Sinusoidal vibrotactile stimulation differentially improves force steadiness depending on contraction intensity.

Studies have reported the benefits of sensory noise in motor performance, but it is not clear if this phenomenon is influenced by muscle contraction intensity. Additionally, most of the studies investigated the role of a stochastic noise on the improvement of motor control and there is no evidence that a sinusoidal vibrotactile stimulation could also enhance motor performance. Eleven participants performed a sensorimotor task while sinusoidal vibrations were applied to the finger skin. The effects of an optimal vibration (OV) on force steadiness were evaluated in different contraction intensities. We assessed the standard deviation (SD) and coefficient of variation (CoV) of force signals. OV significantly decreased force SD irrespective of contraction intensity, but the decrease in force CoV was significantly higher for low-intensity contraction. To the best of our knowledge, our findings are the first evidence that sinusoidal vibrotactile stimulation can enhance force steadiness in a motor task. Also, the significant improvement caused by OV during low-intensity contractions is probably due to the higher sensitivity of the motor system to the synaptic noise. These results add to the current knowledge on the effects of vibrotactile stimulation in motor control and have potential implications for the development of wearable haptic devices. Graphical abstract In this work the effects of a sinusoidal vibrotactile stimulation on force steadiness was investigated. Index finger sensorimotor tasks were performed in three levels of isometric contraction of the FDI muscle: 5, 10 and 15 %MVC. An optimal level of vibration significantly improved force steadiness, but the decrease in force CoV caused by vibration was more pronounced in contractions at 5 %MVC.

Dynamics of spontaneous activity in random networks with multiple neuron subtypes and synaptic noise : Spontaneous activity in networks with synaptic noise.

Spontaneous cortical population activity exhibits a multitude of oscillatory patterns, which often display synchrony during slow-wave sleep or under certain anesthetics and stay asynchronous during quiet wakefulness. The mechanisms behind these cortical states and transitions among them are not completely understood. Here we study spontaneous population activity patterns in random networks of spiking neurons of mixed types modeled by Izhikevich equations. Neurons are coupled by conductance-based synapses subject to synaptic noise. We localize the population activity patterns on the parameter diagram spanned by the relative inhibitory synaptic strength and the magnitude of synaptic noise. In absence of noise, networks display transient activity patterns, either oscillatory or at constant level. The effect of noise is to turn transient patterns into persistent ones: for weak noise, all activity patterns are asynchronous non-oscillatory independently of synaptic strengths; for stronger noise, patterns have oscillatory and synchrony characteristics that depend on the relative inhibitory synaptic strength. In the region of parameter space where inhibitory synaptic strength exceeds the excitatory synaptic strength and for moderate noise magnitudes networks feature intermittent switches between oscillatory and quiescent states with characteristics similar to those of synchronous and asynchronous cortical states, respectively. We explain these oscillatory and quiescent patterns by combining a phenomenological global description of the network state with local descriptions of individual neurons in their partial phase spaces. Our results point to a bridge from events at the molecular scale of synapses to the cellular scale of individual neurons to the collective scale of neuronal populations.