How auditory neurons count temporal intervals and decode information.

The numerical sense of animals includes identifying the numerosity of a sequence of events that occur with specific intervals, e.g., notes in a call or bar of music. Across nervous systems, the temporal patterning of spikes can code these events, but how this information is decoded (counted) remains elusive. In the anuran auditory system, temporal information of this type is decoded in the midbrain, where "interval-counting" neurons spike only after at least a threshold number of sound pulses have occurred with specific timing. We show that this decoding process, i.e., interval counting, arises from integrating phasic, onset-type and offset inhibition with excitation that augments across successive intervals, possibly due to a progressive decrease in "shunting" effects of inhibition. Because these physiological properties are ubiquitous within and across central nervous systems, interval counting may be a general mechanism for decoding diverse information coded/encoded in temporal patterns of spikes, including "bursts," and estimating elapsed time.

Deep Learning for Neuromuscular Control of Vocal Source for Voice Production.

A computational neuromuscular control system that generates lung pressure and three intrinsic laryngeal muscle activations (cricothyroid, thyroarytenoid, and lateral cricoarytenoid) to control the vocal source was developed. In the current study, LeTalker, a biophysical computational model of the vocal system was used as the physical plant. In the LeTalker, a three-mass vocal fold model was used to simulate self-sustained vocal fold oscillation. A constant/ǝ/vowel was used for the vocal tract shape. The trachea was modeled after MRI measurements. The neuromuscular control system generates control parameters to achieve four acoustic targets (fundamental frequency, sound pressure level, normalized spectral centroid, and signal-to-noise ratio) and four somatosensory targets (vocal fold length, and longitudinal fiber stress in the three vocal fold layers). The deep-learning-based control system comprises one acoustic feedforward controller and two feedback (acoustic and somatosensory) controllers. Fifty thousand steady speech signals were generated using the LeTalker for training the control system. The results demonstrated that the control system was able to generate the lung pressure and the three muscle activations such that the four acoustic and four somatosensory targets were reached with high accuracy. After training, the motor command corrections from the feedback controllers were minimal compared to the feedforward controller except for thyroarytenoid muscle activation.