Innate frequency-discrimination hyperacuity in Williams-Beuren syndrome mice.

Williams-Beuren syndrome (WBS) is a rare disorder caused by hemizygous microdeletion of ∼27 contiguous genes. Despite neurodevelopmental and cognitive deficits, individuals with WBS have spared or enhanced musical and auditory abilities, potentially offering an insight into the genetic basis of auditory perception. Here, we report that the mouse models of WBS have innately enhanced frequency-discrimination acuity and improved frequency coding in the auditory cortex (ACx). Chemogenetic rescue showed frequency-discrimination hyperacuity is caused by hyperexcitable interneurons in the ACx. Haploinsufficiency of one WBS gene, Gtf2ird1, replicated WBS phenotypes by downregulating the neuropeptide receptor VIPR1. VIPR1 is reduced in the ACx of individuals with WBS and in the cerebral organoids derived from human induced pluripotent stem cells with the WBS microdeletion. Vipr1 deletion or overexpression in ACx interneurons mimicked or reversed, respectively, the cellular and behavioral phenotypes of WBS mice. Thus, the Gtf2ird1-Vipr1 mechanism in ACx interneurons may underlie the superior auditory acuity in WBS.

Analysis of Neuronal Spike Trains, Deconstructed.

As information flows through the brain, neuronal firing progresses from encoding the world as sensed by the animal to driving the motor output of subsequent behavior. One of the more tractable goals of quantitative neuroscience is to develop predictive models that relate the sensory or motor streams with neuronal firing. Here we review and contrast analytical tools used to accomplish this task. We focus on classes of models in which the external variable is compared with one or more feature vectors to extract a low-dimensional representation, the history of spiking and other variables are potentially incorporated, and these factors are nonlinearly transformed to predict the occurrences of spikes. We illustrate these techniques in application to datasets of different degrees of complexity. In particular, we address the fitting of models in the presence of strong correlations in the external variable, as occurs in natural sensory stimuli and in movement. Spectral correlation between predicted and measured spike trains is introduced to contrast the relative success of different methods.

Dimensionality reduction in neuroscience.

The nervous system extracts information from its environment and distributes and processes that information to inform and drive behaviour. In this task, the nervous system faces a type of data analysis problem, for, while a visual scene may be overflowing with information, reaching for the television remote before us requires extraction of only a relatively small fraction of that information. We could care about an almost infinite number of visual stimulus patterns, but we don't: we distinguish two actors' faces with ease but two different images of television static with significant difficulty. Equally, we could respond with an almost infinite number of movements, but we don't: the motions executed to pick up the remote are highly stereotyped and related to many other grasping motions. If we were to look at what was going on inside the brain during this task, we would find populations of neurons whose electrical activity was highly structured and correlated with the images on the screen and the action of localizing and picking up the remote.