Neural decoding

Research neural interfaces, implantable neurotechnology, neural decoding and closed-loop brain-computer interfaces. Topics include electrode and sensor design, biocompatible materials, high-channel-count recording and stimulation, neurophotonic probes combining optical stimulation with electrophysiology, and adaptive or deep-learning decoders. The appointment includes independent research, teaching and mentoring. Priority review is 15 November 2026.

…However, neural underpinnings of this integration in cortex remain elusive. Here, we used a neural decoding-based appr

…BCIs use ``decoders'‘ to relate neural activity to some desired action. Early approaches used highly specific and targeted statistical models, which required careful adaptation to different sessions or days for the same user, and lacked the ability to generalize across users or tasks. Recently, the availability of a large amount of n

Current approaches to cortical prosthetic vision rely on electrically evoked phosphenes, whose dynamic appearance in the visual field could enable functional sight in acquired blindness. Akin to an input/output dynamic-range problem, the spatiotemporal resolution of phosphene-based vision is constrained by the accuracy with which activity can be modulated in V1 and simultaneously, by the discriminability of evoked activity in higher visual areas (i.e. V2 and V4), for different stimulation parameters. However, the high-dimensionality of the stimulation parameter space makes impractical to exten

Neural decoding is essential for understanding the association between neural activity and behavior. A prerequisite for most decoding methods is spike sorting, the assignment of action potentials (or spikes) to individual neurons. Current spike sorting algorithms, however, can be inaccurate and do not properly model uncertainty of spike assignments, therefore discarding information that could potentially improve decoding performance. Recent advances in high-channel-count probes like Neuropixels (NP) and extracellular analysis pipelines allow for extracting a rich set of spike features to direc

We study the problem of time series imputation in multivariate neural recordings. Compared to standard time series imputation settings, new challenges for imputing neural recordings include the lack of adjacent timestamps for electrodes missing over days, and generalization across days and participants with different electrode configurations. Due to these challenges, the standard practice in neuroscience is to discard electrodes with missing data, even if only a part of the recording is corrupted, significantly reducing already difficult to obtain data. We propose Deep Neural Imputation (DNI),

Distributed neural population activity in the macaque inferior temporal (IT) cortex, which lies at the apex of the visual ventral stream hierarchy, is critical in supporting an array of object recognition behavior. Previous research, however, has been agnostic to the relevance of specific cell types, inhibitory vs. excitatory, in the formation of “behaviorally sufficient” IT population codes that can accurately predict primate object confusion patterns. Therefore, here, we first compared the strength of behavioral predictions of neural decoding (“readout”) models constructed from specific (put

Our daily endeavors occur in a complex visual environment, whose intrinsic variability shapes the way we integrate information to make decisions. By processing thousands of parallel sensory inputs, our brain is theoretically able to compute the uncertainty of its environment, which would allow it to perform Bayesian integration of its internal representations and its new sensory inputs to drive optimal inference. While there is convincing evidence that humans do compute this sensory uncertainty to guide their behavior, the actual neurobiological and computational principles on which uncertaint

Neural decoding uses brain activity to infer internal representations of the environmental states or predict behavior. Our study aimed to decode neural signal from the Posterior Parietal Cortex (V6A), to investigate intention coding and control of assistive devices. Data were recorded during delayed reaching in three different gaze/target conditions allowing to dissociate the retinotopic and the oculomotor reference frames. We used Deep Neural Networks to learn to decode the lateral displacement and the depth of the reached target from neural data averaged over four epochs, including pre-fixat

…in my laboratory for investigating the neural mechanisms of visual processing using naturalistic free viewing and simple target foraging pa