Bladder dysfunction resulting from neurological disease or injury, such as spinal cord injury (SCI), produces symptoms of urinary incontinence, chronic retention of urine, or detrusor sphincter dyssynergia, which greatly reduce quality of life. Electrical stimulation of pudendal afferents is a promising alternative to restore continence and voiding through activation of spinal circuits and reflex inhibition or excitation of the bladder. We sought to develop a computational model of the spinal neural network that governs reflex control of bladder activity by pudendal afferent stimulation. We developed and implemented a spiking neural network model of linear integrate and fire (LIAF) neurons based on previous neuroanatomical and electrophysiological studies. The network model incorporated sensory inputs from both pelvic and pudendal nerve afferent fibers, excitatory and inhibitory interneurons, which receive primary inputs from pelvic and pudendal afferents and regulate the sacral parasympathetic output neurons that innervated the bladder via the pelvic nerve. The parameters describing each neuron and synapse were selected to generate realistic firing activity and reproduce behavior from neural recordings. Feedback was implemented to study the effects of pudendal afferent stimulation and compare the model results to previous studies. Bladder pressure was computed from the SPN firing rate and bladder volume, and pelvic afferent firing rate was continually updated to reflect bladder pressure. The neural network model reproduced the frequency tuning of pudendal afferent stimulation seen experimentally. Lowfrequencies (2-10 Hz) failed to evoke bladder contractions and high frequencies (33-50 Hz) evoked bladder contractions. This model of the spinal neural network that coordinates bladder activity provides insight into the mechanisms of bladder control by pudendal afferent stimulation: inhibitory and excitatory synaptic network interactions of local interneurons and afferent fibers mediate SPN output. The lack of representation of higher-order processing in the brain or brainstem suggests that neural network interactions at the sacral level likely mediate the bladder response to different frequencies or temporal patterns of pudendal afferent stimulation. Further investigation of the neural network controlling bladder function may lead to the development of novel stimulation paradigms for improved bladder control.
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