2 research outputs found
BCI decoder performance comparison of an LSTM recurrent neural network and a Kalman filter in retrospective simulation
Intracortical brain computer interfaces (iBCIs) using linear Kalman decoders
have enabled individuals with paralysis to control a computer cursor for
continuous point-and-click typing on a virtual keyboard, browsing the internet,
and using familiar tablet apps. However, further advances are needed to deliver
iBCI-enabled cursor control approaching able-bodied performance. Motivated by
recent evidence that nonlinear recurrent neural networks (RNNs) can provide
higher performance iBCI cursor control in nonhuman primates (NHPs), we
evaluated decoding of intended cursor velocity from human motor cortical
signals using a long-short term memory (LSTM) RNN trained across multiple days
of multi-electrode recordings. Running simulations with previously recorded
intracortical signals from three BrainGate iBCI trial participants, we
demonstrate an RNN that can substantially increase bits-per-second metric in a
high-speed cursor-based target selection task as well as a challenging
small-target high-accuracy task when compared to a Kalman decoder. These
results indicate that RNN decoding applied to human intracortical signals could
achieve substantial performance advances in continuous 2-D cursor control and
motivate a real-time RNN implementation for online evaluation by individuals
with tetraplegia.Comment: Accepted for the 9th International IEEE EMBS Conference on Neural
Engineerin
Control for Multifunctionality: Bioinspired Control Based on Feeding in Aplysia californica
Animals exhibit remarkable feats of behavioral flexibility and
multifunctional control that remain challenging for robotic systems. The neural
and morphological basis of multifunctionality in animals can provide a source
of bio-inspiration for robotic controllers. However, many existing approaches
to modeling biological neural networks rely on computationally expensive models
and tend to focus solely on the nervous system, often neglecting the
biomechanics of the periphery. As a consequence, while these models are
excellent tools for neuroscience, they fail to predict functional behavior in
real time, which is a critical capability for robotic control. To meet the need
for real-time multifunctional control, we have developed a hybrid Boolean model
framework capable of modeling neural bursting activity and simple biomechanics
at speeds faster than real time. Using this approach, we present a
multifunctional model of Aplysia californica feeding that qualitatively
reproduces three key feeding behaviors (biting, swallowing, and rejection),
demonstrates behavioral switching in response to external sensory cues, and
incorporates both known neural connectivity and a simple bioinspired mechanical
model of the feeding apparatus. We demonstrate that the model can be used for
formulating testable hypotheses and discuss the implications of this approach
for robotic control and neuroscience.Comment: Revisions have been made to improve manuscript clarity and expand the
introduction and discussion. The results are unchange