Biology subjects

Bashford, L.

Publications and source records attributed to Bashford, L..

5 recordsLinked to original sources

bioRxiv2024

Visual context affects the perceived timing of tactile sensations elicited through intra-cortical microstimulation

Intra-cortical microstimulation (ICMS) is a technique to provide tactile sensations for a somatosensory brain-machine interface (BMI). A viable BMI must function within the rich, multisensory environment of the real world, but how ICMS is integrated with other sensory modalities is poorly understood. To investigate how ICMS percepts are integrated with visual information, ICMS and visual stimuli were delivered at varying times relative to one another. Both visual context and ICMS current amplitude were found to bias the qualitative experience of ICMS. In two tetraplegic participants, ICMS and visual stimuli were more likely to be experienced as occurring simultaneously when visual stimuli were more realistic, demonstrating an effect of visual context on the temporal binding window. The peak of the temporal binding window varied but was consistently offset from zero, suggesting that multisensory integration with ICMS can suffer from temporal misalignment. Recordings from primary somatosensory cortex (S1) during catch trials where visual stimuli were delivered without ICMS demonstrated that S1 represents visual information related to ICMS across visual contexts.

neuroscience↗

bioRxiv2023

Neural subspaces of imagined movements in parietal cortex remain stable over several years in humans.

A crucial goal in brain-machine interfacing is long-term stability of neural decoding performance, ideally without regular retraining. Here we demonstrate stable neural decoding over several years in two human participants, achieved by latent subspace alignment of multi-unit intracortical recordings in posterior parietal cortex. These results can be practically applied to significantly expand the longevity and generalizability of future movement decoding devices.

neuroscience↗

bioRxiv2022

S1 represents multisensory contexts and somatotopic locations within and outside the bounds of the cortical homunculus

The responsiveness of primary somatosensory cortex (S1) to physical tactile stimuli is well documented but the extent to which it is modulated by vision is unresolved. Additionally, recent literature has suggested that tactile events are represented in S1 in a more complex, generalized manner than its long-established topographic organization. To better characterize S1 function, neural activity was recorded from a tetraplegic patient implanted with microelectrode arrays in S1 during 1s stroking touches to the forearm (evoking numb sensation) or finger (naturalistic sensation). Touch conditions included visually observed first person physical touches, physical touches without vision, and visual touches without physical contact which occurred either to a third person, an inanimate object, or the patients own body in virtual reality. Two major findings emerged from this dataset. The first was that vision strongly modulates S1 activity, but only if there is a physical element to the touch, suggesting that passive observation of touches is not sufficient to recruit S1 neurons. The second was that despite the location of the recording arrays in a putative arm area of S1, neural activity was able to represent both arm and finger touches in physical touch conditions. Arm touches were encoded more strongly and specifically, supporting the idea that S1 encodes tactile events primarily through its topographic organization, as well as in a more general manner encompassing larger areas of the body.

neuroscience↗

bioRxiv2019

Deep Multi-State Dynamic Recurrent Neural Networks Operating on Wavelet Based Neural Features for Robust Brain Machine Interfaces

We present a new deep multi-state Dynamic Recurrent Neural Network (DRNN) architecture for Brain Machine Interface (BMI) applications. Our DRNN is used to predict Cartesian representation of a computer cursor movement kinematics from open-loop neural data recorded from the posterior parietal cortex (PPC) of a human subject in a BMI system. We design the algorithm to achieve a reasonable trade-off between performance and robustness, and we constrain memory usage in favor of future hardware implementation. We feed the predictions of the network back to the input to improve prediction performance and robustness. We apply a scheduled sampling approach to the model in order to solve a statistical distribution mismatch between the ground truth and predictions. Additionally, we configure a small DRNN to operate with a short history of input, reducing the required buffering of input data and number of memory accesses. This configuration lowers the expected power consumption in a neural network accelerator. Operating on wavelet-based neural features, we show that the average performance of DRNN surpasses other state-of-the-art methods in the literature on both single- and multi-day data recorded over 43 days. Results show that multi-state DRNN has the potential to model the nonlinear relationships between the neural data and kinematics for robust BMIs.

neuroscience↗

bioRxiv2018

Motor skill learning leads to the increase of planning horizon

We investigated motor skill learning using a path tracking task, where human subjects had to track various curved paths at a constant speed while maintaining the cursor within the path width. Subjects accuracy increased with practice, even when tracking novel untrained paths. Using a "searchlight" paradigm, where only a short segment of the path ahead of the cursor was shown, we found that subjects with a higher tracking skill differed from the novice subjects in two respects. First, they had lower motor variability, in agreement with previous findings. Second, they took a longer section of the future path into account when performing the task, i.e. had a longer planning horizon. We estimate that between one third and one half of the performance increase was due to the increase in planning horizon. An optimal control model with a fixed horizon (receding horizon control) that increases with tracking skill quantitatively captured the subjects movement behaviour. These findings demonstrate that human subjects not only increase their motor acuity but also their planning horizon when acquiring a motor skill. New and NoteworthyWe show that when learning a motor skill humans are using information about the environment from an increasingly longer amount of the movement path ahead to improve performance. Crucial features of the behavioural performance can be captured by modelling the behavioural data with a receding horizon optimal control model.

neuroscience↗