients for higher currents due to brighter phosphenes. Finally, the electrodes that were observed to be more effective in producing phosphene perception in previous studies were gradually chosen more by BO also with the allocation of higher current values. This study demonstrates the power of BO in converging to optimal stimulation protocols based on patient feedback, providing a more efficient search for stimulation parameters for clinical studies.

bioengineering↗

bioRxiv2024

Suppressive interactions between nearby stimuli in visual cortex reflect crowding

Crowding is a phenomenon in which visual object identification is impaired by the close proximity of other stimuli. The neural processes leading to object recognition and its breakdown as seen in crowding are still debated. To assess how crowding affects the processing of stimuli in the visual cortex, we recorded steady-state visual evoked potentials (SSVEPs) elicited by flickering target and flanker stimuli while manipulating the spacing of these stimuli (Experiment 1) as well as target similarity (Experiment 2). Participants performed an orientation discrimination task while accuracy and speed of behavioural responses, along with frequency-tagged SSVEPs elicited by target and flanker stimuli, were recorded. Decreasing target-flanker distance reduced both behavioural performance and target-elicited SSVEP amplitudes. Estimates of the critical spacing, a measure of the spatial extent of crowding, from both behavioural data and SSVEP amplitudes were similar. Additionally, manipulating target similarity affected both measures in the same way. These findings establish a clear connection between the suppression of stimulus processing by nearby flankers in the visual cortex and crowding, and demonstrate the usefulness of SSVEPs in studying the cortical mechanisms of visual crowding.

neuroscience↗