Decoding sound level in the marmoset primary auditory cortex.

Neurons that respond favorably to a particular sound level have been observed throughout the central auditory system, becoming steadily more common at higher processing areas. One theory about the role of these level-tuned or nonmonotonic neurons is the level-invariant encoding of sounds. To investigate this theory, we simulated various subpopulations of neurons by drawing from real primary auditory cortex (A1) neuron responses and surveyed their performance in forming different sound level representations. Pure nonmonotonic subpopulations did not provide the best level-invariant decoding; instead, mixtures of monotonic and nonmonotonic neurons provided the most accurate decoding. For level-fidelity decoding, the inclusion of nonmonotonic neurons slightly improved or did not change decoding accuracy until they constituted a high proportion. These results indicate that nonmonotonic neurons fill an encoding role complementary to, rather than alternate to, monotonic neurons.NEW & NOTEWORTHY Neurons with nonmonotonic rate-level functions are unique to the central auditory system. These level-tuned neurons have been proposed to account for invariant sound perception across sound levels. Through systematic simulations based on real neuron responses, this study shows that neuron populations perform sound encoding optimally when containing both monotonic and nonmonotonic neurons. The results indicate that instead of working independently, nonmonotonic neurons complement the function of monotonic neurons in different sound-encoding contexts.

Phosphorylation of tropomyosin extends cooperative binding of myosin beyond a single regulatory unit.

Tropomyosin (Tm) is one of the major phosphoproteins comprising the thin filament of muscle. However, the specific role of Tm phosphorylation in modulating the mechanics of actomyosin interaction has not been determined. Here we show that Tm phosphorylation is necessary for long-range cooperative activation of myosin binding. We used a novel optical trapping assay to measure the isometric stall force of an ensemble of myosin molecules moving actin filaments reconstituted with either natively phosphorylated or dephosphorylated Tm. The data show that the thin filament is cooperatively activated by myosin across regulatory units when Tm is phosphorylated. When Tm is dephosphorylated, this "long-range" cooperative activation is lost and the filament behaves identically to bare actin filaments. However, these effects are not due to dissociation of dephosphorylated Tm from the reconstituted thin filament. The data suggest that end-to-end interactions of adjacent Tm molecules are strengthened when Tm is phosphorylated, and that phosphorylation is thus essential for long range cooperative activation along the thin filament.

The advantage of flexible neuronal tunings in neural network models for motor learning.

Human motor adaptation to novel environments is often modeled by a basis function network that transforms desired movement properties into estimated forces. This network employs a layer of nodes that have fixed broad tunings that generalize across the input domain. Learning is achieved by updating the weights of these nodes in response to training experience. This conventional model is unable to account for rapid flexibility observed in human spatial generalization during motor adaptation. However, added plasticity in the widths of the basis function tunings can achieve this flexibility, and several neurophysiological experiments have revealed flexibility in tunings of sensorimotor neurons. We found a model, Locally Weighted Projection Regression (LWPR), which uniquely possesses the structure of a basis function network in which both the weights and tuning widths of the nodes are updated incrementally during adaptation. We presented this LWPR model with training functions of different spatial complexities and monitored incremental updates to receptive field widths. An inverse pattern of dependence of receptive field adaptation on experienced error became evident, underlying both a relationship between generalization and complexity, and a unique behavior in which generalization always narrows after a sudden switch in environmental complexity. These results implicate a model that is flexible in both basis function widths and weights, like LWPR, as a viable alternative model for human motor adaptation that can account for previously observed plasticity in spatial generalization. This theory can be tested by using the behaviors observed in our experiments as novel hypotheses in human studies.