State-space decoding of primary afferent neuron firing rates

Abstract

Kinematic state feedback is important for neuroprostheses to generate stable and adaptive movements of an extremity. State information, represented in the firing rates of populations of primary afferent neurons, can be recorded at the level of the dorsal root ganglia (DRG). Previous work in cats showed the feasibility of using DRG recordings to predict the kinematic state of the hind limb using reverse regression. Although accurate decoding results were attained, reverse regression does not make efficient use of the information embedded in the firing rates of the neural population. In this paper, we present decoding results based on state-space modeling, and show that it is a more principled and more efficient method for decoding the firing rates in an ensemble of primary afferent neurons. In particular, we show that we can extract confounded information from neurons that respond to multiple kinematic parameters, and that including velocity components in the firing rate models significantly increases the accuracy of the decoded trajectory. We show that, on average, state-space decoding is twice as efficient as reverse regression for decoding joint and endpoint kinematics.

Delineating the whole brain BOLD response to passive movement kinematics

“…The bulk of these studies record firing rates from the afferent fibers [11], [12], [13], [14]. Yet since there are different afferent substrates for sensation of kinematics (i.e.…”

Estimating bladder pressure from sacral dorsal root ganglia recordings

“…Nonlinear characteristics of bladder units (Fig. 5) suggest that the inclusion of bladder pressure velocities (contraction, relaxation) and the use of splines and other advanced regression techniques [13] may improve model accuracy. This approach can also be integrated with a contraction detection algorithm and closed loop stimulation for bladder control while optimizing the firing rate kernel.…”

Online feedback control of functional electrical stimulation using dorsal root ganglia recordings

“…5). Some decoding algorithms, such as state-space regression models, have demonstrated significant improvements over reverse regression models but are not currently tractable in real-time [10]. Other decoding methods, such as Bayesian classifiers or fuzzy neural networks may achieve similar improvements while remaining computationally tractable [11].…”

“…Access to this sensory information at a single location is a significant benefit over recording from multiple peripheral nerve locations or with multiple external sensors. Recent studies have demonstrated that limb position and velocity can be decoded from signals recorded from lumbar DRG in cats [9], [10]. …”